NVIDIA Corporation (NVDA)

Investor Day 2015

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Mar 17, 2015

Ladies and gentlemen, I'm very pleased to welcome you to NVIDIA's Annual Investor Day. You guys see a lot of green here, so happy St. Patrick's Day to My last slide. So, just overview of the agenda. We divide the day into 2 different parts. The first half starts with Jensen, our CEO, then we talk about a couple of our G2C conference. Second half of the day, we'll end with obviously our finance presentation and our Q and A. So, with no further ado, I'd like to invite our Co Founder, President and CEO, Janssen Huang. I was expecting way more jokes, way more jokes. You know he wanted to be a stand up comic, right? How did that work for you guys? That's right. Now he's in IR. I don't know what that means. See, I could have been a static comic. You guys, welcome to our Analyst Day. This is a day I always look forward to. A couple of reasons. First of all, of course, it's GTC. GTC, as you guys I hope you guys were there, but GTC is really about developers. It's really about all of these developers that come together once a year to share their work in high performance computing, where high performance computing is a tool that enables them to do their work. They share their breakthroughs. They're inspired by each other's ideas. They look at, of course, all the papers that you guys are going to see today. We focus on this particular time each year and then we present it to each other. We inspire each other to do even better work. And then together, we could dream, dream about the future. Some of the things that you guys will witness throughout this week are just people who are dreaming about the type of problems they can solve or the type of breakthroughs they could achieve or some new application they would like to bring to the world. I mean, it's really about dreaming the future. And for us, what really exciting for me is what's really amazingly exciting is the opportunity to work with them and to enable their dreams, to work with them to enable their dreams. The second part is Analyst Day. It's right next to each other for a very good reason. It's an enormous amount of work for all of you I know. And you're enjoying the GTC conference, you're laying up on new ideas. And then, of course, right in the middle of it is our Analyst Day. The thing that is really about putting the 2 together, even though there's a lot of work to do for everybody involved, is that you get to see us in our rawest form. This is what really, really inspires our company. And I said today, and I mean it, that this is the beginning of it all. If it wasn't because of all of this work here with developers, why would there be applications or end markets or end results that were somehow created, why would there be large markets for visual computing? Why would people need visual computing? And it all starts right here. And so this is an opportunity for you guys to look at it right at ground 0 and see some of the work that's about to change the world several years from now. Now you guys have heard me talk about NVIDIA for a long time. Long time for some a very long time. I've been CEO of NVIDIA for 24 years as you guys know. And I've had the opportunity to invent with all of the great people at the company the modern version of computer graphics. We are the largest computer graphics company in the history of mankind. Computer graphics, we are the largest computer graphics in the history of mankind. We define modern computer graphics. We invented modern computer graphics. We also reframed what computer graphics mean and meant some 10, 15 years ago. We expanded the scope of visual computing. We call it visual computing. It's not just about the generation of images from all the data that's inside your computer, but it's understanding the world and understanding the images that come into the computer. Visual computing is a very, very broad field today as you can see. We've reshaped our company in several ways. Whereas we the first NVIDIA that you guys met was really a PC graphics company. We built a chip that was connected to PCI Express. It was Windows compatible. It was VGA compatible. It supported the APIs that were standard in the industry. Make it as fast as possible. And make it as fast as possible. Largely for the 1st 5 years of our company maybe arguably even 10, our endeavor was solving those problems, making computer graphics faster and faster, which is very important because you need to experience it in real time. That in itself was an enormous contribution already. We were an industry standard component PCI Express compatible with Windows, compatible with DirectX, compatible with Open largely interchangeable. But because of our incredible execution, because of the people that are inside the company, we were able to stay a step ahead the entire way. We started out as 1 of 50 companies. We eventually ended up being the only company, the only standalone computer graphics company in the world, the largest the world's ever seen. In no time in the history of mankind did one company play a role in computer graphics in such large and broad markets. So I thought that the strategy for the 1st 10 years arguably perilous, arguably extremely dangerous, but we executed with quite a bit of flare and we became quite large. Well, being a component company, being a PC graphics company, fueled our growth in the 1st 10 years. But at some point with every component, it doesn't matter what component you are. It doesn't matter what component you are. It could be a video component, it could be an audio component, it could be a component. It doesn't really matter what component you are. In the end, if you're an interchangeable component, whether it's a DRAM component or flash component, it doesn't matter what component you are, whether Ethernet component or USB component or it doesn't matter what component you are. It could be a camera component, it could be a computer vision component, it could be an ISP component, it doesn't matter what component you are. In the final analysis, if you're a component company, eventually components get good enough. This is a problem that has been characterized in a lot of different ways. And well, I thought Clay did the ultimate job of describing it. Christensen over at Harvard did the ultimate job of Christensen over at Harvard did the ultimate job of describing it, Innovator's Dilemma. At some point, technology becomes good enough. It doesn't matter what component you create. And so we discovered that not only is that true, in fact, we've always believed that a it deeply, solve deep, deep problems and go build a company that is not a components company, but a visual computing company that engages 4 vertical markets or a few vertical markets. We chose 4 and these are the 4 that we selected for the various reasons that we'll describe. We selected 4 to deeply engage. And for those markets, we will be the domain experts. We're not a component supplier, we will be the domain experts. We will know more about those fields than any company on the planet. Technology level. We'll understand it at the software level. We'll understand it at the ecosystem level. We'll not only support standards, will drive standards. We are a partner to all of the ecosystem partners. We will evangelize that market. We will solve problems for that market before that market even realize it has a problem. For the markets that we serve, we will be utterly experts that we were not going to be just horizontal component suppliers that we were going to be domain experts in several vertical markets. Gaming is one of them. Gaming is one of them for several reasons. 1, we love gaming. There's I still remember in 1993 when we first started the company and nobody invested in gaming. There were no VCs that invested in gaming with the exception of 1 turned out to be my investor as well. They were the only investor that even understood what gaming was about. Electronic Arts had 16 employees. Said video games and they said okay, but tell me what your killer app is. I said, said video games and they said okay but tell me what your killer app is. I said video games. And they said okay startups don't depend on startups. And their point is that it's a young company. How could you there's a lot of different ways you can think about that. And it was utterly correct. But nonetheless, we helped build that industry. And now video games is 100,000,000,000 market. It is expanding still. Everybody is a gamer. Anybody who is born today, anybody who was born in the last 15 years a gamer today. Gaming is obviously a very large market. It's also one of the most technologically challenging markets. And the reason for that is because we're trying to create virtual reality. We're trying to create virtual reality. We're trying to convince you that when you're playing Madden Football that you're actually playing Madden Football, that you're actually that team, you're actually that player. And the animation and the graphics so real. You can't tell the difference. I still believe that in a very short time another 10 years' time you are going to watch a football game and you're going to be Well, we think that there's a market for that. We think Well, we think that there's a market for that. We think that the problem is extremely difficult to solve and that has been one of our vertical markets. We know more about this industry than nearly anybody from the technology to the ecosystem to the markets itself on a global scale. Cars, over 10 years ago, we came to the conclusion that the car is going to be a supercomputer on wheels and a supercomputer on wheels is going to be software defined. You can never write all the software necessary for every single car at the time that it was taken off the line. And if there was a way, if there was a company who could provide value to this particular vertical market that would be us. That the car will be the ultimate visual supercomputer. We were wrong for about 10 years. And then last year we became right. This is going to be a very large market. I believe there's going to be a lot of computers inside cars. Those computers there'll be feature detectors, there'll be computer vision detectors. There'll be all kinds of be sonars. There'll be radars. There'll be all kinds of all kinds of things like that. But there will be one thing for sure. Some cars will have sonar. Some cars will have LIDAR. Some cars will have computer vision. Some cameras. But there's one thing we know for sure that every car will have a very powerful computer insight and that software is going to define the features. Anybody who has a Tesla, anybody who enjoys a Tesla now cannot understand how to drive a Porsche Cayenne. I have no clue how to drive that car. What are all these knobs and dials? And when's my when am I going to get my next software update? That's my only question. Anybody who drives a Tesla today, the nanosecond that you see that OTA, if you didn't sense a little bit of joy, if you didn't sense a little bit of joy that you're about to discover some new feature that Elon worked on our behalf, if you didn't experience some amount of joy, I would be very shocked. The car is going to be a rolling supercomputer. It's going to be software defined. And companies who are great at software are going to be great at building these cars. It's going to be built it's going to be about building computers on wheels. Well, somebody who said, I think it was Alan Kay that said, anybody who's serious about software builds their own hardware. And it's true because most of the software that we're building on these platforms are really intricate things. I need to OTA this thing for the I'm going to support your car for as long as you shall live. And that's not an that's not a that's not a minor promise. The reason why NVIDIA's gamers are so in love with us is because I support their GPUs and their experience for as long as they shall live. And if some and I've been asked, Jensen, how long are you gonna support my drivers? And I look them in the United States, for as long as you shall live. And it's true. We've been supporting an installed base of GPUs now 200,000,000 large and we update new drivers for them and we bring new joy to them every single month. One of the reasons why NVIDIA is so the dedication we have to that architecture, we can't do that. We can't do that. We can't do the OTAs. We can't update that architecture. If we don't own that architecture, you can't update that architecture if you can't own that architecture. And if you're not sure, just ask yourself how many of your cell phones and how many of your tablets, Android phones and tablets are you still waiting for lollipop? If you're serious about software, you build your own hard work. NVIDIA does that. Enterprise. This market capturing, realizing people's ideas, realizing people's imaginations, transforming it into products. That is a classic visual computing problem. This is the oldest of visual computing markets, the workstation market. Our market show here, obviously very, very high. It's a market we care very deeply about. All of the sensibilities and the car that you designed 20 years ago now and if it were to be contested in some way and got to bring the database back, you can trust that if you had a Quadro that database will pop right back. And all of the pixels will be there. Our dedication to that market all reflected in the market share. How is it possible that a company has 90% market share on the free market? How is it possible 90% market share on a free market? From top to bottom just by every industry we serve enterprise graphics. One of the things that we did, we imagine all of us are taking our work with us. I do more work in a mobile state than just about any other state. I'm doing more work everywhere else except my desk. Now it turns out that most designers can't do that. And why can't they do that? Because you can't pack a work station with you. You can pack a basic PowerPoint PC with you, but you can't pack a work station with you. It's too much. Too much data, too much computing, too much graphics, too much everything. So we thought wouldn't it be great if we just virtualized and throw it into the cloud. And now we move the computer to the storage. A lot of wonderful things can happen. In addition to that, I'm sure Greg and Jeff will talk about that. But as a result, instead of moving storage data to the computer, we did the opposite. Instead of moving data to the computer, we did exactly the opposite. We did the miracle and put the computer next to the data. The opposite. We did the miracle and put the computer next to the data. The world's most interactive graphics computer, we now moved all the way across network to the computer. The benefits are fantastic. HPC and cloud. You guys saw that's what HP this is HP and C and Cloud is precisely what GTC is about. It's precisely what GTC is about. And you guys see the enthusiasm here. You see the questioned all along the way is unquestionably valuable today. Accelerated computing, GPU accelerated computing Question all along the way, maybe it's a fad, maybe it could be replaced by FPGAs, maybe multi core CPUs will come along. They'll just add more transistors. All true. But accelerated computing and the reason why it exists is more profound than that. It's more profound than that. The architectural nuance more deep than that. There is a very specific reason, a collection of specific reasons why accelerated computing is the right way to do it for a lot of applications. And we prove it year in and year out year in year out. If you wrote a CUDA application in the year 7 years ago that exact CUDA application if it turns out to be Stone who's who's doing some really amazing work at UIUC on molecular dynamics and trying to trying to figure out a cure for cancer. If you're that guy, well it turns out John has written a CUDA application 7 years ago and every single year we just doubled the speed. And we're about to increase it by a factor of 10, we pass go. Unbelievable. Port once, write once, sit back and do research. Sit back and do research. Sit back and change the world. That's really ultimately what they want to do, high performance computing. Now we still have an OEM business. I mean from all of this technology, all of these markets that we serve, we serve with the processor, an enormous amount of software, maybe a system associated with that, maybe a cloud service associated with that. But whatever is the best manifestation of that solution, whatever is the best manifestation of that service, we're going to help somebody solve a problem. Whatever is the best manifestation of that, we will create that and go to that market. Sometimes it's related to a graphics card. Sometimes it's related maybe it's in the formation of a system. Sometimes it's in the formation of a system that goes to retail, sometimes maybe it's a cloud service that's hosted at Amazon. Okay. We look at each one of these vertical markets on first principles. We then of course invent the technology underneath. And those GPUs are still valuable for PCs. And we fight hard in every opportunity we get and we seek out opportunities where GPUs, graphics, visual computing still matters, maybe not for office laptops where people want it to be as thin as possible, but still there are mobile workstations, maybe they're gaming PCs, maybe they're PCs that they would like to target at digital content creation, maybe they just want it to be the highest performance PC that they offer. Same thing with tablets, same thing with other devices. We still have OEMs and of course IP. We have the richest portfolio of visual computing intellectual property on the planet. We have made greater contribution in the last 24 years to modern computer graphics in any company and arguably all of the companies combined. This is our field. We're serious about IP. We're serious about patents. We invest a lot of money filing the patents. We invest a lot of money inventing the patents. We invest greatly as you guys all know. As a result, we have a war chest of intellectual property that keeps us out of harm's way on the one hand, but we can also of course leverage on the other hand. We are very serious about that. Those are our 5 markets. Now here comes to here's the scorecard. Now we typically because of where we started, the way we describe our business to you changed over time. And now we've reshaped the company. We're going to describe to you, we still report the numbers in various ways, but we're also going to describe to you our company's business from those markets, from the viewpoint of the markets. And as you can see, the efforts that we've made, the efforts we've made in the last several years to engage those markets have resulted in real growth and real not only just real growth, really significant growth. This is a growth company again. There is no question NVIDIA is a growth company. Gaming has grown 36%. It is now very, very, very clear to all of you that although we are a PC gaming company, we're not a PC company. We're a gaming company that uses PC as the platform to deliver that capability. And PC is like air, PC is like water, PC is like electricity, PC is essentially a utility for most people. So the fact that we provide GeForce on top of that utility doesn't make us that utility company. We're a gaming company. We're a gaming company in that business growing 36 percent. There's a reason why it's growing so fast. You see it all around you. PC gaming because it's largely open and because everybody has it. It is culturally friendly. Before you buy a game console for your child, you're going to buy them a PC. It is utility friendly. You need it for social anyways. You need it for work anyways. You need it for study anyways. You need it for all kinds of things that you do anyways. Adding a GeForce to it turns it into a mighty game console. It's open. It's also open for innovation. It is the 1st platform to have invented massive online multiplayer games. It was the 1st platform to have innovated MOBA, turning multiplayer into a battle arena, the invention of esports. It is also the platform where VR will be innovated. 1 innovation after another Lots of reasons why gaming is going to continue to grow. Production value is growing all the time. At some point, it reaches critical mass. The reason why well, not just it's a great movie, but the reason why they can invest 100 of 1,000,000 of dollars in Star Wars is because they know there are a lot of people who are going to come and watch it. And that DVD and Blu Ray and movie theaters are abundant. There's a lot of different ways for you enjoy Star Wars. They can invest $1,000,000,000 and still make it back. Well, it turns out you can now do the same about gaming. You can invest 100 of 1,000,000 of dollars on Call of Duty. You can invest 100 of 1,000,000 of dollars on Astasis Creed. You will have confidence that you can get it back. And the reason for that is because the PC platform has reached a critical mass so large and there's such a large audience, you can now increase the production value. The benefit that we get that most movie mediums don't get is that when you increase your production value, it increases the need for GPUs. Today, you guys saw Kite running on 1 GTX Titan X. Craziness, 8,000,000,000 transistors to play that trailer. Well, if the production value of games continue to grow, we're going to continue to increase our ASPs. ASP of our GPUs is declining Moore's Law. It has defied Moore's Law now for many, many years. And the reason for that is because production value drives higher demand for GPU technology, which therefore drives higher ASPs. And so we're benefiting from a larger installed base. We're benefiting from gaming become larger than ever. And we're also benefiting from the fact that ASPs production value is increasing. VR is going to take that to new levels like we've never seen before. Auto has been growing 100% a year. 85% is Jensen's version of CEO math 100% a year. Okay, so just doubling every year. Enterprise, our market share is very large and so our growth has slowed a bit. Well, there's 2 things that we need to do. We need to innovate and invent the future, and then we need to innovate and invent the future. And we have two examples of that for you. We invented grid so that you could virtualize graphics and put it into the data center and mobilize everybody and provide designers, workstation designers, digital content creators to provide them the freedom that we all have. Could you imagine we're going to invent technology that's going to free people? They don't have to be burdened. They don't have to be strapped to their desk anymore. 2nd, we're going to take computer graphics and design to a level that is just incomprehensible. We're going to show you some pretty amazing stuff. I think we're going to show some pretty amazing stuff. I haven't seen the demos yet, but if they're going to talk to you guys about this new thing that we're working on, I won't run it you, but it's pretty amazing, okay? We're going to innovate. We're going to innovate for growth. And then 3rd, HPC, as you can see, we're growing very nicely. I really do believe at some point we're going to reach the tipping point. And then growth can very well accelerate. Here's the thing that I see. It's already been growing very fast. It's not easy to grow the space because it's all about software. You have to develop the software. You have to develop the software and then it gets deployed. You're seeing that the benefits of deployment. And now as you could see, the groundswell of new software that's being developed for GPUs is growing all the time. It's growing all the time. It's going to show up somewhere. It's going to show up somewhere. If you develop the software, it will be deployed. If you develop the software, it will be deployed. You develop on a Titan, you deploy it on a Tesla. You develop it on Titan, you deploy it in the cloud. Tesla's all over the place, okay? And then lastly, our OEM business. If the PC market is down, we'll be slightly down. And if PCs become more thin and light, it puts pressure on the number of GPUs that are adopted. We still have a very large sizable OEM business. My sense is that over time, it will decline. But hopefully, my expectation is the growth businesses not only outgrow its decline, but the quality of the business is far better. Not only can you see that the growth rates are now big on large businesses, the quality of the business is much better. We're the only computer graphics company in history that has made its way and invented its way from arguably the platform for computer graphics, which is a workstation as we became a PC. We innovated and invented our way into 2 very challenging environments for computer graphics. GPUs are the largest, most complex processors that humanity makes. It is. The GPUs we create, Pascal, consumes more engineers, more transistors, more time than any processor that is built anywhere else. Well, that computational horsepower has a burden. It needs a large envelope. It needs large space. Energy efficiency is not the friend of parallel computing. Yet over years, we've challenged ourselves to drive the energy efficiency so high that we can now put GPUs into the smallest of mobile devices. We have made it so energy efficient that you could populate 100 of 1000 of GPUs in a data center and it won't melt. These data centers are now powered by GPUs because it is now the most energy efficient way to do processing. We had to invent those things. Graphics cares about latency and we made graphics so fast. We fought the speed of light and every layer of code to the point where we can now put graphics away in the data center and you feel like it's right next to your desk. That made it possible. Those 2 fundamental inventions, virtualization of graphics, put it into the data center, extreme energy efficiency, so that I could put it in the data center in large numbers and also put it into a mobile device. Those fundamental inventions made it possible for us to turn mobile cloud, which is unquestionably the most powerful force in the history of computing to turn mobile cloud into our friend. If we didn't do that, we'll be fighting mobile cloud for the rest of time. Obviously, no computer company will be a computer company in the future if they're not in mobile cloud. We've turned mobile cloud into a platform of advantage. We've turned mobile cloud into an innovative platform for us, one where we can now inspire new ideas instead of all day long worry about it. I am so excited and so proud of the company for making that investment. And look where we are now, mobile cloud, big and growing businesses, the ultimate difficult challenge. You can put storage in the cloud, that's easy. You can virtualize computing, that's easy. Computing is virtualized all by itself anyways. That's what virtual memories are for. Virtualizing graphics inherently hard. No one has ever done it until we did. We invented virtualization and for graphics and we made it possible for GPUs to exist in the cloud and mobile environment. Okay. Now they're both growth platforms. So very quickly, we have several winning strategies that I described. 1, the focus on vertical markets, not a whole bunch of them, the ones where our expertise, the visual computing expertise our company has is utterly vital. It's utterly vital. Without it, you will not survive. Without it, you add no value. Without it, nobody returns a phone call. In these vertical markets, our expertise is utterly essential. Number 2, engage in these vertical markets, but be thoughtful about the products that we bring. These platforms has to be much, much more We know the problems they need to solve. We know the problems they will need to solve, and we know the problems they wish they knew that they will have to solve. We're solving those today. And then 2, data center growth and mobile growth. Those are the 4 basic strategies of our company. Vertical market, add a ton of value. The way to do that is domain expert, being a domain expert. And then don't forget, mobile cloud is the future. Mobile cloud is the future. We've got to go ride that wave. And if we can ride that wave, we can expand our market dramatically. I'm going to show you just very, very quickly a few examples. This is an example of us focusing on a vertical market and not just thinking about it as a about it as a component. Titan ultimately is a component. Titan X is the most complex component the world's ever made. A normal company can't make such a component, but we don't even stop there. We turn the PC into a game console, if you will, within a game console within but as easy as a game console with a click of a button. With a little click of a button, it's called OPC, optimal playable settings, okay, with OPS, optimal playable settings with just a click of a button, it will understand the nature of your PC, understand the nature of the game and set all of those complicated settings all by itself for you. There's a whole layer of mathematics that go on top of our chip. We call it game works. Fire, smoke, water, hair, explosions, collisions, particles, cloth. Well, that's called physics. That's called Newtonian physics. Well, it turns out physics is really, really hard to replicate. Shadows, light rays, god rays, volumetric rendering. These are all effects of real life that are just incredibly hard to duplicate, mathematically intensive. We have a fantastic team of computational mathematicians Industrial Light Magic. We create these special effects, of industrial light magic. We create these special effects and we put it into these things called Game Works and we license up the game developers and they end up in games. 2 benefits. 1, the game becomes much, much more beautiful. And 2, because the production value is higher, more people play it. And if there is a subsequent benefit to ourselves, it therefore consumes more GPU horsepower. But we do it for the first two reasons. We invented GPU accelerated cloud. I already told you about that. We are in full production now. I think VMware announced, I forget which day, but sometime this week that vSphere 6 is now in full production. We have quite a few trials fully deployed and pilots running, 1800 or so that we're counting. There are probably others that we haven't counted because we don't see everything in enterprise as a very large space. But we captured about 1,000 the year before. And so we're growing at quite a large click. I'm quite excited about this. We have now set up the ecosystem from all of the servers and all the OEMs, all of their sales force, we call it OEM sales enablement. We have set up servers, OEM sales forces, OEM marketing teams, the software companies above it, VMware, Citrix, all of the application providers, we have now created the condition by which this entire ecosystem can go to market. We invented this platform. We invented this capability. And now we've harnessed the entire and organized the entire ecosystem of enterprises around the world to be able to engage literally every company in the world. Quite excited about this. Then use mobile to revolutionize cars. Use mobile to revolutionize games. I already talked plenty about cars and we're going to talk plenty more about cars. And so I'm just going to jump ahead to one that I'll introduce to you. You've heard me say before that mobile is more than phones. Mobile is more than phones. I said it over and over again, mobile is more than phones and mobile is more than phones. I think it's apparent now that mobile is more than phones. It's hard to hear that mobile is more than phones when phones is the only thing growing and the whole industry was being revolutionized. But here's the thing that's going to happen. Just as mobile cloud has revolutionized the way we use our mobile devices, because to put a computer into your hands, the only way to do that is with mobile technology, very low power technology, small technology. And it's connected to applications in the cloud. The benefit of an application world connected to the cloud is you have no idea what's about to be invented. And anybody who's cynical about it can't be cynical anymore. Look at the inventions that have happened along the way. Somebody all of a sudden invented the first app, which killed off a device, a GPS device. A map killed off a GPS device. Adding a small sensor into a camera with an app kills off a discrete camera. Of course, nobody carries music players anymore. But that was just really just the beginning. All of a sudden, Twitter comes along and Uber comes along and all these great Yelp comes along. I wish somebody would solve all that noise that goes into Yelp. I love Yelp. I just wish it was more pure. If you guys have an answer for that, that'd be great. All of a sudden these apps come along as a result. And we didn't think of it. We didn't think of it. Nobody thought of it except those innovators. And as a result, one after another app changed the way we enjoy our day. Well, I believe the same thing is about to happen for Cars and the same thing is about to happen for the way you enjoy television. We've been working for some time now. In fact, this was the 1st shield we wanted to build, but for a variety of reasons, it just took a lot longer. One of the reasons is waiting for an operating system that is connected to the world's largest store. Android TV is really important. I think Android TV is going to do the for smart television that smartphones has done to mobile devices. It's going to be connected to a very popular store. You're going to be able to download applications. And as you heard Elon said today, when you download applications, you have to design a computing platform that has a lot of horsepower. And the reason for that is because you just don't know what applications you're going to download. And if you want this platform to have some meaningful life, and I don't know about you guys, but my cable set top box is at my house for 15 years now. There is no hope that you can create a a device and applications organically enter into the world's largest store, and if that user interface was compelling, a lot of things could happen. And so we decided to build Shield. It is the world's most advanced set top box. It is the world's most advanced smart TV device. It is the world's first 4 ks smart TV device. It's powered by Android TV. It's powered by Tegra X1. It's connected to the world's most popular store. And of course, because it's Android TV, it has a really fantastic user interface called voice search. And our hope is this, before I show it to you, our hope is this. Our hope is that we will help redefine the television experience, that there are 40,000,000 over the air set top box over the top set top boxes already, that this is going to the way people enjoy televisions in the future. Everybody will have a smart television. There are a lot of televisions in the world that don't have that capability. And that's one of the earliest ones, the best one and the one that has the ability to unify entertainment for you, meaning whose television shouldn't play games? We've already established everybody plays games. What television shouldn't enjoy games? We brought those 2. We turned a game console into an app. We turned a game console into an app. Just like we turned a music player into an app, a camera into an app, a GPS device into an app, We turned game console into an app and connected to an invention that we made called Grid. Okay. So before without further ado, guys, let me introduce you to Shield. Thanks, Anson. What you're seeing here is the Android TV UI running on who's going to his name is not actually Shield. I see Shreedhar every week and we're always playing with Shield. For a moment there, I thought his name was Shield, but his name is actually Sreedhar. Sreedhar, take it away. I'm sorry. All right. So what you're seeing here is the Android TV UI running on Shield and it just looks beautiful. The whole UI is now optimized for a nice 10 foot lean back experience that you can control using a game controller or remote. And because it's Android, all your Google Play movies, music, everything that you purchased on your Android tablet or phone is now immediately accessible in your living room. It also provides you really history. And this is the recommendations tab on top and it basically pulls content from all these apps that are installed on your Shield device. The thing I absolutely love about Android TV is its powerful voice recognition feature. Instead of typing in long strings of text in my game controller to search for content on YouTube or Google Play Movies, now I can naturally speak to my TV using voice recognition. We have a microphone built into our shield controller and our shield remote, so I can basically use that and talk to my TV. Let me show you how. Show Me Movies by that Robert Downey Jr. Has acted in and lists it in a nice format here. It also shows me YouTube clips that Robert Downey Jr. Is featured in. So it's really powerful. It's pulling in content from all the various Google sources. So I like the movie Avengers. I'm going to go ahead and click that and go to Google Play to purchase it. Since I've already purchased it, I'm going to resume playing. It's being seen from the web, so it will take a few seconds for it to buffer up to go to full HD resolution. Another cool feature about Android TV is it has something called as info cards. Here for example, I can pause the movie and it immediately recognizes the actors on screen. It recognizes that I have there's Chris Evans on the screen and since Scarlett's face is facing sideward, it just says I miss Scarlett Johansson. I don't know much about Scarlett, so I'm going to go ahead and click on her info card to find out more about Scarlett. It says that she acted in Avengers and she is the Black Widow. So And it also lists all the movies that she's acted in, the YouTube clips that she's featured in and what are the other cast members of Avengers and what people search for when they search for Scarlett. So very powerful way to get information about your favorite actors, music and stuff through Android TV. And like Jensen mentioned, about Shield is it's the world's 1st 4 ks playback device, living on Just a quick comment, Shreedhar. One of the things I really love is the day I took it home. Most devices when you take it home, when you first plug it in, it's a brick, Okay. It's a digital brick. The thing that's really cool about this device is you go home, you type in your Gmail address, everything shows up. All my music has showed up, all the videos I've purchased, all that showed up. And my preferences are there. It's actually really interesting. All right. So, Shield is the only living room device that is capable of playing 4 ks videos, not just 4 ks videos, 4 ks videos at 60 frames per second. No other device in the market is capable of doing that. Let me show you what 4 ks 60 FPS looks like. So that's 4 ks 60 FPS video. A lot of content is coming. Content providers like Netflix, Amazon, Hulu have all talked about bringing amazing 4 ks content to the market, and Shield will be ready for that content. And obviously, being a living room entertainment device, it's not just about videos, it's also about music. And like I mentioned, I can go to music players and use my voice search to search for stuff. For example, Journey. Goes ahead and pulls up all the albums of Journeys. Let me go in here and select the greatest hits. Let's play one of our favorite songs here. And now I can go back to my home screen and like do other stuff like watch photos. I'm going to show you a bunch of photos that we captured at our Project Inspire event, which we held last year. This is basically an event that we do every year to help out the local community. We basically select a local school or a community place and try to improve it by rebuilding it, planting trees and stuff like that. So really fun event that I really enjoy going to every year. So that's Android TV and Shield as a great media playback device Shield. You can either download great Android games from the Android store or you can stream games from the web through our NVIDIA GRID cloud gaming service. Shield is powered by our Tegra X1 mobile processor, which is one of the which is the most powerful mobile processor in the market today, and it uses our Maxwell GPU. And you can get some amazing games. And what you're seeing here is Crysis 3 running natively on our Shield device. It's amazing that a game that required a high end GeForce PC just a few years ago is now running on a mobile device that is thin and sleek and can sit in my media center in the middle of the year? Architecture is exactly the Okay. Unity Engine. All of these engines, it doesn't matter which engine it is, could be a Crytek engine. In this case, it's a Crytek engine. These engines are exactly the same and produces exactly the same pixels as GeForce, which makes the portability of it incredibly easy. We recently launched our NVIDIA GRID Cloud Gaming Servers through which you can stream amazing PC games at full 1080p resolution at 60 frames per A good way to test the latency of any cloud based gaming is to look at racing games like this, which are very latency sensitive. So take it away, Ben. Sector 1 is where you really need to push yourself. Good. Let's push for first. A hard time keeping up with that. And the reason for that is because that's twice the performance of current generation game console, state of the art game console. It's a click away, click and play in 60 seconds, click and play. No downloads of 10 hours. You don't have to run the Best Buy. You just click and enjoy, just like you do with Netflix, just like you do these days with Amazon Prime. We're going to do for games what Netflix has done for movies. The second not so easy feat was Ben's achievement just now. Other companies have 10% free time for inventions. We have 10% free time for gaming. All right. Very good job. So, Shield. We believe that Shield will revolutionize your TV experience. It is the world's most advanced smart TV device. And we believe that future of smart TVs is about all kinds of applications, including amazing games. In doing so, not only are we changing the way you view television and hopefully bring to everybody the benefits that smartphones has for mobile devices, but we're going to dramatically expand the reach of games. And notice what we're doing here. You've seen this strategy before. This is just an even larger version of that. This strategy is exactly the same as adding a GeForce to a Windows PC and turning Windows PC into a game console. We're doing exactly the same here. We're adding Tegra to a smart TV platform and turning that smart TV platform, of course, into everything that a smart TV is, but also a wonderful game platform. Okay? So revolutionizing smart television, the Shield Android TV console. We're going to put it on sale. It'll be available in May. And the base product is available at $199,000,000 And we're going to be announcing peripherals and its pricing and so on and so forth as we get closer to launch. Let me give you an update on IP. I've already mentioned the importance of IP to our company, the importance of invention. We are the world's largest visual computing company. We're the most innovative visual computing company and this is an area we care very, very deeply about. You guys can see that visual computing and its reach is growing all the time. That can't happen without invention. That can't happen without So we care very, very deeply about fundamental invention in this field. We have a treasure chest of great IP. And we're serious about monetizing it. We're in litigation with Samsung and Qualcomm. We don't have anything particularly to report today, but the case is moving nicely through the courts. And on June 22nd and June 23rd, we hope to have the first of the ITC hearings. This is a very important case for us. And largely, our IP strategy is focused on this at the moment. Okay? Growth. All of these new things that I've talked to you about over the years, and I think that we've been saying the same thing over and over again, today, of course, we get the chance to reframe it again, This reshaped NVIDIA with our new strategy and our way of engaging these vertical markets deeply. Rather than being a component company, we no longer think about how many GPUs are sold. We no longer think about how many GPUs are sold. That's the component view. We no longer think just about what's the ASP of GPUs. We care about both of those matters. But that's not enough anymore. What's much, much more important is what is the market opportunity within that market that we serve. The the market opportunity for GeForce is how big is PC GPUs. How big is PC GPUs? But that's not the case anymore. The question that we have to ask ourselves now is how big is the gaming market? Because we're not engaging PC GPUs, we're engaging the gaming market. How big is the gaming market? And we have 2 ways to engage it. We have engaged it through PC, which is still growing, surprisingly large. It's going to continue to grow, believe. We have every evidence it's going to continue to grow. And we have a new way of engaging that game market through Shield. 2nd, the automotive market. Rather than selling components into the automotive industry, we're selling computers into the automotive industry. Instead of thinking about it as a GPU, as an SoC, think of it as a mobile supercomputer. Think of it as a mobile supercomputer, rich in software, rich in capabilities. Some of the capabilities are related to, of course, infotainment and beautiful digital clusters. Some of the capabilities are are are related to deep learning. As we go towards self driving, we don't have to get there without spinning off and not get the benefit of spinning off a lot of capabilities along the way. We'll get there. We'll get there when we get there. However, we're going to spin off a lot of capabilities along the way. Okay? So don't be distracted by the fact that we're working on self driving cars. It's the same thing as we're distracted by trying to solve photorealistic rendering. Along the way of photorealistic rendering, along the ways of virtual reality, along the ways of being able to replicate everything that you see right now, we're going to spin off a lot of great technologies along the way. Auto is not about chips, auto is about auto computers. Enterprise, high performance computing, the way we go to market with grid increasingly software rich. Increasingly software rich. The processors that be in the cloud will be many and growing. We see those as large markets. Okay. So that is my part of the afternoon. I appreciate all of your attention. It's been fantastic talking about our company story with you guys all throughout the years. I want to thank all of you for your support. And I think that the opportunity for Q and A is afterwards. Am I right, Arne? Okay. All right. Next up is Jeff Fisher, Head of our Gaming Business. Fish? Thanks, Jason. Thank you, everybody. Hello, everybody, and welcome. It's another year of Investor Day. I want to talk about PC Gaming. I want to talk about PC Gaming. We had a good year in GeForce Gaming. I think you all many of us were talking last night about what an amazing year was. We've seen growth in virtually every region in the world, especially in China and Southeast Asia, dollars 2,000,000,000 plus revenues past year for GeForce gaming business, 36% year on year growth. We also launched Maxwell, our certainly best GPU ever and the most advanced GPU ever built. Maxwell delivered 2x the performance of a prior generation GPU and 2x the performance per watt, the 2x the power efficiency of prior generation GPUs. Today, Jensen announced Titan X, our new flagship Maxwell GPU, the fastest GPU we've built. Titan X is priced at $9.99 It's a product that we've engineered in house and we will ship out to the channel to all of our channel partners as a complete product. And it really bears the resemblance of a high performance gaming product. I can pass this around. Why don't I try that? Although you guys can really appreciate the quality of the gaming product we deliver, see if it gets all the way around. And please, that one does not work. So I don't want to see it on eBay not making it around and see it on eBay later today. So Maxwell has been great for our business as well. But first let me back up as I talk about the PC gaming market, let me back up a little bit and talk about gaming overall. There's roughly 1.7 1,000,000,000 gamers in the world today. Jensen mentioned virtually anybody over 15 to 20 is a gamer. This is roughly 20% of the world's population playing games. They play on a multiple of different platforms, including and they play on mobile cloud devices. And they play on mobile cloud devices. PwC's entertainment survey last year showed that video gaming number 2 to cable subscriptions, but is ahead of virtually every other type of household entertainment spend, ahead of books, ahead of movies, ahead of music and continues to grow. Take for example, the movie Transformers: Age of Extinction that came out last year was the number one grossing $1,000,000,000 in $1,000,000,000 in sales. Now contrast that to Grand Theft Auto V that came out last year. Grand Theft Auto V did $1,000,000,000 in sales in 24 hours. It broke every entertainment revenue record in history. And that was just on one platform, that was on console. Grand Theft Auto V, it turns out, comes next month for PC. And we're really excited about that to help drive really the PC gaming community. So games are driving household spend. Gaming is a huge market and continues to grow year over year in all regions in the world. So let's well, one more thing, one more point I wanted to make. Game Developer Conference was 2 weeks ago up in San Francisco. It's kind of the mecca of the gaming industry for game development. There's always an annual survey where they ask the attendees, what platform do you intend to target next year? A PC again comes out as number 1 target for game developers attending GDC. Mobile cloud is number 2 and a distant 25% says console. So PC remains and continues to be a very relevant target for game developers and game development and platform for gaming. So let's take a look at PC gaming in particular. 330,000,000 core PC PC gamers. But these are core PC gamers, gamers that play hours and hours a week on PC. Gamers that spend money on games, dollars 330,000,000 and I think Newzoo is estimating this is growing at about 10% a year for PC gamers. DFC, which tracks software revenue to PC gaming shows that PC games is roughly $27,000,000,000 $28,000,000,000 business today and that's growing at a CAGR of about 8% and should continue to grow for the next several years. Steam, you may or may not be familiar with Steam. Steam is the number one digital store for buying games online. Steam's active user base has grown from roughly 50,000,000 in 2012 to 125,000,000 last year. And this is dedicated targeted at PC gaming. There's roughly 4,000 developing their own gaming OS for PC called Steam OS. And there's roughly 1,000 titles of that 4,000 that are targeted at the Steam OS, Linux gaming. But it isn't just Steam. There's also new gaming genres like MOBA that Jensen mentioned, multiplayer online battle arena that has become really the turbocharger for esports. But League of Legends, the number one game in the world, has roughly 27,000,000 daily players. 27,000,000 people are playing League of Legends every day. League of Legends is a PC only title. The reason for that is there is a genre, a class of gaming, and certainly competitive gaming that wants to play on PC. Why keyboard and mouse? It gives you a level of gameplay that is non existent through a controller and other platforms. PCs were very relevant to future of gaming and today and current gaming. So what are a couple of factors that are driving PC and continue to make the PC platform growing? I know I talk to you each year at these events and the number one question is how and why does this continue to grow? Every year it does and it is not in New York, it is not in particular Korea, Southeast Asia. And one of the real turbochargers of this has been East's computer gaming as esports. Roughly 200,000,000 global fans of esports. It's a pretty amazing number. And these are people that are playing or aspirational gamers or people watching online. They flock to events. The League of Legends Championship, which was held last year in Korea, which was really the birthplace of competitive gaming. It was held last year in Korea in an outdoor soccer stadium, World Cup stadium. They attracted 45,000 fans to fill this outdoor arena to watch an esports competition in Korea. They came from all over the world to watch. The gamers that are competing in this championship are celebrities among the gaming community. On top of that, there were about 30,000,000 people that tuned in online to watch the championship. They not only come to these big events, they come to small gaming events all over the world and we've sponsored many of them in Taiwan and China and the S. As little as 50 to 100 people in ICAFAs to several 100 to 1,000 in gaming venues. So esports is big and growing and the audience fans are rivaling some very well known established franchises like the NHL, the NFL and even Formula 1. This is a die hard base of gaming fans. The other another factor fueling PC gaming is the social aspect. Twitch, which was roughly started in about 2011 as a gaming channel. And it just caught fire. If anybody wants to go watch live stream of a gamer and the anybody as from an amateur to a pro can broadcast their game play on Twitch. While Twitch has grown to roughly a 1000000 broadcasters, people broadcasting their gameplay gamers, 100,000,000 viewers who are tuning in at any time of day, find their favorite gamer 100,000,000 people watching 16,000,000,000 minutes of gameplay a month. And as you know, Twitch was bought by Amazon last year for $1,000,000,000 Amazon sees this as a very important cultural and social vehicle for getting in touch with this enormous gaming community. YouTube. YouTube is a place that you can clip and upload your playing a game or want to know exactly the right technique to take down the boss in a game, you can go to YouTube and see some of the best people or just amateurs teach you how to play through it. Now you think your kid is actually on Khan Academy, but in fact they're on the Battlefield 4 channel, trying to figure out how to get through that last level. Trying to figure out how to get through that last level. There's roughly 15% of the content on YouTube is dedicated to video games. And the content, the gaming content on YouTube generates about twice the engagement of any other content on YouTube. People will watch it and re watch it. They'll comment on it. They'll talk to their friends about it. They'll share it. YouTube is also an important social platform. In China, where social gaming has driven this ICAFE culture, we've talked about ICAFE with you for years. There's roughly 150,000,000 icafes around China where people gather to play with their friends, 150,000, sorry, where people will gather to play with their friends. Social gaming. Game streaming is just now starting to pick up in China. Yy.com, which is a huge social network in China In fact, we have a relationship In fact, we have a relationship with YY that with a single click, you can now from a GeForce PC broadcast your gameplay up into YY. So this is an exciting area of growth for social gaming and streaming. Competitive gaming, social Okay. So Jensen mentioned it a while ago. We've talked about this for years. It's an important ingredient in the PC as a piece of gaming hardware. It is open and scalable. Configure a PC for the right performance and price that suits their needs. DIYer, they can build it or they can buy it. In techniques to gamers who want to build their own PCs around the world. It's like car modders. Kids used to mod their car. I know I did. Now they like to mod and update and think with their PC. 1 of the challenges of a PC that is so configurable is compatibility and optimization. Every PC has got slightly different memory, CPU, hard drive, GPU, monitor So how do you make sure that a game that is being developed for PC runs best on that PC? Well, we've leaned into this problem and have started and been working on a game platform around GeForce. We no longer just sell the GPU, although it is the heart of what we sell, GeForce GPU, and a range of GPUs for multiple gamers. But on top of that, we've built a client we call GeForce Experience. Jensen had mentioned it earlier. GeForce Experience is designed to make your games play best on your PC. Tons of gamers ready to download it and play it. This morning, our 57 1,000,000 GeForce gamers who have GeForce experience installed on their PC got a notification that there's a game ready driver available for Battlefield Hardline. Click here within our client to download and update your PC and get it ready for Battlefield Hardline. And the issue is, is that Battlefield Hardline has been years in development. Our engineers have been working with team. We've been QA ing it. We've been making sure that it runs best on this wide configuration of PCs. And we want to make sure that every PC is ready for Battlefield Hardline when it comes out. And we want to make sure they have the latest driver, the latest configuration of OPS for GFE the day it comes out. So we align what I'll say is our OTAs around the most important games that come out in the industry. And there's roughly 1 a month. Get a notification, update your PC, get ready to play Battlefield: Hardline. The developers really love it because they know that consumers are going to get the best experience on a GeForce PC. Another aspect of GeForce Experience is OPS, click to configure. Of course, there's many settings in Battlefield Hardline. We want to make sure it's set exactly for the best experience on your PC. But also we're developing shared technologies inside of GeForce Experience. You can now go back and clip and upload to YouTube so your friends can see it or you can teach somebody about a playthrough, you can do that automatically. There's also a single click share to Twitch in GFE. So in addition to just keeping your PC finely tuned and optimized, we're building GFE into a share platform, so you can start to connect and share your gaming experiences with your friends. And finally is GameWorks. Jensen had mentioned it. Tony is going to be up in a little bit to share some of what's going on in the labs of what I'll say is NVIDIA's industrial light and magic. He has a team of some of the best visual effects GPUs. Lighting, smoke, fire, hair, whatever it is in characters that will get us closer and closer to cinematic realistic effects in games, Tony is working with our architects and game developers to bridge that gap and deliver next generation gaming experiences on GeForce GPUs and make sure they work that we've got game ready possible gaming experience on every GeForce PC. So GeForce is a platform. Okay. So let's talk about some of the growth drivers for GeForce GPUs inside the PC gaming market. And the first one I want to mention is notebooks. Gamers today want a portable way to game. They would like to take their gaming on the go, dual purpose platform to study with, at a college or just take portable notebook. In fact, we call them transportable or what I used to call them are desktops in disguise. These were very big bulky notebooks that the portability of them was roughly to run from 1 power outlet to the next. You had a rough about a half an hour of play time on your gaming PC. Well, Maxwell changed all that. Launching Maxwell has enabled a range, a new generation of sleek, sexy gaming notebooks from a number of new players. This is a notebook built by MSI, MicroStar. And in this notebook is thin, it's light, has got long battery life. I'll leave it up here. I don't know that this one will definitely show up on eBay if I pass it around. It's thin, it's light. It has a GTX 970 class. It's one of our highest end GPU class GPUs in it. These notebooks are have resolutions of 1080p up to 4 ks. They're designed for 1080p full HD gaming at 60 frames per second of all the latest games. Long battery life, they come pre installed with GeForce Experience. And as you can see, we have seen a real acceleration in the growth of gaming notebooks. It's not just new entrants like Microstar and Gigabyte. Alienware, Dell has a is shipping Maxwell high notebooks, thin and light, Lenovo as well. And we're seeing Tier 1 OEMs now getting into the more and more into the gaming space. But this segment is alive. They are the same they play the same League of Legends and Battlefield 4. The new gamers who want portability are picking up gaming notebooks. One of the drivers of GeForce GPU in PC gaming. Another as Jensen alluded to is production value of games. And this is really important. When Tony gets up and talks, you'll see some of the future of effects that are going into games and new effects do drive GPUs. But more importantly, as an ecosystem and industry shift, new consoles launched at the end of 2013. PlayStation 4 launched at the end of 2013 new generation of consoles have raised the baseline, the floor if you will, for the target of AAA games. When AAA developers are targeting a low baseline, they have to stretch way up to take advantage of a a high end GPU. Now that baseline has raised 6 to 7 times. So AAA games are targeting a new class of performance. You look at a game The Witcher from 2,006. In the same era as Xbox 360 and PlayStation 3. The next generation of Witcher, which is launching in we just started marketing it on GeForce. It's going to be available, I think, in the end of April. You can see the fidelity of the image of the current version of Witcher moving closer and closer to, well, I won't say reality because we still have a long way to go, but a much more realistic effect taking full advantage of the hardware that's available today that wasn't available back in prior generation console days. So AAA game production value is increasing and a large driver of that is this new baseline set by console. Our GTX 960, which was launched in January, is roughly the equivalent experience 1080p, 60 frames per second on a PC. So I'll call that a baseline for PC. This is where AAA games over the next several years are going to be targeting for that sweet spot for their baseline experience GTX 960. So what does that mean for GPUs? And I'll use CEO math here, because Jensen sitting on Jensen's knee for the last 21 years, I've learned a few things from him, including what CEO math means. Okay. I wasn't sitting on his knee, standing by his side. You look at the installed base of GPUs. Yes, that was kind of creepy. You look at the installed base of GPUs, 960, which we just started shipping in January, there are roughly 100,000,000 GPUs in the installed base that have a lower class of performance than GTX 960. As the AAA games continue to push past this new baseline set by consoles, this is an opportunity, it continues to be an opportunity, which is what we've seen over the years, but continues to be an opportunity for this installed base to continue to be motivated to upgrade their PCs. And we think Maxwell, the performance class of Maxwell going forward is between that and the production value games is a great opportunity to drive GPU, GeForce GPU over the next several years. Finally, new gaming experiences. The current class I just mentioned, the new baseline 1080p gaming 60 Hertz PlayStation 4, GTX 960 is what's really driving the business today. 1080p screen is really the screen of choice for gamers. It is probably the most popular resolution full HD among gamers today. But if you build something immersive for gaming, we have learned if you build something immersive for more realistic experience. So we've already seen that with 4 ks gaming. Gamers are upgrading to get to a higher resolution screen. But at GDC 2 weeks ago, we all experienced something pretty mind blowing with virtual reality. A number of the game developers wanted to demo virtual reality, the latest generation, some pretty amazing virtual reality demos. EPIC, Valve, Crytek, all had developed these amazing demos to show at GDC in a VR with a VR headset. The problem was there wasn't a GPU on the planet fast enough to drive it in a way that it was completely immersive. And if you've ever had a VR headset on and I encourage you, we've got 2 set up in the exhibition area, because we can't crowd around the screen, it's 1 person at a time. If you have the time, I hope you will experience it because you will see, I promise, the future of gaming if you have an opportunity to put it on. It's pretty mind blowing. The prototype VR headsets that were shown at game developer conference are roughly 1 megapixel per eye. It's about 1 kx1 k. You have to run those at 90 Hertz, 90 SP fast frame rate because imagine putting your phone right up to your head. I mean literally it is right up to your eyes. It's got to be fast. It's got to be responsive. It has to be high resolution for you to be really tricked into believing that you are there and not get a little motion sick. But at about 1 kx1 k90 Hertz, it is pretty amazing. There's still prototypes. In order to drive these next generation demos with this generation demos with this version of VR, call it baseline, you needed a Titan. I was in the room when Tim Sweeney, who's the Head of Epic Games called Jensen and said, hey, dude, I need a GPU. I can't show my latest generation demo in the VR headsets at GDC next week without some hardware. And he knew we were working on Titan X. Our plan was to surprise everybody and launch Titan X today. But we Tim's a great friend of ours and Epic is an important game development we, turns out we had about 20 Titan Xs that we populated at demos at GDC 2 weeks ago and announced its existence, saved all the specs for today, but announced its existence. So you need a Titan X, a $1,000 GPU, our highest end flagship product to really enjoy a basic VR experience today. It's about 2x the horsepower required that you would need on a 1080p 60 Hertz. Now imagine to get to what perspective, 4 ksx4 ks90 Hertz, which is what you would want to have a fully immersive experience, is about 12x the horsepower of 1080p today. Now you add a demo like RedKite where you're not just talking about the number of pixels, but the quality of each pixel. Each pixel is immersed, it's rich. Now you're talking about 24x. So I believe the future PC gaming is very rich. Pushing numbers of pixels, pushing quality of production value of content. We've got many, many, many, many years to go before we can deliver on something that would be real life in real time. Lots of headroom for us in this industry. And that's what gets us really excited in the GeForce team in engineering. Sure, every day we get motivated by competition. We want to stay ahead. But more importantly, we really want to deliver next generation gaming experiences to gamers. And it is there. It is possible. It is within our reach. There's a long road map of things we have planned. There's a long future of things we can deliver we're excited about and are just not technically possible today. But that's really what motivates and inspires the gaming organization inside of NVIDIA from day 1. Okay. So before Tony gets up and I think it really shows you some cool demos, talks about our game work strategy, NVIDIA's effectively our industrial light magic inside of NVIDIA. Let me quickly summarize. PC gaming is big and we see it continue to grow. We really do. Esports, social, scalable, production do a So they're not just inspired to buy a new GPU because of speeds and feeds, but they enjoy the entire GeForce gaming experience. And finally, growth. Notebook gamers, production value content and really important new immersive experiences for the future of gaming that can really only be delivered on PC. So that's my story on PC Gaming, GeForce and Growth. And I'd like to introduce Tony Tomasi, my brother in arms, who leads our content dev team and a band of really brilliant engineers and artists in the GameWorks organization. Good afternoon. I only have a couple of slides and hopefully I can get to show you some cool technology. As Jeff mentioned, I manage the GameWorks effort for NVIDIA. The strategy is pretty simple, and it maps directly to what Jensen talked about earlier. We've got IntraSee's largest investment in value of games, to make games better, to solve the hardest problems, to create technology and middleware that's kind of at this intersection of engineering, art and science and really at the very forefront of real time graphics R and D, which drives gaming forward. There's no doubt that games of 10 or 20 years ago were compelling fun games, but they didn't have anything near the involvement of today's games or the realism of today's games. And part of that is the technology of gaming has advanced and production value has advanced. Gaming is now a huge business. It's the largest business in the industry. And Jensen mentioned that many games are investing movie like blockbuster movie like budgets and building these games. 100 of 1,000,000 of dollars go into the The output The output of this group, the work, the body of work that they produced is embodied in the GameWorks libraries. These are simulation SDKs, technologies, algorithms, toolkits. These are pieces of technology that we license to game developers so they can integrate into their games and advance the state of the art. The way we come up with these is that we work and we have been working with game developers and publishers for roughly 2 decades. We know all the key engines. We know all the key engineers. We know all the key game developers because we work with them on a daily basis. We know their pain. We know the challenges that they're trying to face. We know the problems that they haven't yet solved. And we pick the hard ones. We try to work at the very edge of what's because frankly, that helps move the industry forward. And we're always a little bit beyond what today's games are doing, but then we're trying to be there so when the next game comes around, we develop the technology that they're after. And lastly, we build tools. And these are complex pieces of software that are being built. Video games, modern video games may be the most complicated pieces of software being built. Essentially, they sit on top of a game engine, which is essentially an operating system for real time graphics and gaming. Millions of lines of code, all of which has to run-in a fraction of a second to produce a realistic image and be enjoyable. So they need go faster. And we do go faster. And we do these across a variety of platforms. Our main platforms are, of course, the PC and Windows and Linux, as well as Android. Our mission is to advance the state of the art to kind of push the boundaries to increase the production value of games. Ultimately, for successful games will continue to advance, the industry will get larger. And as those games advance, they'll need increasing horsepower and GPU richness to drive them. We've worked tirelessly with all these developments. So this is just a sampling of some of the games we've shipped over the last year. And this actually happens to be 12 different game engines that Game Works technology has been integrated into. And these are some of the 4, CryEngine, Call of Duty, some of the largest gaming franchises in the industry have Gameworks technology integrated into them. And we've integrated into those core engines, which tend to be reused for many games. So while this represents a dozen engines, we've worked with many dozens of games over the last year, all of which integrated some GameWorks or technology to advance the state of the art. So that's it for the slide piece. Now we get to the fun piece. So one of the most compelling things that we get to do in NVIDIA is invent the future. 1 of the biggest problems in gaming is lighting, particularly in real time. Video games have gone to enormous efforts to be very clever with the way they compute shadows and the way they compute light and reflections. But a lot of that is tricks, hacks, frankly. In particular, one of the problems is that to compute bounced light, you have to pre compute it today in games. You have to kind of set your scene up and then offline render what's called a light map or a hemisphere of light that you can look up into. The problem with that is that as you move around in the game or the game changes or someone blows a hole in the wall and the sunlight streams in, well, the lighting needs to change, but you've pre computed it and you can't do that. So we wanted to tackle that problem. So what we came up with is a technology called VXGI. It stands for Voxel Based Global Illumination. What we did was we created a volumetric representation of the game world using voxels, think Minecraft, something like that. And then we do a form of ray tracing called cone tracing to bounce light in the world in real time. So that not only do you get the normal kind of direct light or the direct shadow that you're used to in basic games, you get interactively bounced light so the lighting system can be recomputed every frame. You can get accurate reflections, you can get accurate shadows, and you can get some really stunning imagery. So, let me go ahead and go to the demo here and kind of step you through it. So what we have here is the VXGI library that we've integrated inside Unreal Engine 4. This is a shipping game engine. This code is available today to game developers. It's being used by game developers today. In fact, when Epic changed their business model Unreal Engine 4, they took what is quite possibly the most powerful game engine tool and they made it free, which makes incredibly high production value games available to literally everyone and this technology is integrated into that platform. So what you're looking at here is essentially, I'll call it the diffuse component of light. So let's kind of cycle through it. So here's diffuse light. So what you see here is no textures, no direct light. You see kind of the green glow and indirect stuff. You might have images like this before called ambient occlusion. But in this case, we're actually computing all the colors of light, the indirect bounce. If you look at what games Of course, light bounces Of course, light bounces. And in the real world, when it bounces off a surface, it is influenced by that surface and it can change the color of the light. But in a traditional video game, you get only direct light. You get this kind of very dark, stark looking world, and there's no color bleeding. There's no attributes of real physical behavior. But if we combine that with VXGI's indirect lighting and the direct lighting, you start to see a much more realistic world. You get light that bounces. You get green tints from the wall reflecting and bouncing onto the floor. This is all being done in real time. You get real reflections. So that little object there as it moves around the reflection on the floor is correct. One of the other advantage that we get with VXGI is traditional games will cheat with reflections. They'll just kind of look up into a cube map or they'll pre compute it. We can actually because we're bouncing light, light or reflection is nothing more than a bounce. So let's go ahead and cycle through the different reflection modes. So that's kind of the standard way Unreal Engine 4 do things. They kind of fake it. It's kind of specular trickery. You see that little sheen on the floor, but you don't actually see the objects. And I'll just go ahead and turn VXGI on. And now you actually see you're picking up some of the material, you're picking up the metallic surface off of the wall, you're getting a real time computer reflection. This kind of technology is integrated in games today, and I expect you're going to see games shipping using this technology by this fall. We introduced this as part of Maxwell towards the fall of last year. We've integrated in game engines now and I think you're going to see game engines shipping with really soon. This is pretty cool stuff. This is at the very bleeding edge. This is real time global illumination, running integrated into the game engine at 60 frames per second. So that's pretty cool stuff. So while as cool as that is, that's not actually the future because that's here now. So one of the great things about what we get to do at NVIDIA is we get to work on the future, the problems that are just out of reach for game developers. So if we can come back to the slides. One of the problems that's always been just out of reach of game developers is to do, what I'll call, accurate clothing simulation. Typically in a game, you get what I call plastic clothing or plastic hair for that matter. Everyone wears a helmet or they're all bald, they all look like Jeff or if they have hair, it's plastic hair. Sorry, that was probably a little too close to home. And their clothing tends to look kind of plastic. They wear a lot of armor and things of that nature because to do physically simulated clothing turns out to be really, really hard. The clothing itself tends to move and flow, it bounces and particular layered clothing one of these kind of unsolved problems in real time physics. Cloth is thin, it moves around. And as you move, it can interact with folds of other pieces of cloth or itself. And the solver, the mathematical solution for that has been out of the reach of real time graphics. We've actually solved that problem by actually modeling not just the cloth, but the air itself in the physical simulation. And for the first time, we're going to get real multilayered clothing simulated in real time. So this happens to be a system that we built on. It runs on the GPU. It will be coming to physics soon. This is a little bit out of taste of the future. It uses about 400,000 polygons for the dress. It runs entirely in the GPU and it's about 40 times faster on the GPU that won't have been on the CPU. So let's go ahead and take a look at that. This is all in real time. So just to prove, let's go ahead and change the cloth of her dress. Here we go, cycle through it. How about the one with the texture on it? I kind of like that one. There you go. All being computed in real time. This is the kind of basically simulation that in fact clothing designers use and they did it offline in many cases. They render a frame in simulation and then they'd look at the results. We're actually now doing similar style physical simulation rendering entirely in real time. The dress flows, interacts, the folds, it corrects itself, and then you can, of course, change it and stuff like that. This is, like I said, a little bit of the taste of the future. I think in a year or 2, you'll start to see games that have complex clothing that looks right as opposed to the plastic clothing and the helmet heads that everyone currently experiences in games. That's just taking that next step further towards production value and realism in games. In games. Okay. Let's come back. One more super hard problem. And in fact, this tends to be a problem that is really 2 problems in 1. Fluids. In most games, you don't see a lot of water that's interacted with. You don't see a lot of fluids that are waters and you'll see people walking in a river leaving a little it'll look like a wake, but waters and you'll see people walking in a river leaving a little it'll look like a wake, but really it's just a circle of textures behind. There's no real interaction there. And that's because fluid simulation is computationally enormously expensive. It's a volumetric computation. Not only is the fluid simulation complex, but the ability for one simulation to interact with another simulation is a real problem. People hack it in games today. Having your rigid body, your bullets, interact with other things in the game world turns out to be physical systems that historically just don't play nice together. And so it drives up engineering costs and it makes the implementation of those things in the game just impractical. We've developed a system called FLEX, which is a unified physics system, which basically can build a variety of physical simulations out of particles. These are special particles, but they can represent cloth, they can represent water, they can represent rigid bodies and they can all interact with each other and they all run great on the GPU. So what we thought we do is try to put together a technology demonstration that demonstrates a surface here. You'll see a volumetric fluid simulation inside it and then shoot it with a bullet. There's roughly speaking about 400,000 particles in a volume being simulated here. And we're actually ray tracing the results in real time on a Titan X. So let's go ahead and take a look at this demo. Yeah. One of the great things about doing things in real time is you can control the time and you can accelerate it and with a camera that shoots 10,000 frames a second. With the physical simulations we're able to do now on GPUs and the really high quality rendering we're able to do now on GPUs, we can start to approximate much more realistically real world properties, not just physically based rendering, it's kind of all the rage these days, but actually physically based behavior. Things will react properly, simulations can interact with each other. And with the horsepower that we're at, some of these really tough problems that game developers have found impractical, we're at the verge of making them practical impossible to manage, which is pretty cool. I can't wait to see games where we had even more realistic, I guess, we'd use that up, blood splatter, mud kicking off of tires, you name it, or clothing simulations where the cloth can be torn and interact with the game. These are the kinds of things that we're working on at NVIDIA with Gameworks, again, kind of drive that production value up going forward. So hopefully, you guys got a little taste of the future. Like I said, VXGI is really here now. That's that global illumination technology. These two technologies are really kind of a peek out of the labs. So it's kind of a special treat. These are things that are going to be implemented over the course of this year and probably shipping in games next year, whereas VXGI is a this year company. So that's what I've got. So GameWorks, it's about driving the production value of games up, making the GPU an increasingly critical part of the gaming equation, driving the industry forward so that we get realistic games in graphics in real time. Thanks. So who's is it Jeff, are you up next or is it Greg? It's Jeff. Okay. So next, let me introduce Jeff Brown. He's going to tell you all about, I believe, cloud and grid, is that right? Enterprise graphics. So I might have stolen your thunder. I apologize for that. Thank you. Thank you, Tony. While a lot of us are graphics heads, Tony, Greg, myself, history at Silicon Graphics and Apple and HP and that kind of research and technology is just amazing stuff. And in the spirit of Arnab's history of the Standby Comic, now for something completely different, we're going to be talking to you about our Enterprise Graphics business. My name is Jeff Brown. I'm responsible for externally what we call ProVis and Design, internally what we call Enterprise Graphics. And then Greg Estes is going to join us. He's our VP of Enterprise Marketing. So he spans over not only Enterprise Graphics, but also HPC. And he's going to introduce a brand new technology initiative that we believe is going to be the future of growing our Enterprise Graphics business around technical compute or technical graphics. Before we jump let me just introduce the 2 platforms. One of them that you know and love Quadro. This is a historical legacy for us, a dynasty. Silicon Graphics, for example, 15 years of absolute dominance. And it's because we are extremely customer facing, application facing, customer facing. NVIDIA is the place to be if you care about graphics and digital computing and we care about our customers and the work that they're doing. The paradox for them is they want to push the boundaries in technical graphics, but they're incredibly conservative. These are people whose work is driving earnings of companies or research, medical, energy, etcetera. So Quadro, our traditional workstation business, it goes to market through branded global workstation OEMs. A lot of times we say that Quadro is actually constrained by the channel that it goes to the market through. But very, very serious business, very serious customers, and we bring a lot of innovation to them over the years on a huge amount of commitment. Our newest enterprise graphics platform is what's called Grid. And Jensen described the challenges to bring GPUs to the data center, power efficiency, virtualization. We had to invent ways to basically fractionalize a very, very complicated graphics engine. And we had to figure out ways to deliver those pixels in a very, very low latency, low bandwidth kind of So the 2 platforms, we call them virtualization and visualization, you're going to see they're very related. Grid delivers the promise of Quadro in a virtualized data center sort of way. Before we go forward, we're going to look back at the two businesses. So Enterprise Graphics, we did $833,000,000 last fiscal year. Quadro, as we've talked about, really is the market leader in professional graphics. We really stand alone with that responsibility. We had a record year with Quadro. Quadro is mission critical to all sorts of customers. And Quadro is responsible for bringing dozens and dozens of aircraft cars, millions of medical radiology records. So very, very mission critical right in the heart of technical engineering workstations and applications. Now, we're going to bring Greg up in a bit to talk about how we're going to expand this market. And great lead off from Tony's presentation on GameWorks. And the huge benefit to being at NVIDIA with a massive research department is that we can leverage not only economies of scale of building these processors, but can leverage a huge amount of research. And one of the holy grails, I'm not going to give it away for Greg, one of the holy grails in professional visualization has been the ability to do something called physically based rendering. How do you create a physical digital prototype that makes it much faster to market, fewer iterations and better decisions upfront. And the basis for this is something that's called physically based rendering. It's a concept that's been around for a while. We're applying a ton of technology and a massive ecosystem to solving this problem and delivering that to millions and millions of professional users. So that's on the Quadra side. On the grid side, this is a tremendous anniversary anniversary today. A year ago at GTC, Ben Fathi from VMware, the CTO, you may remember, came on stage and we talked about a co development to bring vGPU, our virtual graphics technology into vSphere, which is the 80% 800 pound gorilla in enterprise 6, every version, every copy now shifts with NVIDIA GRID vGPU as of yesterday. And Jensen talked about the trials doubling year on year, and that's really only a fraction of the customers are going to experience the benefits of virtual GPU and vSphere. And we're going to talk about what those problems are and how that really solves the problems for those customers. So we expect to see explosive growth. Last year's grid growth was all built off of the work we had done with Citrix going forward and they represent less than 10% of the enterprise virtualization market. VMware represents 80%. So we did all that basically without the VMware engines engaged. So what we're going to do now is we're going to break this up into 2 sections. We're going to talk about the enterprise graphics market as a whole, talk about Quadro first. But I want to use this kind of as a map for you all to think about who we believe our customers are for Enterprise Graphics. If you're visual, you could actually imagine a triangle here. This represents the 725,000,000 professional users in the marketplace, PCs, laptops, workstations. We believe that this is our addressable market for pro graphics, whether those are PC graphics, workstations or virtual graphics. Quadro is laser focused, we call the designer market. That's an installed base of somewhere around 25,000,000 to 30,000,000 users. These users all use a tool or multiple tools. They might use a CAD tool, a digital content tool, a film tool, a medical tool. And they are doing the mission critical work within enterprise. And this has been our target for Quadra for all these years. It's a sizable total available market. And vision for how we believe that we're going to grow this TAM even further and our GPU penetration into this market even further. I'll come back after that and talk about how Grid allows us to address this and push down and expand our overall graphics enterprise marketplace. So without further ado, I'll introduce Greg Estes. Thank you, Jeff. So I'd like to talk about this area of physically based rendering, which is what we think is going to be a great opportunity for us to address a larger part of the market and bring something that's been the dream of designers to the marketplace. People have been working to be able to sometimes build creations that the first prototypes weren't what they sometimes build creations that the first prototypes weren't what they expected or they created a building had lighting that had caused problems in certain ways. If you take a look at this beautiful image here, you could think about there's I guess there's 3 ways maybe that you could create something like that. One could be, if it already existed, you could take a photograph of it. If it didn't already exist, you could have an artist design it, use Photoshop and other painting tools to create it, Or you could model it in computer graphics and then try to understand what it would look like when you went to go build it. And in doing that last one, you would be able to then take that model and use that model in your manufacturing process or your building process to construct it. So ideally, you would have a visualization that would allow you to understand to, of course, have the model. And that could be something simple or if you look at our customer Boeing with the 787 Dreamliner, there were more than 2,300,000 parts to that airplane. So the models can be extraordinarily complex. The second thing that you have to have is you have to understand what the materials are, right? Because the materials are going to affect when light strikes it and interacts with that material, certain things happen. That energy is either absorbed or reflected or refracted. And that light, as Tony mentioned and showed you, bounces around in different ways, which is all about not only what it's like to feel when the product is built, but also what everything is going to look like because that light bounce around. So if you look at the other example there, which is a real place actually, it's in Portland called the Portland Armoury. Where are those lights are and what it looks like if you're building a building, it could affect the safety or if you're building a product, it could affect how beautiful it is or the feel that a understand what your products are going to look like, which is of course important to the 25,000,000 designers that Jeff talked about at the top. Last year at GTC, our customer Honda for the first time at great expense more than $2,000,000 and great effort was able to visualize a car on stage with Jensen in real time, an entire automobile, slice it arbitrarily, understand what it looked like and be able to see that and manipulate it in real time, which if you can imagine if you're an auto manufacturer, it's an enormous advantage in being able to bring products to market not only quicker, less expensively and ultimately more accurately. You can imagine what would happen if for instance, you built a prototype and you found out that when light hit the windshield and with a certain material for the dashboard that glare occurred. Well, you would want to know that before you tooled anything and before you built it. So having an accurate simulation of vision of bringing this capability not to one customer with a TAM of 1, much as we love them, but to millions of center that we'll talk about more in just a moment, all together to have these products work together and then work with a series of partnerships to bring this capability again to millions of users. So it starts with bringing our rendering software, our physically correct, bring that broadly across the market. The second thing that we're going to do is to create what we call height of the material, it's got surface properties and how light will interact with it. So now we have something that measures light correctly and we have something that to bring to to bring to the market through Iray. And then we're going to make that scalable from laptops into high end workstations and out of that and into the data center. So you can use it if you're end workstations and out of that and into the data center. So you can use it if you're sitting in an airport end GPUs rendering a very complex design in real time. And so the heart of this is Iray 2015, which again is our physically based renderer, which calculates how light works. The second part is this new material definition language. So you'll be able to create materials and there's a file format that allows you to bring those materials that may be something that you would have assumed would already work in the marketplace. It doesn't. Today, it is generally not possible to have a material work in one of these 3 d design tools, then send it over to another worker in another part of your company, have them use a different tool for that and get exactly the same result. Because each of these applications physically correct, the materials are correct, the way we calculate light is correct, you will be able to get a correct answer, which is critical for these 25,000,000 designers. And we're going to do that in a way that's built specifically for Quadro and Quadro VCA. The Iray software knows that you have a Quadro in the system and can scale seamlessly and elegantly to multiple quadros if you have a workstation that's able to support and the highest end workstations now are able to have up to 4 quadros in them. And then go outside of the box into the data center with our new Quadro VCA product, which has the power of 8 of our highest end Quadro M6000s, roughly the equivalent of the ready packaging and then scale that from there. And as a whole, then that again brings us the ability to have interactive physically based Partners like Autodesk that have architectural tools like Partners like Autodesk that have architectural tools like Revit and design tools like 3ds Max, those that are used by entertainment customers and auto styling like Maya. You've got to so CATIA. CATIA is a great example. Dassault is one of our great partners and they have multiple products that we work within them. CATIA is about the highest end styling and design system that you can have. If you were going to go build a nuclear submarine, you would use CATIA to do it. And most of the auto manufacturers use CATIA or Siemens NX, which is one of their main competition. So we're working with the big boys in the most complicated problems. But we're also working down in the volume parts of the marketplace as well like 3ds Max, which is used by 100 of thousands of users and even DAS 3d in the lower right there. That tool is actually free. So we're building this community and this ecosystem all around this idea of bringing Physically Based Rendering to designers. Then the lower bar there is a very important part of the equation. Middle So our partners there, I'll start with Oldcastle in the middle. Oldcastle makes a library of different materials. They think of of materials that you can go get from them and buy them. And then when you put that in your model, it's physically accurate. You know what it's going to look like. Alec Arithmic on the right, it makes a series of software products that allow you to create your own material. It's used in the game industry, for instance, because if you wanted to do a Minotaur and it was drooling, there's no material for Minotaur drool or any kind of a custom design that you need to be able to do, right? You can do that using these tools. And then what's super exciting hemispherical scanner that not only measures what the light that comes off of the material, but the height map as well. It knows the texture of it as well. So if you're I'll make up an example Ford and you're designing the next generation Ford Fusion, you can take the materials for the seats. You can give it to X Rite. They can look at the that these tools and your designers can have that exact material. So you can make that specification for all the different partners that you're working with or all the parts of your design system. And all of this is working together based around our new Maxwell architecture to accelerate it. And a great example of that a real simple benchmark is this particular scene of the Z4, it was more than twice as fast, 2.4 times faster when using Maxwell and this new version of Iray versus last year's version of Iray and our previous Kepler architecture. And the results for our customers for this have really been outstanding. Here's an example from Audi. With NVIDIA Iray, the time I need for a 3 d model to meaningful image is extremely short and the creation of physically based renderings, I've never had these fast turnaround times. So we're providing real value to these customers that have these real needs and we're doing it in a way that it's going to expand our market at the same time. So value for us and value for our partners and value for our users as well. So we have an expanded market opportunity here. We've got this broad ecosystem of partners and we're scaling it from the laptops to the data centers. And I'd like to turn it back to Jeff to talk to you about the rest of our data center 2nd growth strategy, which is around grid and bringing this capability into the data center. Thank you. Thanks, Greg. And then just one note, we're not going to do these demos here, but off on the exhibit floor, which we're going to take you to, I believe, right after we're done, we've got examples of Ira physically based rendering plugged into the applications that Greg has talked about, as well as a pretty amazing spectacle called the death ray. So we invite you to check that out. So the segue here is into data centers. You can see everything that we're doing on the Quadra side, and we believe we're very enthusiastic and goes into virtual workstations, what we call vGPU, virtual GPU, which is the grid graphics virtualization platform. So back to this icon here, everything we've talked about so far with respect to designers where Quadro, it's addressable market sets that 25,000,000 user designer, we've never been able to penetrate down into these sub segments of users in the enterprise or business computer Virtual GPU gives us the ability to bring Quadro quality application performance compatibility into a virtual environment. Now we're partnered with people like Citrix and VMware and Microsoft and they've been coming from the bottom. Server virtualization and the 1st generation of VDI has really saturated this task worker space, where the applications are not graphics rich, like they use, they're primarily web or text based. So we're looking at a couple of different segments now where we believe that Grid Enterprise Graphics is going to penetrate. Some of our early customers certainly are workstation users that want to take advantage of the benefits of virtualization, which is to move to flexibility using any device, remote access, security. So part of the grid opportunity matrix certainly is designers. What we're really excited about is we're seeing very, very early indications. I shouldn't call them early anymore. When you're up to 2,000 trials, it's no longer early, but within this power user space. So roughly the way to think about it, for every designer, for every engineer in CATIA designing engine for Boeing's Dreamliner, there's 20 to 30 people downstream that use that data in some sort of way, documentation, service. And then there's whole sectors of users that are smaller businesses or more utility kinds of applications in the enterprise. This is where Autodesk AutoCAD sits Bentley Microstation. So our initial target for Grid is this 225 1,000,000 user base. Again, our motivation is to push down. VMware's motivation is to push up. And that's why we are so well synergized in the market. Now speaking of that, VDI is not new. Virtual desktop infrastructure is not a new concept. It has, however, been a failure outside of that task worker marketplace. You asked yourself why is that? Because the promise of VDI is so great. The promise of VDI, the enterprise is mobility and freedom, being able to work anywhere, being able to get more flex time out of your users, being able to manage your contractors and your interns with more security. IT managers love it because they're not managing 1,000 or tens of thousands of workstations and PCs and laptops. Users can bring their own devices and they get managed through a single pane of glass is what enterprise IT calls this. Everything is managed as a virtual workspace. And then finally, security is utmost on the radar for global companies security. How do you, Jens, talked about the benefit of virtualizations moving the data and the compute to the data center. The advantage of that is performance and flexibility and provisioning, but also guarantees you security. Your data never leaves your corporate data center. All you're sending down the pipe are pixels. You're sending pictures on the pipe. So it's ultimate security for your most important information, your car design, your aircraft design, your Star Wars Next. So here's where VMware, Citrix and NVIDIA are just so tightly aligned. Now last year, as I mentioned, we made a commitment together with VMware to bring vGPU into vSphere, which is their server virtualization hypervisor. And as of yesterday, we achieved that goal. Every copy of vSphere ships with NVIDIA's vGPU. Everything we've done so far in terms of lifting grid off the ground has been done basically with our very good friend, Citrix. They represent about 8% of the market for hypervisors. So as of today, our ability to address the market has increased by 10 fold. All of our trials, all of our initial pilots, all of our initial deployments have been either early access or with Citrix. That gives you an idea of context. They are the gorilla. They've now incorporated vGPU. We've got a ton of trials going on. We're well past 2 1,000 trials that we know about. We've gotten to the point right now where we're not only managing our lighthouse and trial accounts. We've got lots of channel partners, distributors, system builders, the OEMs, all their sales teams enabled. So we're really starting to see lift with the business. What I want to do real quick is jump to a demo. There's a couple of things we're doing with VMware in terms of go to market. We went through a really great early access and it's called direct access program with VMware. And one of the ways that we got enterprises and users to experience Grid before was available widely was we set up something called TriGrid. We literally set up our own virtualization stack with our own virtual workstations and we invited and enterprises and software developers to try Grid. It was a trial. You could register through a very simple web form and we give people different access times. VMware loved that idea because it is actually relatively difficult to set up a server with all the software, all the security. So together with VMware, drive. So try grid with the vSphere stack. So this is going to be open. We had about 20,000, 25,000 users to 25000 users try our own proprietary TriGrid. And going forward, amplified through VMware, we're really optimistic that this joint trigrid vehicle for people to test and trial is going to be a phenomenal lead development and actually initial qualification platform as well. This is co hosted, co marketed. We collect all the leads and share them with VMware. We distribute those out to our channel, either our VARs, systems integrators, OEMs. So, Mohan, if you could flip over. So, what we're going to show you is we're going to show you what Trigid looks like. You very quickly register, you submit, very low bar to enter here. But the benefit here is we gather a lot of leads. So Milan is going to show you what trigrid looks like. Now note he's on a Macintosh. He's on a laptop. He has launched a VMware vSphere vGPU Windows Desktop, fully accelerated. Now to Jensen's point, here's a bit of the machine is connected at 800 megabytes, 800 megabits because it's sitting basically on the hub of the Internet. So his access to data and files and applications is lightning fast. So he's got a 3 year old MacBook Pro running a lightning fast PC. So he gets application availability and compatibility while he's running on his machine. So you can see that here. What he's running here is a very simple, let's call WebGL, which is Google's that's called WebGL. It that's called WebGL. It's a benchmark for running browser graphics. Digital Aira, Aira was the star of GTC 2 years ago, I think. So this is running on our highest end titans 2 years ago. Now you're seeing this run on a virtual workstation. So this is a fraction of a GPU running up in the cloud, giving you performance that's actually better than that keynote performance for digital higher was just a couple of years ago. So making this kind of performance accessible in a virtualized environment. Thank you. So we're showing you here is an application from a company called Esri. Esri is it's called the GIS application. You may have heard of them, really famous. Every Department of Transportation or Urban Planning, government uses this is kind of Google Earth on steroids. The data here, this is a multi gigabyte data set running on his MacBook Pro, access to a huge amount of data. This is the city of Philadelphia, I believe. You can see this is completely a submeter lidar topography data. So used by all sorts of government transportation workers, you're going to drill a pipe or by domain, this is what you're going to use. Again, running virtually over the net off of TriGrid. Esri is so excited about this. They're using this actually to beta their next generation software. Very cool benefit to the ISV because they don't need all their user base to have the latest, greatest a yourself. It's a really, really amazing tool to show the world the value of virtualized graphics. All right. Thank you, Milan. Placeholder? Real quickly, I want to take you in sort of a staccato manner through a couple of case studies. We've been managing through our direct business development team with VMware a series of lighthouse accounts and want to kind of illustrate the use models and the kinds of customers that are seeing value out of virtual graphics. PSA, Peugeot, Citroen, one of the largest auto manufacturers in Europe. They've got sites in Spain, Valise, France, South America. Their CTO is actually here. He loved listening to Elon Musk. Their objective was they've got all these distributed sites. They want to manage one data center. This is a very traditional Quadra customer running CATIA. So their goal really was remote worker access. So this is productivity of their engineers over remote sites, global design teams. So they achieved that with Grid. And these guys were very, very early pioneers This one I love. These guys, STV, a global architecture firm based in New York and Philadelphia, They did the World Trade Center in New York. They did the Denver Airport expansion, massive projects, global footprint. They were not a Quadro user. They were using an Autodesk application, still had some graphics, intensive needs, but they were not a Quadro user. They weren't buying workstations. They didn't need to, but they need Grid. And Grid gave them what they needed, gave them the application compatibility. It gave them the productivity because their teams, their architects are all over the world. They set up mobile offices on-site, reduce their costs and increase their manageability. So all in all, good massive breakthrough for STP. We love these guys. They're actually presenting here as well. Their 2nd year in a row presenting their success story in one of our breakouts. Another one of my favorites, St. Laure's Academy, which is a pilot school, part of a local Bay Area Catholic Diocese, K-twelve example. In this case, they're teaching their students Adobe Creative Suite, Creative Cloud, photo editing, video editing, web development, very GPU intensive application. And what they can do here, the beauty here is that with Grid, they can teach their students, they don't have to worry about viruses. They can basically clean the images at the press of a button. They can also give their students access to way more resource than the students either have or they may have the budget for through provisioning. Students can also get access to the virtual machines from home. So again, we had no Quadra business in the education market to speak and this is a phenomenal example where virtual GPU brings a lot of value to K-twelve. They're the pilot school for this entire Catholic diocese district. Another one that's well, these are really stories, Metro Health, which is a HMO in Cleveland, Ohio, focused on geriatrics. They actually implemented VDI. They were an early Citrix VMware success story. But what ended up happening is they upgraded their software, the radiology software, and their entire system broke. It just broke. It could no longer view the radiology images. This was a higher resolution version of the same application, their EHR records system. So what they did is they had existing server infrastructure. They plugged VGQ in there. They ran a beta version of VMware and they were up and running. So this solved a problem where they just fell off a cliff and their doctors weren't able actually to do these diagnoses with their new software. So really, really great story here. So you can see new industries, new markets, but you're seeing a very consistent theme in terms of the value proposition for virtual GPU. And they see the kind of user base expanding as well through this technology. So that is that's grid. Qadra lifts into our virtual workstations as well. So there's this secondary benefit. Grid is at the point right now where it has lifted off. Business is doubling right now. The number of customers in trials, pilots, deployments has really achieved liftoff. It really is the essential catalyst to make this real. And again, we're super well aligned with VMware, Citrix, with all the system builders. And it really does expand our adjustable market for virtual GPU for graphics outside of our traditional enterprise enterprise graphics target, which is that designer market. So we're seeing early indicators of that as in fact true. So we're very, very optimistic about the overall enterprise graphics market. So again, I'm going to hand it over to Arnab and invite you to come see the demos on the floor. And thank you for your time. So the bad news is we're only at the halfway point. But the good news is Jens has already did everybody's presentation. Right? No one's laughing. Terrible. Anyway, we'll have to work on the comedy, but next time a little bit more. So right now, we have the break. We're gonna walk over to the demo room to show all the demos. But before that, we have coffee and drinks. So if you guys need that, we can go get that. And thank you very much. Good afternoon. You guys can all sit down. Jensen can do what he wants. It's his company. So anyway, we're just starting out the second half of our day. We have 3 more presentations and we'll have Q and A after that. So I'd like to introduce Shankar Trivedi to talk about our HPC and Cloud opportunity. Thank you. All right. Thanks very much, Orno. Okay. As Ornev said, I'm Shankar Trivedi. I'm responsible for our enterprise sales and industry business development organization. So all of the things that we do in front of the customer, all customer facing activities. And I joined NVIDIA about 6 years ago almost to the day. And my mission has been to grow our server data center business. And basically, I came to NVIDIA with about 25 years of experience in enterprises, data centers, servers, systems and middleware, working for companies like Sun Microsystems, IBM Corporation and Fujitsu Corporation. And I spent additionally, I spent about 3 years as the Head of Marketing and Corporate Development for a small cap enterprise software company. So it was a great privilege and very exciting for me to come and join NVIDIA with a view to sort of growing our enterprise business. And we started with HPC. So I work very, very closely with Jeff Brown and Greg Estes on the enterprise graphics side. And I work very closely with Ian Bach and Sumit Gupta, who are on the HPC and Computing side. So but today, my mission is to talk to you about our plans for HPC and cloud. And last year was a fantastic year for us. Tesla had yet another record year. We grew our revenues are now 279,000,000 As Jensen mentioned, we grew about 53% year on year. So it was a phenomenal year. And the Tesla sorry, the Kepler architecture GPUs, the Kepler architecture GPUs are just awesome for enterprise computing for data centers. In the previous generation of Fermi, we made very, very significant strides, but Kepler has just been transformational in terms of its architectures designed for the data center and for enterprise computing and doing multiple tasks at the same time with HyperQ and multiple parallelized threads. So it's been phenomenal. And along the way, when I started 6 years ago, we have no business at all in data centers. There were no servers. New category of accelerated visual computing for data centers. It has been a journey. We've had to create the market. We've had to create the ecosystem. And today, I'm going to share with you our plans, our strategies for growth in 2 kind of segments, one being high performance computing or HPC and the other being cloud computing. And if you think about it, Jeff Brown talked about the 3rd leg of this tool, which is the is about a $50,000,000,000 market. And we are poised to address at least a 5,000,000,000 growth opportunity as I'll share with you. So having said that, it all starts with, as Jensens put it eloquently both in his keynote and his introduction, it all starts with a developer. Without a developer, there are no applications. There are no uses for whatever product you're making. There are no customers and therefore there is no data center. And what's fascinating, when I first joined, we had about 400 people or so at our GTC. It was like a science fair on steroids with lots of poster boards and lots of excitement. And today, we have 4,000 or so people at GTC. And the way I kind of think about it and GTC, of course, is a developer conference. The way I think about it is, course is a developer conference. The way I think about it is this is all about making helping people write their master's thesis, helping people defend their PhD thesis and produce awesome research and helping people do postdoctoral work of the kind that Jens a new category, a new way of doing work, okay? And what's interesting is a lot of people have this conception that it's all about CUDA and CUDA happens to be some language. It could not be further from the truth. It's all about a platform. And what we've been doing is making it easier for our developers to capture the to make parallel programming easier. We basically everything we do on the developer side is all about helping our people having and making it easier to PGI, a PGI, a compiler company. And compilers are critical to our future as a developer platform. And we've propagated the standard for accelerated computing called OpenACC where you can take long complex on a single system, on a multiple system, multiple GPU system, on a cluster of GPUs and indeed on multiple racks of processors running in a data center. So the second point there, which is all about development tools, we have great profilers and debuggers and these together with our partners products, companies like Alenia and Vampyr, our partners products have to work not only on a single node, on a single GPU, but also in a single node across multiple nodes on the cluster, on the rack and on the data center. So these things need to scale when you're solving real problems. And then lastly, as you heard, we also make easier for people to program using a huge number of accelerated libraries. In your industry, random number generation, RAM, is a really important mathematical function. We provide an rand called CU RAM. We do linear algebra. We use deep neural networks with CU and fast Fourier transforms and so on and so forth. So the bottom line on all this is a few years ago only a few years ago, we had just a handful of applications that were accelerated that could take advantage of the GPU in the data center. Today, we have over 300 and the rate of adoption is increasing. You can feel it every day. You can see what's room and he expressed how he was working with Apache Spark MLIB and accelerating that. So that's a new instance of GPUs being used in an exciting area. So developers are everything. Developers are embracing the platform. And as a result of which, the first market that we chose to go after was the high performance computing market. This is a $15,000,000,000 server market according to IDC. And our estimate and what do you see is because of the huge number of new applications that are coming on stream and because every time somebody buys a new HPC system, the latest research shows that more than half of these new HPC systems will have accelerators installed in them. So our view is that the opportunity for us is conservatively probably around the region of $3,000,000,000 I'd like to always speak doing market The other thing is The other thing is the practical side of it. So when I sort of go in into a project and I say, okay, if the customer is spending CAD100 am I getting CAD20? And if the answer is, I'm getting CAD20 or more, then I think CAD3 1,000,000,000 is a good target market opportunity for growth for NVIDIA. And in many cases, we get more than 20%. But of course, we don't always have accelerators. So I think $3,000,000,000 is accurate and it's very, very significant. So we're best known for the headline always is the fastest computers are accelerated by NVIDIA Tesla. And so the last year we announced our new partnership with IBM, that was the new news at last year's GTC. This year you see the fruit of that partnership. So the United States government has decided, after a long important evaluation architecture. So these systems will incorporate obviously an IBM power CPU, NVIDIA's future code named Volta GPU. And the most important thing is they will incorporate the high speed interconnect, which is called NV Link. And these systems will be at least 100 petaflops, if not 300 petaflops in terms of performance. And they will come in at an energy level below 10 megawatts. So if you do the math, you're looking at one of the most energy efficient supercomputers in the world, way more energy efficient. And today we always already have NVIDIA Tesla powers the most powerful supercomputers that are energy the most powerful energy efficient super computers and this one will be even more energy efficient than that one. So the key point here is it's a very, very high extreme performance combined with extreme energy efficiency. So but that's just the highest end of the pyramid, so to speak. That's the pinnacle. That's the lighthouse. What's also happened is we've democratized high performance computing. The way I like to think about this is, in 2,009, in my alma mater, which is the Indian Institute of Technology, and I went to the Delhi campus, in all of the Indian Institute of Technology, there were no supercomputers, 0 supercomputers. Today, if you visit India, every single Indian Institute of Technology has a local supercomputer, right? And whether you go to you go to Clemson, you go to Indiana, you go to Purdue, you go to Seattle, you go to San Diego State, etcetera, etcetera, everywhere around the world in every nation, in every university, in every research communities are installing mini supercomputers. A couple of weeks ago, I was with my life sciences team in Washington, D. C. And we did a seminar at the National Institutes of Health. And National Institutes of Health invest about $5,000,000,000 in grants to researchers, both intramural within the campus and outside the campus at various medical colleges and so on. And this lady had called a seminar and she'd invited all the program managers, so about 70 people turned up into this beautiful conference room. And we went around the room and there were so many people said, I'm in cancer research. We 40 ks40s. I'm doing the genomics department and we are just installing 75 ks80s. And so you see my point that around the world, research laboratories, governments are adopting Tesla high performance computing systems increasingly. And what's most interesting is it's not just in government. So even 2 years ago, industry adoption of Tesla was very, very small, handfuls of systems. Today, if I go around the manufacturing industry because of the availability of applications such as ANSYS and Abacus and AM Soft and CST and Agilent, EM Pro and so on. The adoption of Tesla high performance computing by manufacturing industries is increasing. Not all of these companies have big systems right now because they will always follow the method that Jensen talked about. They start with a trial, they then go into a pilot, deployment over 3 to 5 years in your data center. So these are long adoption cycles, but all of these manufacturing companies are using Tesla high performance computing, for example, at the highest level to design and make and simulate their new products. In financial In financial services, the story I like to tell is, it's initially taken on by people who have the most pain. So, the most pain. So one of your colleagues, actually a bank in this room, had so much pain, they had to complete their risk management calculation within 8 hours. And this particular bank was using 4,000 of its servers to do that calculation every day for 8 hours. And because the calculation was becoming more complicated, it was taking more than 8 hours. So they had to put another 400 servers to get it done. And this could just go on and on. And so because this gentleman had so much pain, they decided to replace this estate and use 400 Tesla accelerated servers. So 4,000 servers down to 400 servers, save themselves a lot of money of capital, save themselves a lot of energy for deploying the system and gets his work done on time. So that's kind of the typical adoption of Tesla for high performance computing, both in governments and national institutions and education as well as in industry. And the reason people adopt Tesla is it's not just a graphics card. When I started and we had like 4 servers and I went to one of the other big banks. We had 4 models of servers that supported Tesla and I went there and I sat down with a gentleman and he told me in the most colorful flowery Irish language that my we had no way of running it in a cluster, we had no way of integrating into the data center infrastructure management stacks, we had no way of doing management alerts and diagnosability and reliability and data center manager requires. So the way we now think about it is it's all about the data center platform. We accelerate, we provide facilities for our customers, our server customers to accelerate the performance of their application on the fly. So it's not just the raw performance you talk about, we can do energy limited performance. We have this feature called GPU Boost. So it's not just hardware, but hardware and software that drives system performance. And then these move data or programs across the nodes, across the network. So the way you work with the interconnect on the system becomes extremely sub system subsystem. So people can orchestrate it and manage it and deliver predictable performance to their customers. And then lastly, we fit into all of the infrastructure management. And in infrastructure, you need to think about 2 things. 1 is the actual hardware, the systems and the network and the utilization and the metering and reliability and serviceability, but also the application deployment, the application provisioning, the application management. If you don't need to reboot an application, you can't turn down the whole system. You've got to provision it dynamically, restore it with checkpointing and so on. So we in the 5 years, we have developed Tesla into a platform for accelerated data center computing. And that's the reason why the adoption of Tesla in data centers is increasing. And one other thing, for some customers, we are offering even enterprise support services. So these applications are now so critical. The for the support services so that they are guaranteed a response time, they know when the fix is going to happen, we back support them for multiple generations of software stack and so on. So we're building up a to all of the world's top selling server OEMs supporting Tesla. All of the leading server companies in the U. S. Like HP and Dell and IBM and Cisco, all of the specialist HPC companies that build supercomputers like Cray and Fujitsu and Bull and SGI, all of the major Chinese server companies like Huawei and Lenovo and Inspur and Subon and also all of the major Taiwanese So this is today you can get a Tesla based data center server or rack from just about anybody. And we have like 100 more than 100 server models with more than 350 options on those server models to configure Tesla for your data center rack or server. And we know not only do we support the standard X86 architecture, 100 of these servers, increasingly we Tesla to a Tesla GPU and the applications are accelerated. So today you can buy power based servers with GPUs from IBM and Tian and you can also buy ARM based servers from that incorporate processors from companies like Applied Micro and Cavium and buy them from system vendors like E4 and Cirascale. And my prediction is that the number of providers of non X86 based servers is going to increase quite dramatically over the next few years. And above all, the most the common thing across all of them is the applications are accelerated using the developer tools and the developer platform that I talked about in the second chart. So the thing that's new and really exciting is whenever you look at this, Jenson mentioned Amdahl's Law, you always have to have a balanced system architecture. So one of the key bottlenecks in the bottleneck dramatically with the introduction of NVLink. And now we are reaching out into the market to all of the different server OEMs or various types to increase the adoption of NVLink. So with NVLink, we will now start to be on the server motherboard. So it'll be much, much easier to plug in a Tesla processor like a Pascal, for example, into the server processor. So IBM has already announced power based servers with NVLink and Pascal. So they're actively taking them to their customers. And today Quanta Computer QCT, Quanta Computer Technology announced an NVLink based server, the first ever X86 server available with NVLink. And the reason why Quanta is computing in a moment. So I now want to just transition into why do customers buy Tesla? What's driving the adoption? Why are they buying? And so in high performance computing, the primary reason people buy is to accelerate their scientific discovery. This great example from Nature a couple of years ago is all about taking the HIV virus and actually understanding its detailed supercomputer to get a affected a large parts affected large parts of China and Southeast Asia and allowed drug companies like Glaxo to put out variants of Tamiflu to market faster to prevent the spread of the disease. And this year, the same problem is afflicting India and the local drug companies are using this great science, this great scientific discovery to actually save people's lives, okay? And so that's kind of the overarching reason why people buy and we can call it may not be scientific discovery, it may be technical discovery, it may be design discovery, but at the end of the day, it's helping the scientific and technical community, the creative things faster. And then the economics of the purchase decision are I've summarized them in the following So first of all, because the application works much faster, you need fewer servers, right. And because you need fewer servers, even though each server is a little bit more expensive, you end up spending less money. So your capital outlay, your total cost of acquisition is lower. And the other thing that happens is because these servers are significantly more energy efficient, you end up consuming less energy. And as many of you know, the most the highest cost of operation of a data center is the energy cost. So if you save energy cost dramatically, that's a big, big reduction in the total cost of ownership. And more often than not, you end up doing a lot more work, right? So in this example, and these examples are meant to be illustrative, So basically, the simulation is of molecules and the output is the amount of simulation in nanoseconds nanoseconds per day. So one day of using this computer, the server, the 32 servers, in one day, it will simulate 58 nanoseconds of how the molecule is going to react when reacting with another molecule, okay. So in this example, your cost goes down before Tesla, your costs were 200,000, you bought 32 CPU servers, you spent 21 kilowatts of electricity and you did 58 nanoseconds per day of work. After including incorporating Tesla, you spend 14,000 dollars you buy 1 Tesla K80 server, you consume 1 kilowatt of energy and you do 2 20 nano nanoseconds a day of work. So about 4 times the amount of work. So the whole theme here is it costs you less because your application runs faster, you have fewer servers, you consume less energy and therefore your operating systems operating cost comes down. And more often than not, you end up being doing more work. Now, the amount of more work in many of my customers in supercomputing and in government, the way it actually works is they have a fixed budget, dollars 20,000,000 or $8,000,000 or $5,000,000 whatever. It's a fixed budget and they're trying to optimize their acquisition cost and their operating cost within that fixed budget. So with accelerated computing, they can get a lot more done for the same amount of money. And that's the significant value proposition for Tesla and HPC. So now what's happening with as you see with somebody like Quanta saying, hey, we're doing NVLink for our new servers. What's happening and as Jensen spoke about extensively, is the big thing that's happening for cloud computing customers is deep learning. And it's best exemplified by the Google Brain application. So the Google Brain experiment was actually done over 1,000 servers, about 16,000 cores. It cost about 5,000,000 dollars out of Google's estate and consumed around 600 kilowatts of energy. And in that they did about 1,000,000,000 neurons. And I think each neuron was connected to 1,000 other neurons. Okay. So that gives you an idea of the scale of the problem. And using this deep neural networks, using convolutional neural networks, this is of course, Andrew Ng's famous paper, just one year later, they simulated 11,000,000,000 neurons, okay, 11,000,000,000 neurons using only 16 Tesla servers, 64 Tesla processors and the cost was like $200,000 right. And so you can kind of see the same theme. You save a huge amount of money because you can do it with far fewer servers, consuming far less electricity and you end up more often than not doing more work. So I by the way, in this example, I put it down conservatively at about 6 times 11 times the amount of work or 4 times amount of work. It doesn't matter. The key point is it is more work for less money. And notice once you're down to $200,000 you've started to democratize this as Jensen described in his keynote. So you're making it more accessible to everybody. So what's happening is deep learning, the trend is really, really clear. What's driving deep learning is, first of all, the math exists. Secondly, the applications are now accelerated with the GPU, right? So it is possible to do deep learning using an accelerated application. Secondly, and this is probably the most significant, there's huge amounts of data that are now being made available to people. Everyone knows sort of like how much I think that says around 10 exabytes of data is uploaded every day to the Internet. There's just huge volumes of photos and half of this data by the way is video and YouTube and so on and so forth. What's equally important is retailers have data about your shopping habits in store and online. It's the data volumes are just ginormous. The genome see if everyone on the planet had their genome sequenced and every genome, we did the 26 chromosomes and every one of those chromosomes personalized cures for cancer or genetic issues and so on. So these data sets and the combination of that with this amazing deep learning algorithms that are now accelerated together with the democratized power of the GPU's computing capability, I think is going to drive deep learning very, very significantly. So let me illustrate to you, unfortunately IDC or DataQuest or Gartner or Forrester does not have a market study with statistically significant data to show exactly how big this deep learning market is. But let me give you a sense of what it is. It isn't just about recognizing cats. Some people have the sound bite, oh yes, the Google Brain experiment and what they figured out was the computer recognized that this was a cat. I mean, that's not a significant thing. So let's take, for example, facial recognition. If you've been following the Tsarnaev trial, you'll see that the entire sequence of what happened on that day and how the terrorist bombings were done has been stitched together using multiple data sets. All of them are temporal, meaning time stamped data sets from so many different sources to synthesize so that we have a picture of what happened exactly on that day. Now the implication of that is it's all about public safety. So we need to be able to simulate it. It's not just about anti terrorism or prevention of crime. It's even about tsunami warnings, earthquake simulation, what's actually going to happen if there is an earthquake? How do we make sure that our people, the public are safe? So this image classification, this facial recognition, these algorithms are tremendously important and will be used by lots and lots of defense agencies, police, security forces, municipalities, cities, retailers and so on and so forth. And it isn't just about image. Most of today and a lot of you've seen in the show floors is about image because it's easy to see images. But what's it's as applicable to sound and language as it is to image. So a great example that it's not just about Google and you sort of speak to Google and isn't it amazing that now Google recognizes an Indian English accent? I mean, I just find it so cool. I can actually speak in an Indian English accent and it understands what I'm saying. But what's also interesting is music. So if you have an application like Shazam, for example, it will just a few bars of music, you can play into Shazam and it recognizes not only the artist and the tune, it can even tell you, for example, this is Mahler's 5th Symphony as conducted by Michael Tilson Thomas instead of Herbert von Karajan, for example. It's that good at recognizing and classifying music. And music is a complicated problem. So the top line is kind of all about consumers and regular people and public. The bottom row is much more about the enterprise use of these. So there are a number of companies already on the show floor here at GTC showing medical image interpretation using deep learning. And this is everywhere. The amount the quantity of medical images, whether they're x rays, ultrasounds, MRI, CTs and so on is quite amazing. And I remember 4 years, 9 years ago or something, I accidentally fractured my toes because I was running to pick up my daughter from a basketball volleyball game and I crashed into the door and I fractured 2 of my toes. And I'm sitting in Palo Alto Medical Foundation and this lady who's obviously the podiatrist, you know how to look at the x-ray, she looks and says, oh, yes, your toe is fractured. And after about 5 minutes, I said, I think you mean 2 of my toes are fractured. Because even with her experience, she mistakenly did not see that 2 of my bones were broken. And if this can happen with a simple fracture on X-ray, imagine the complexity of interpreting these images when you're looking for organ patterns and tumors and changes in cell and so on and growth of cells. So this is a complex problem with huge implications. And then lastly, I want to highlight Netflix sort of paper about how they're using deep learning for and Tesla for recommendation engines, the cool demo that you saw. A few weeks ago, I was in China with Ixi Yi, Q I y iXi Yi. And this is the kind of Hulu and Netflix of China. And they are part of Baidu and they make their own programming and Chinese kind of soap operas. And unfortunately in China, people don't pay. So most of their revenue, they have lots of subscribers, but they don't pay a lot of money. So they have to recover a lot of their money from advertising. And so they're looking at deep video for recommendation engines, so that while the show is being streamed, they can have dynamic content being inserted either into the frame of the video based on who So that's a kind of new way of thinking about deep learning for video. And I really hope and I'm very excited to see that they're working in this project and I hope it I'm pretty confident it will. So deep learning is everywhere. I don't know how big this market is. I can sense about 3 weeks ago, we were in Washington, D. C. With my team and we went to all the security services. We showed them the deep learning demo. We sat with them. They have, of course, amazing images, right? You can imagine these amazing images, very, very high resolution sort of images. And deep learning is obviously applicable to them. So we know already that industries are computing. And it's going to be a huge market and we're very well positioned for it. So let me just sort of close off with where we are today. So the first key point is big customers, big companies are adopting deep learning. This is a small sample of large corporations, what I call industry leaders that are using deep learning. IBM is one of the best speech recognition companies and they're using it. United Technologies, they don't make driverless cars, self driving cars, but they do make autonomous vehicles, like drones and planes and equipment defense forces and they're using it for occlusion detection, which will help the autonomous vehicle better recognize and of course, United Technologies also, by the way, owns Otis Elevator. So maybe they're working on improving those driverless elevators that Elon Musk talked about in the keynote today. So you saw the demo of Skype simultaneous translation, Skype, which is owned by Microsoft, and how you can speak in English and hear it in Spanish and somebody else is hearing the same thing in German. That's all you've done using deep learning. So here's the thing, because it's more learning. So here's the thing, because it's more affordable, Wired put out a headline saying, now you can make Google's $5,000,000 artificial brain on the cheap. In other words, for just a few $1,000 you can buy the Titan X and set up a deep learning system. So all of these start it doesn't cost a lot of money to be really, really innovative. And so what's happening is the areas are image and video, voice and language and sound, big data, there's some awesome demonstrations of large data sets. There's a company for example, GIS Federal, I was looking at their demonstration. They've got the Federal Aviation database, the live database of all the plane journey is happening over the United States right now. Now you can now draw a polygon a Now this when it was running on a normal Oracle database, this took 92 hours. And what you'll see in the demo is it takes literally seconds. We can you can see it. You can see exactly where your flight is. You can get all the flight data that's currently available from the FAA right there for you, large data sets and analytics around large data sets. And there's lots of industry specific things going on as well. And then many companies, as Jensen pointed out as well in his keynote, many companies are offering deep learning as a service. So you can other companies, other startups can build deep learning application using their platform as a service. And what's most interesting, especially since you guys are investors and you represent that community, is many of these companies are being acquired by the large corporations. So just the other day, IBM bought a company IBM's Watson Group bought a company. But DeepMind was bought by Google, Cool Iris was bought by Yahoo! Mad Bits bought by Twitter, Wit dotai bought by Facebook and so on. So what happens is the startups through the innovation, they use the our developer platform to develop cool large corporations. And so the upshot of that is, this is large corporations. And so the upshot of that is this is leading to rapid adoption by all of the cloud computing data centers. So it's difficult to know exactly how many data centers Google has or Apple has or Facebook has. And I did a study of trying to figure out exactly how much Google spent last year on data centers. And it turns out to be somewhere between $3,000,000,000 $7,000,000,000 right? And I asked Quanta and Quanta said they sold 1,100,000 servers last year, right? And most of their customers are in data centers. So you get a sense of these cloud computing data centers are very, very large. They consume lots of energy. And the fact that many of the applications they're now running, given the large data sets gives us a tremendous opportunity for rapid adoption in these cloud computing centers. And every single one of the companies listed on this chart are all using a significant amount of Tesla processors processes already. And some of these companies are just sort of hosting companies or infrastructure as a service companies and others are more application companies. And I'm pretty confident that a lot of software as a service companies will also start using the Tesla accelerated computing platform for their cloud computing data centers in the future. So I think I'm exactly it says 0.06. So let's in summary, we created this new category of acceleration by focusing on developers, by helping them speed up their applications. Because of that, we went into the data the computing where we have now already deep penetration in government and significant penetration into industry. And we're seeing this new growth opportunity in deep learning in the cloud computing data centers. So I hope that I've shared with you how and remember, I showed you the value, why people buy. So they buy the application runs faster with fewer servers, consumes less energy, a lower cost of acquisition and because of the lower energy, a lower cost of ownership. And that's the reason that this adoption is increasing and we have positioned for strong growth in the future. Thank you very much. And to introduce my colleague, Rob Shongor, who runs our automotive business. Thank you, Shankar. By the way, I don't think it's 0.06. I think it's negative 6. 6 minutes over. Okay. Hi, guys. I want to I'm Rob Tonger. I'm the GM of the Automotive business. And you guys have heard a lot about automotive. I know you guys track it a lot and we had a lot of conversations last night. I got a lot of questions and a lot of the questions focus on why is NVIDIA in this business? What are the things that are happening? And I guess what I'd like to do in the presentation is walk you through a number of First of all, why are we excited about this business? What are the metrics that we look at to measure our progress in it? Or what are the things that we look at to drive the business for us? What are the things that we look at? What are the problems that we look at that we think we can solve? And then finally, I'll summarize with our product strategies, our business strategies and what the future looks like. So maybe what I'll do is I'll start with the end in mind, okay? I'll start from the perspective of why are we in this business. If you guys look at NVIDIA and you look at the history of NVIDIA, we always focus on markets that are fundamentally have a very, very complex problem to solve, okay? Now if we had intercepted the car market about 20 years ago when NVIDIA was started, I think we would have been trying to solve the problem of how can we accelerate the turn signal or the radio in the car, right? There just wasn't a lot of electronics or computing in a or device to what we refer to as a supercomputer on wheels. And there's a number of things that are driving that. And if you could just start and you know from this morning and you look at what everybody's saying and you look at all the news, without exception, every carmaker in the world is working on a self driving car. Okay? Think about it. A car with a radio to a self driving car. Okay? That's like going from a hot air balloon to landing on the moon. Okay? Now in order to do it, the amount of problems that will need to be solved are going to be enormous. There'll be lots of people involved. There'll be lots of vendors. There'll be lots of suppliers. There'll be lots of partners. There'll be lots of applications. There'll be an enormous amount of things that have to be solved in order to make this happen just like landing on the moon. But at the core of it, you're going to need an enormous amount of technology and it's going to be a major disruption. And at the end of the day, that's where NVIDIA always focuses. We focus on segments of the market where there are visual computing problems car, there's a lot of things to do. You don't just go from the hot air balloon to a self driving car. There's lots of things that have to be developed along the way. There's lots of intermediate steps, right? So what I want to do is I want to walk you through kind of what are the things that we're involved in and then how do we see the business developing, how do we see it evolving and then how do we measure ourselves, okay? First of all, you guys know that we have a very deep engagement in automotive. We've already talked about some of it, but there's one thing that I to point out here. If you look at the image there on the left hand side, that's the M6000 Quadro Rendering Ray tracing. When we talk to automakers, the fact that we are involved in many other industries is a huge advantage. Think about it. If we were only solely focused on automotive by itself, right, there's no way that we would just it would justify creating the investment that's required to build a self driving car. Because NVIDIA is involved in so many different industries from medical research. All of the lessons we learned, how do you cost reduce a computer? How do you create scalable architecture? How do you create one software that can be leveraged from one business to the next? How do you involved in all these different fields, what NVIDIA can do is leverage those experiences, bring them to automotive where what we're doing is essentially building another supercomputer. This one just happens to be on wheels, okay? In this case, what you're seeing is an example of that. When we go to a car company, the architecture that's used to design the car is the same architecture that's used in the car computer, which is the same architecture that's used in doing simulation and the same architecture that is used in the data center. The only difference is that the data center, it's 20,000 cores in a PC, it's 2,000 cores and in a mobile chip, it's 200 cores, right? But the leverage of the same architecture and the software across all those different things means that a carmaker can leverage an enormous amount of investment. And the more these car computers grow in complexity, the more the cost of developing them is. So NVIDIA's fundamental value proposition is always to reduce the overall cost of development. And as a car computer gets more complicated, that's a very important value proposition. Now by the numbers right now, NVIDIA, we're at about 8,000,000 cars on the road, about 25,000,000 more cars coming, right? I picked a few images here just to give you a flavor of what are some of the cars that are coming. So the top image there is an image of the Audi TT. That's kind of a cool car that's coming or I'm sorry, it's a cool car that's on the road right now. What this car does is it takes the infotainment nav, it integrates it into the cluster and you have an integrated cockpit. Audi calls it the virtual cockpit. The image on the left there is interesting also. I think since last time we talked, you guys may know that we announced Honda as a customer. Honda is interesting for a number of reasons. I think some of you asked last night, is NVIDIA only focused on high end? You're only going to be in premium cars, right? Now, car that a lot of people drive, okay? Now in that particular case, a car that a lot of people drive, okay? Now in that particular case, what's interesting also is that this is the world's first Android car. The more complicated the car gets, the more carmakers have to look at the overall cost, right? They have to look at not just what is the cost of one part and one component of a car, But as you know, 80% of the cost of developing in a car computer now is software. So they know that again that NVIDIA has expertise in Android. Android has lots of things built in. You can download it. It's free. You can have over the air capability and you can bring this into a car almost immediately. So this is a time to market strategy and in this case software is the key thing that matters. So these are some interesting dynamics in the market that are driving us that again that NVIDIA has some unique capabilities and as a result, we're able to drive our business. I want to share with you some of the growth metrics that we measure, all right. And I've picked some of the ones that I think are meaningful, okay? And I'll talk about some of the dynamics that are underlying these growth metrics. Of course, revenue, our business is growing very rapidly, of course, due to carmakers engaging us. And you're seeing a proliferation in the models. Now there's really 2 things that are occurring within the business. First of all, carmakers know that if you develop a car computer, it's much better to take that car computer and leverage it. If you can build it once and leverage it into multiple models, then this is a good thing. So what you're seeing in models increasing is really 2 things. You're seeing the proliferation of a base model computer which is going into multiple models and you're seeing new design wins. Okay? So for example, one car computer developed originally on Audi. Now that car computer is inside the Volkswagen Passat or it's inside the Volkswagen Golf. So you're seeing both design wins as well as proliferation of models and both of these things we think are important because again the cost of developing these car computers is going up. It's not enough to have a chip. A chip is just a start. If you look around this conference, this conference is not about hardware. It's about researchers and developers. It's about how do you take this chip and then build applications on top of it. The automotive industry is no different. So one of the metrics that we use to measure our progress is to look at how many people out there are developing, what kind of applications are they developing, computer vision, graphics, capability, all that kind of thing. So if you walk around GTC, you're going to notice that automotive has a much, much bigger footprint in GTC than it ever did. So what we're showing on the bottom left is SDK shipments, the software developer kit for in the automotive business, it's called Jetson. And these are development kits we send out and then we support it just like we do in the gaming business, just like we do in the professional visualization business. Same thing. It's that same strategy that NVIDIA has become excellent at, but we're leveraging that now into the automotive world because once again it's a complicated computer and it requires lots of applications. The last chart, ADAS deep learning engagements. You obviously heard a lot about deep learning this morning. ADAS for those of you not familiar with the term is advanced driver assistance systems. Another way to say that last chart is beyond graphics. If you look at our growth and our revenue, it's all infotainment today. It's all graphics. But a big, big, big part of the future and if you look at self driving cars, it's going to be deep learning and it's going to be computer vision and it's going to be assistant systems and computation. So when you look at the investment that we make into these GPUs and you look at the investment, it's not just graphics. It's about One other thing I would mention as an indicator of what's happening for us within the automotive space is just to walk around GTC, all right? If you walk around GTC 2 years ago, I think we had maybe a few guys. But here at GTC, and I really encourage you to do this, we're going to have 27 automotive presentations. Everybody from Matthias Rudolph, the Head of ZFAST at Audi is going to be talking about self driving cars, what they're doing with NVIDIA and then the next early the head of ZFAS at Audi is going to be talking about self driving cars, what they're doing with NVIDIA and then the next early trends for deep learning. Electrobit is going to be talking about how to design next generation human machine interfaces. How do you extract the power that's within a processor, a GPU and create information that's functional and is also visually delightful? How do you measure human machine interface? Right? These are all things that we're going to be talking here. Daimler, BMW, Audi, Tesla, Hyundai, Daimler, BMW, Audi, Tesla, Hyundai, just all over the map from all over the world. 8 automotive Tier 1 suppliers. You guys know that in the automotive space, there's an entire ecosystem, massive companies. And when NVIDIA delivers solutions to the market, we partner with Tier 1s. Tier 1s have been working in the automotive world for So these are people like Bosch, Conti, Denso, Delphi, they're all here and they're presenting. Software companies, press and analysts, and if you want to take a spin in a car or take a look at a car, there's I think 20 cars here that are showing various innovations in GPUs. So it's I think just looking around you here at GTC will probably give you a sense of what's happening and have flavored beyond slides of really what's happening within NVIDIA's automotive business. And at the end of the day, again, what's driving all of this is that the car is becoming a supercomputer on wheels. Now we've talked about that. I've said it before, but what does it really mean? Essentially, there's several trends within the automotive market that are driving the need for more processing. 1st, the proliferation of displays, more and more pixels. We talked about this at CES and what I want to do here is share with you some empirical examples of what's really happening out there so that it's more concrete. Secondly, more cameras, more sensor devices. All of the inputs from these sensor devices and cameras have to be processed. And it's pretty complicated and the bar is rising continuously. Without exception, I mentioned every carmaker is trying to solve the self driving car problem, without exception. All of these computers need a number of things. They need parallel processing. They need extensive software. And it's software architecture. It's not just one piece of software. There's system software, middleware, application software, lots of things. And finally, it needs deep learning technology. And the reason why is that you simply You need something that works in parallel with the existing solutions that you've implemented and we'll talk about that. Now the question is, what's the TAM, right? A supercomputer on wheels. Well, from our perspective, the answer is within the next 10 to 15 years every car will have 1. 20 years ago when NVIDIA was started, people said what's the TAM for 3 d graphics? And the answer was of course 0. And then I said, well, how many computers eventually will have it? And people would say, well, I don't know, maybe 1% or and the answer now 20 years later is 100. 100 percent of all computers have 3 d graphics, right? It doesn't mean that NVIDIA is in all of them, but in the areas where visual computing matters and 3 d graphics is pushed by segments of the market that need it, that's where NVIDIA is going to play. But this fundamental trend is required in order to push us. Okay. So let me talk about some of the market trends. By the way, this is a real this is a postcard I a crappy NAV system. A crappy NAV system, only not in those words. So but what it really highlights is this, okay? Before you get to a self driving car, This was perfect. And by the way, I saw this article and then I went into the message boards and I was reading the comments and then I actually I extracted this one message because I thought it was so perfect, right? This is why everyone hates in car navigation systems. Seriously, why don't they just integrate CarPlay or Android Auto and stop charging $1,000 extra for a NAV system for new cars? This is 2015 not 2,005. Seriously, I mean how many of you have a car nav in your car that looks like it's from 2,005? There's Normally, nobody would raise their hand because there's an investor comp. Everyone's very serious. But it's true. What consumers expect is what they see on their smartphone or what they see on their tablet. And because it takes 10 years to get something out to market, the end result is consumers don't want to buy a car now. And by the way, where do carmakers make money? Accessories. Selling them, selling the nav, selling your premium sound system, that's where they make their money. Now how are you going to get it from here to there? Okay. Now where you see NVIDIA engaging, right, again is these are lessons learned from other industries. So what NVIDIA is good at is taking those lessons somebody like Audi and accelerate that time to market, okay? Now this is a funnel if you're looking at all the different steps in all the areas where NVIDIA is involved in, this is the first one, okay? Now, what's the validation of what are some of the proof points about the need for more pixels and more displays and all that kind of stuff? Well, the carmakers are not oblivious of this. We did a very nice rendering for CES that kind of showed conceptually a car that would have lots of displays. Well, the carmakers just went ahead and they're doing it themselves. If you look at the Daimler keynote at CES, they had a card. It just has pixels everywhere. It's just displays everywhere. Audi had the same thing. The Audi Prologue, I think there were like 7 displays with all sorts of information that can flow back and forth between them. But it's not just concept cars, right? This is the new Audi Q7. This has 4 NVIDIA Tegras. You've got the cluster, that virtual cockpit, you have the infotainment head unit. In this case, you have 2 rear seat tablets or rear seat entertainment. They're detachable tablets. You can sit in the back seat. You can load your music, take your music, load it onto the car's sound system, play with environmental controls, all of this kind of thing. So this is not so this is not just concept cars. Now if you're going to develop a car computer, you have to be able to drive the pixels, but it also has to be scalable, right? You can't have a car computer that just costs a bazillion dollars. We're not going to be able to drive the business by doing that. So what we did is we announced a product at CES called Drive CX. And Drive CX is a digital cockpit computer and we showcased it using a Tegra X1. Now what the Drive CX and this is our strategy, the Drive CX, it is a complete car computer end to end, all of the components that you need. And we use this. We show this to customers, we show it to Tier 1s and with this they can deliver a scalable solution. So let me describe or let me tell you what that means. A scalable solution means that maybe you have an entry level SKU of a car where you only have one display, but you want to have the same design and use it for subsequent cars. So you need to be able to add capability to it. The base level configuration of a DRIVE CX would have for example one display and then you can optionally add more displays in this case up to 3. You can power 16,000,000 pixels with this at the high end. You can optionally add cameras to this also. And I'll show you what can do with that in a cockpit. But the end result is that you have a lot more performance. And then as a result, you can also create visuals that consumers expect and that consumers would be delighted with. But again, the hardware is not enough. A DRIVE CX is not enough by itself. The DRIVE CX, the hardware, the Tegra X1 at the core, one of the things that we are doing is to work with computer that they and the effort that they put into craftsmanship in the rest of the car. Craftsmanship is a word that's used a lot at automakers. It's not enough to just have a car that's functional. They put a lot of effort into craftsmanship. Now the UI lags the car in craftsmanship and what we do are things like this. Now in addition to the chip and the hardware, what NVIDIA is doing is providing software services, tools so that we can help the carmaker extract the performance of our processor and then to deliver something that is functional and beautiful. Doing this is very hard. Car makers know all about cars. They know all about how to do instrumentation. They know about what information they need to present to the driver. We know all about graphics and 3 d graphics. So this is an actual if you went to the demo area and if you go there, you can see this in action and you can see the tools and the software that we're providing. And this is also by the way I think a reflection of the evolution of our business model. I think the evolution of the business model and you know that in other businesses we start and a lot of people view us as a chip company. And at the end of the day after they've worked with us for a while, they think of us more as a software company. I think the automotive business is no different. The end result of this is that you can deliver something like this instead of something like that. Okay. The image on the left, I think we're all familiar with. The image on the right is actual real navigation application that's running on a Drive CX Integra X1. There's no reason for a consumer and I think the end result of this is that you want to develop something that the consumer is going to love, not that they hate, right? If you have something like this, you're not doing something like you're not developing 3 d just for the sake of having 3 d. In this case, visual cues of buildings, cities, intersections are important visual cues that give a driver much more immediate understanding of where they are. In this case, we use lighting in 3 d graphics to reduce the clutter on the outside edges of a map and to focus the driver's attention on the center, right? There's lots of subtleties and lots of things that you can do here. And again, you just have to have the capability. But in addition to graphics, My bad. All right. The other thing that we demonstrated and you can see it here at GTC is we have because of the Tegra X1 has so much capability, you guys know that when we launched Tegra X1, we doubled the graphics performance. Many of you have seen TopView or SurroundView in a car or maybe you have it? Like you're parking and it looks like there's a camera floating, right? I don't know if you've seen it, but the quality of these top views is actually pretty lousy, right? And the reason is what you're doing is you're stitching the video from 4 separate cameras, left, right, front, forward front and the rear. And then you have to stitch them together really well. And then there's the ground might be uneven, so you get lots of distortion. What we are developing is a best in class surround view. We use the GPU to do lots value of But in addition to the value of the image processing that we're doing, because we can process this in the Tegra X1, it means that you no longer have to have an external box. Today, surround you is an external computer in a car. By processing it in a Tegra X1, you now can save $200 to the automaker. That's an enormous cost saving, okay? So this is an example of reducing overall system costs that we can do by providing better processing. By the way, there's one other interesting dynamic of this. Because you have distributed boxes today in a car, a lot of them can be hacked. If you go to China, a lot of these external ECUs, Chinese, people just put an emulator on the CAN bus and then they can just basically hack it and put something else in there. So I think there's advantages and you'll see more carmakers move would be of course ideal for that. And then, would be of course ideal for that. Beyond graphics and beyond the cockpit computer, at CES we announced a second computer called NVIDIA DRIVE PX. And NVIDIA DRIVE PX is basically 2 TEGRA X1s. It has CUDA programmability. We demonstrated the ability for doing more advanced computer vision algorithms. And then of course as we talked about a lot today, it is the platform for deep learning for a self driving car. Now the first thing that a DRIVE PX does is it allows you to connect up to 12 cameras. Now you might be thinking 12 cameras is crazy. Who in their right mind needs 12 cameras in a car? It's actually not that crazy at all. Today's car has 2 forward cameras, 4 surround cameras, 2 cross traffic cameras. You have a camera internally that monitors the driver. You have you can replace the side view mirrors, reduces the drag of the car by 15%, reduces the CO2 footprint of the car. All of these are things that are being worked on right now within the car industry. If you're driving fast on the Autobahn, you want to be able to pick up objects as fast as possible. Now today, carmakers can use things like radar. They can use ultrasonics. And you can pick up radar, but you can't tell what it is. So ideally, what you would be doing is using higher resolution cameras. And the reason you can do that is because the cost of cameras are coming down at Moore's Law. They're coming down in cost every day and much faster than radar. So with a Drive PX, you can hook up to 12 2 megapixel cameras capture at over 50 frames per second and still not overtax it. So this is 1.3 gigapixels per second of processing and that's what you would use it for. You would use it for being able to connect high resolution cameras and then be able to process them and capture very quickly. CUDA programmability, what could you do with CUDA programmability? Again, because we leverage and because CUDA is if you go online and just do a Google search on rain removal, there are so many different rain removal algorithms that you can use. And what we can do is process, clean up computer images, feed it to cameras and allow detection, allow safer driving in the car. At CES, we demonstrated a surround vision solution where we can do an auto valet application, right? You can reconstruct a garage and have a car park itself in a garage that it's never been in before, only using cameras, right? And then finally, the deep neural network computer vision, which we talked about a lot this morning, right? And I think one of the things that's pretty important is if you look at the model and what is NVIDIA's strategy, our strategy with Drive PX is to deliver a solution that is a complement to work and investment and solutions that already exist. So that's really what the solutions out there right now and I got asked about it a lot last night. How do you complement other computer solutions or things that are out there? Think about it, when NVIDIA went into the PC market, when we went into the PC, we didn't go into the PC and redesign everything in the PC. There was an X86 CPU for example that was doing just fine. We didn't have to redesign an X86 CPU. People are invested in the X80 CPU and it does its job just fine. So what we do is we complement the X86 CPU. I think it's no different in the car. Complement the X86 CPU. I think it's no different in the car. There are some computer vision solutions that are just at detecting objects, but it's not enough. For those corner cases, for example, like as we showed this morning and Jensen talked about in the keynote, when you're doing free space calculation and a bus comes or there's a car door that opens or there's a pothole or there's something for those types of situations, you need some help. And so the complementary solution of a deep neural network working in combination with an existing computer vision solution, we think is the ideal platform beginning now. For all of the things that people are working on the intermediate steps that you work on to get to a self driving car, things like dynamic cruising, being able to change lanes, auto valet applications, all of these types of applications are steps and then ultimately you get to the self driving car. And at the same time, you want the deep neural net to be learning like the baby hitting the ping pong ball, right? It's a behavioral thing. You didn't teach the baby Newtonian physics. You just told the baby to hit the ball, right? So, I believe that ultimately the task and what we talked about this morning, the task of building that self driving car requires a complementary approach to the conventional solution because the conventional solution it's going to be impossible to get there, all right, without something else. Now, the thing that I think everybody was waiting for and which I think was addressed today was when can I get it? When can I develop on it? When can I get started? And the answer is today or in this case May. But the end result of this for developers and researchers and this is what I think everyone is excited about is that you now have that platform, right? You can begin working now. You can begin working today. The Digits dev box contains the hardware and it contains the Digits software framework that Jensen talked about this morning, all right? And if you think about it and I know I got some questions on this last night and let me just talk mention it briefly in the context of a car. Picture that the box on the left is in a data center of a carmaker and that's where you're doing the learning. That's where you're doing the training. What gets downloaded to the car goes into a DRIVE PX, right? The deep neural net model gets downloaded to the car. This is terabytes of data and this is tens of megabytes of data, all right? And then when the car learns, if it finds something that it's not familiar with, it uploads it back to the data center. It learns and then downloads a new deep neural net model to the car. And then not only does that car get smarter, but every car gets smarter. That's how this works. This platform, this development platform is just a micro version of that future. The learning platform and then the runtime platform in the car. Okay. I'm almost out of time. What I've done is I've tried to walk you through every step of what I think are the important developments, the important trends, the important strategies that matter to us, that matter to the automotive market and that are ultimately driving our business. I think at the end of the day, I think to summarize our growth metrics and our growth drivers, we're currently at $200,000,000 roughly $200,000,000 business growing 85 percent, 8,000,000 cars on the road, 25,000,000 more coming. We talked about the car being a supercomputer on wheels. I think hopefully, I gave you the context, the depth beyond that statement so that you understand what's driving it. I think for us, the cockpit infotainment business, the proliferation of displays is a growing business for us. And then ADAS, assisted driving is an expansion beyond that. And then I think ultimately, as I started, what's driving this ultimately is the moonshot goal that without exception every carmaker is trying to drive the self driving problem. And I think what this is going to do is going to define lots of intermediate solutions. Before you get to a soft driving car, you will have cars that will help you. They'll be semi autonomous. Help you park. They'll help you cruise. And those will be things that will drive our business in the short term until you get to a self driving car. Okay? Thank you very much. Good afternoon. I am your last speaker for today, and I'm going to finish up and talk about the financials. I thank you all for being with us today, seeing through the, GTC keynotes, and then spending the day hearing from Jensen and all the different business units on what we have cooking up for us in the future. I will take this opportunity to kind of recap some of the things that you saw in the individual pieces and how this kind of puts together from an overall financial perspective. I have 4 goals to deliver today. 1, focus on our transformation. Where are we? What type of progress have we seen? What do we measure to look at our transformation in terms of where we are? 2, our overall profitability. Drivers are our profitability. What have we seen in this last year and help you in terms of key metrics across there. Shareholder value and our capital return program, focusing you on how important our capital return program is, what we look for in the future and how you can help think about how we'll deliver that. And then number 4, how to model and think about our business as we move forward, okay? Those are our 4 things I hope to accomplish. With that, let's see if we can get started. Okay. Let's start with a recap of fiscal year 'fifteen at a really high level. First, overall revenue. Revenue finished at fiscal year '15, dollars 4,700,000,000 13% growth, more than $550,000,000 increase over fiscal year We'll talk about that a little bit further in the next couple of slides. Secondly, record gross margin levels as well. About a year ago, I stood on this stage as well and talked about, again, a record gross margin. We beat that record gross margin again this year. We also had a long discussion in terms of what would be those drivers that you could potentially possibly see any more gross margin. And we did that and hit and growing more than 70 basis points over prior year. 3rd, profitability. Pick your favorite metric, operating income, net income. I chose EPS. Overall, EPS growth more than 53% increase from the prior year. Large of this is driven in terms of the revenue you saw on the top line, expanding our gross margins faster than our overall revenue but thirdly, our continued effort to look in terms of our investments and make sure that we are leveraging every last dollar that hits us in the overall OpEx to drive our operating income. So by any standards, looking at our results since fiscal year 2015, an exceptional year, and we're very, very pleased with the overall results. So before I go to the next slide, I thought this would be a great opportunity for us to talk about where we are in our transformation. What I mean from our transformation, you heard from Jensen really talking about why move from a commodity type of business? How do we focus on the individual platforms as we go forward and why that's important. 1, our platform vision allows us to get closer to our customers. The more tied in you are to the customers, the more that you can help them, the more that you can provide to them. Number 2, it provides us that overall diversification of our overall business as we focus on the computing platforms of the future, data center, cloud and mobility. And you can see that by taking what we've learned in our PC overall business, leveraging that strength and applying it to all those different businesses. The 3rd piece is value. The more that you can provide in an overall platform, the more value that you can provide, the better the overall business model that we have. And you can see that right now in terms of the results that we're providing. So I get asked quite a bit, and even 3 or 5 years ago, probably the most popular question that we had from this room and this group was how are your attach rates doing? How do you think about the overall PC market in general? Is it going to grow? How's your attach rate going to do? You remember, those are the questions you all asked. Even about 1.5 years ago when I first started, that was the most popular question that we've had. What this chart displays, the gray bars represent the worldwide PC unit shipments growth over the last 3 years. That's below the 0 mark key platforms that we've been focused on. Our green the key platforms that we've been focused on. Our green bars. Our green bars represent one of our most important PC platforms, PC market declined. Over that period of 3, 4 years CAGR, 17% growth in our overall gaming platforms for the PC and in this last year, 34% growth, an exceptional performance even in a market where the overall PC units have now been growing. Secondly, where are we in transformation when we look at our business mix? In this chart, I took an opportunity to look back just 2 years ago and where we stood in fiscal year 2013 when you thought about our business mix. Our business mix, I'll call it fifty-fifty. 50% of our business focused on OEMs, focused on GPU, Integra to the OEMs and our overall IP royalties. These platforms. Now let's put us where we are at the end of fiscal year 'fifteen in Q4, a very different story. It's actually eightytwenty now. In less than 2 years move to eightytwenty, 80% of our business stemming computing, auto, doubling their size in terms of our overall company mix. You see strong growth. You've seen our gaming transformation and looking at the overall revenue growth in totality. Our white bars indicate But when we actually just extrapolate our 4, But when we actually just extrapolate our 4 key platforms, gaming, enterprise, high performance computing and saw it. So when people ask me, what inning are you in, in terms of your transformation? It's a hard analogy for me. My sons play basketball, but I get the point of the question. I think we're quite far. I think you're seeing really the results of a lot of the investment, a lot of the work for us to diversify into these overall platforms. Here is kind of a summary of revenue. Up on the top, focused on our key segments. These segments are still important. These are the form factors of the platforms that we deliver. We'll still continue to look at our GPU segment and also our Tegra segment. They're both extremely important for us. But down on the bottom, what you had seen from individually the presentations today is how we did in those growth platforms. And what you'll see here is we've received now a gaming business that's more than $2,000,000,000 in total. Our high performance computing, you heard us talk about it, it's in the 100 of 1,000,000 of dollars. Here, we have it at $279,000,000 to help you in a little bit more. We've talked about our automobile business growing to almost a $200,000,000 and growing quite exceptionally, over 80%. So I hope this provides a little bit more clarity, a little bit more understanding in terms of our success, how we have taken these platforms and grown them into reasonable and reach us to this high expansion? So here, reach us to this high expansion. So here, the center, white, is our overall growth platforms, each one of them higher than the company average. That's what's driving our overall growth platforms. Even the ability that they're driving in expansion in terms of our gross margin. So we're quite fortunate. But again, it's based on really that gaming, it can be around the company average. You can see that just because it is about 50% of our overall business. But again, there's a lot of different pieces of that. And we provide value at all different price points and levels driving our overall gross margin. Our enterprise businesses are even faster and even higher gross margins than our gaming. And then as you can see, our IP is some of the strongest. Yes, to the other side of the white bar, our gross margins that are not at the company average. That's okay. Volume, a lot of it, can add in terms of overall profitability in total. And those are still extremely strategic focus for us as well. Operating margin expansion. Talked about EPS in the first couple of slides. When you look at our overall operating income performance as well, hit about $954,000,000 for the fiscal year, up over 40% from the prior year and the highest level over the last 4 We expanded our overall margin on operating income by 400 basis points. Where was that focus? That focus was really on our investments. That wasn't a statement that we did not invest. Of course, we invested. Of course, we continued to make the right moves that we needed to capture those growth markets that you had seen. Expansion in terms of our sales capacity, expansion in terms of our go to markets for such important parts of those markets, but continuously looking for efficiencies across the group. Engineering, a great opportunity using the unified architecture across the Maxwell line that is consistent to continue to find ways to find savings in terms of what we deliver. And we executed that and really, really drove our overall operating income. I get questioned a lot. Are we done? Is that the end? No. We'll continue to make the investments that we to going forward. But again, we're just going to be focused on the overall profitability of the company. And we'll talk about that a little bit later. Generating cash flow. Important metric for us. As you know, this fuels our overall capital return program. Quite a good year in terms of our overall free cash flow levels at the end of fiscal year 'fifteen at $783,000 driving more than 35% growth from the prior year. A lot of focus, again, in terms of the exact type and really trying to manage that efficiently across the group. Over the last 4 years, our overall free cash flows have averaged about $700,000,000 a year, which again, its main use is thinking about our overall capital return program. Our cash balance. I get a lot of questions in terms of how is the cash levels, what is your focus in terms of that. 2 years ago, our overall cash balance in total the U. S. We executed a convertible debt shortly after that. Why? To overall leverage better use of our international cash, so we could fund our capital return program. We're now where we sit. Our overall cash balance is still at about $4,600,000 Our net cash is at about 3 point $2,000,000 And we have about $1,700,000,000 in U. S. Cash. Our uses for that is definitely our capital return program as well as our operations that are headquartered in the U. S. We'll talk a little bit further, okay? Our capital return philosophy and what are is our goal? Why do we have it? Why did we put it in place? Again, it's a key component of our overall shareholder value delivery, and we're going to make sure it's a long standing program for the overall company. The first thing we appropriate thought through the investments that we need to think about for the future. Additionally, solidifying a competitive yield, a competitive yield against our peer set, competitive you would like to see in terms of returns of a dividend. This is a long term program, our dividend, so we need to be very cautious and very thoughtful in terms of our level of dividend. Additionally, we use share repurchases to enhance our overall shareholder value and capital return program is the last piece. But the number one goal in terms of how we look at this is trying to return the largest percentage of free cash flow that we can associated with where our cash flows come in and our ability to leverage our U. S. Cash flow for return. Let's see how we've actually done. We restarted our capital return program in 2013. That was the start of the dividend at that time, and we also repurchased stock to have about $150,000,000 return of capital. In fiscal year 'fourteen and 'fifteen, more than $1,000,000,000 returned in capital to shareholders over that period of time. The white line refers to our actual shares outstanding, more than a 12% decrease in shares outstanding over that period. And then for fiscal 'sixteen, we've already talked about our intent to return $600,000,000 for that year as well. Not including fiscal year 'sixteen, but since the beginning of our program since fiscal year 2,005, we've returned $3,700,000,000 or approximately 70% of our free cash flow through our capital return program. It's here to stay. We'll continue to focus and provide much information as we can in terms of what you can expect for there. So in summary, when we think about our commitment to shareholder value, I think our number one focus is growth on the top line. Growth in terms of our platform vision or growth in terms of expanding to the adjacent markets outside of just the PC platform that you've seen in the past and moving to the cloud, the mobile and the data center is important parts of our future. Our excellent in operations. Excellence in operations stems in a lot of different pieces, everything from our operations on how we manufacture our products, how we think of the overall overhead to that as well as how we think about our overall engineering processes, how we go to market and the sales. You have our commitment to continue to focus on that, on making sure we're making the appropriate investments to grow these platforms as we go forward. Our overall operating margins grew over 400 basis points. We're very pleased with where they sit right now at 20. And we had guided again for the Q1 along that same lines. Shareholder Again, our historical average is a pretty good indication of what you could probably see forward. It might change quarter to quarter. It might change year to year. But overall, we will try the highest amount of free cash flow that we can to return to investors. Modeling our business. When you come up with your growth platforms and you come up with your overall strategy vision, they're generally going to be multiyear in nature. So when we think about our transformation to our visual computing platforms, you can look at that as a multiyear effort. We've talked about a lot of the expansion of the TAMs through each of the business units' presentations and how important that is going to be for the growth as we go forward. The gaming market, extremely healthy. Our continued expansion in high performance computing moving into deep learning and our enabling of the partners as we think about virtualization and with the virtualized GPU. All of these are great drivers of our overall future growth, and we'll see if that and we'll definitely see that continue. 2, profitability. It's a balance. As you move into an overall platform type of strategy, you have the opportunity to expand your overall business models into unique opportunities. Now you're leveraging the overall software skills of the engineers to deliver the additional value on the top line. So what you're going to see is different business models all the way across our overall businesses, each of them delivering a different level of profitability. But profitability growth is definitely our focus. Shareholder returns. We've talked about it. They'll be here for a long term. We're committed to our intention to return $600,000,000 this year, and our dividend is definitely a long term. So we'll continue to focus on that. So with that, I'm going to bring back up our business unit leaders in Jensen, and we're going to open up for Q and A for the Thanks. Come on, you guys. And please, please, we'll have some microphones come across. So before you answer your question ask your question, excuse me, let's make sure we get a microphone everyone can hear. Thank you. Thank you. Another way to maybe a lens to look at your financial software development and the business in general. As you talked about having many of your growth platforms now with higher than average corporate growth margins and that's a point of launching. How do you think about the ability to invest? Do you think investing will as a percentage of revenues, may stay the same or can it scale? Basically, it's another quest way to ask, what is your potential in terms of operating margins over the next 5 years? Would you like me to answer? For me? Either way. We should there's a couple of ways to think about growth. First of all, the TAM of the markets that we're engaging are very large now. There are also new markets. The concept that high performance computing is going to be very important in the cloud is I think a foregone conclusion at this point. I mean, we're just feeling it all around us. The fact that we could put accelerated GPU accelerated graphics in the cloud is you're feeling it. It's us. It's all of our partners that are engaged in this platform. It's all of our OEMs that are part of the Grid platform. You could just feel how vibrant that opportunity is. We're solving real problems. The market opportunity is big. So the first thing about growth is that you have to create something that engages you into a large TAM. However, that large TAM today, you have to think of it in terms of a SAM, because some of that TAM requires additional market cultivation to happen. For example, Shanker talked about the fact that you have to enter in first of all, you have to convince somebody that your technology is possible to solve their problem. Then of course they will do trials and then go into a pilot and they go get budget approved and then before you know it they deploy. That process in certain enterprises could be 9 months. That process could be a year and a half in some large companies. And so we have to be thoughtful about how long that would take. Now we could during that time, we could put all the money into it that we want, it won't make a difference because it just takes time for people to go through their own process. And so each one of these verticals, whether it's accelerated cloud, whether it's high performance computing, whether it's automotive, which is a multiyear design cycle, whether it's so on and so forth, you have to be thoughtful about how quickly it will grow irrespective of your investment. And so we have to find that measure. And it basically goes like we've created a TAM and we can imagine a very large opportunity and we can feel it, we can feel the buzz of it. And then we have to be measured about the rate of investment because sometimes just takes time, okay, if that's helpful to you. And so we just got to find that balance. Yes. Can you help me understand? If I could there was something I was going to say just one second. There was something else. But in no time in the history of our company in no time in the history of our company have we ever engaged a TAM, an opportunity that is many, many times bigger than the size of our company today. You guys do the analysis of your own TAM, just kind of rough ball it and you'll come to the conclusion whether it's our estimate, estimates of our partners or the estimates of the people that are in this conference. It is a multi $1,000,000,000 TANDA. It's many times the size of our company. And that's what's really exciting to all of us. And so I think the growth opportunity surely there. We just have to be measured about how quickly to invest and add fuel to it so that we can, as Colette said earlier, continue to deliver increase in profitability. Okay. Go ahead. Yes. I have a question about maybe particularly on the auto side. How are you different than Mobileye? I mean, there's a dramatic difference in valuation. When I listen to your presentation, it doesn't sound that different. What's the difference? We do actually very different things. We do very different things. There are computer vision chip that's connected to a camera and it detects things. It detects things. For example, when I walked into this room and this is an extremely unfair explanation, but I'm going to do it anyways because it's easy to understand. When you first walked into this room, this room is a smart room. If the lights were off and you're the 1st person to walk into this room, it detected us and there was a motion sensor and thunk the lights turn on. In the case of today's ADAS, there are several ways to basically perform that same functionality. You could do it using sonar for things that are very close to you. You could do things you could use a radar. Radars in fact, most of the cars that have ADAS today are radar based. And you can also increasingly add cameras that allows you to also perform some of those functionality. It detects a car in front of you apply the brakes. It detects that, you're close to the lanes of the roads. You could translate that to a subtle buzz in the steering wheel. That's what ADAS is. That's a radical difference to a self driving car. A self driving car needs to understand what you would do in a very large number of very complicated conditions. If you had to describe, for example, what is the algorithm around for a very long time. If you had to describe how a self driving car works, what is the unifying theory of self driving cars? In fact, there isn't one. There's no unifying theory for playing tennis. There's no unifying theory for golfing. There's no unifying theory for driving, there's it's a behavior that is learned. It's a learned behavior. And that's one of the reasons why we believe the answer is to use a processor like what NVIDIA builds that has the ability to run a deep neural network as I was describing today that can learn the behavior, that can learn the behavior. Just as scientists have have taught these deep neural nets how to learn the behavior of inferencing, understanding, recognizing an image, the behavior of recognizing a photograph and that same basic technology to infer what is the right, what is the best driving action to take. Okay. So it's just a radically different thing. They're computer vision detector. We are a platform for software. Now our platform for software has several applications. The application that is driving our growth at the moment is digital clusters and infotainment, beautiful graphics. What Rob was saying earlier, more and more cars are going to have richer and richer graphics because there's more and more pixels. There will be no cars in the future that should have dials and knobs and that kind of craziness. Okay. I don't even know how to drive a car with dials and knobs and you know it's just crazy. You just want to have a nice computer, nice and elegant and why wouldn't you want it to be more beautiful than your Android device 3 years from now? And so it's got to be pretty rich. So graphics, computer being the basic computer, that's the anchor point, if you will. That's the starting point of what Rob does. And then on top of that, there is a self driving autonomous vehicle platform that infotainment, digital clusters, a supercomputer, infotainment, digital clusters, a supercomputer if you will for cars and then it's augmented with self driving technology. Okay. So we're in fact very, very different. Now of course some people find joy in battles. It's like it's just more fun to watch people battle. We're not battling with them. Every platform that we're in for self driving they're in too and every one they're in, we're in. I mean, there's no sense of the battle. We all just work together and build these self driving cars together. Can you talk a little bit about the PC Gaming business in the context of Intel's preannouncement? You cited a bunch of negative factors for PCs. You guys have done a great job of explaining how you've decoupled from that and your growth is separate from that. But are you completely decoupled from it or any of the currency factors or things like that that Intel cited things that we need to be thinking about? Let's see. I think they cited largely enterprise PC declines. And we are in 0% of enterprise PCs. We've been out of that market now for, I think like 18 years. And the reason why is because most of you just would rather have a thinner laptop that and run office. And largely, I think that that's been solved. We're largely decoupled from that. I appreciate that though. Do you guys want to add something else? I got it here. Jensen, you had mentioned that you want to be to movies. And is that something NVIDIA wants to run? Or do you want to hand it off to another company to actually use that? That's really a good question. In fact, that's a fantastic question. I thought about that for a very long time. And it came down to this. I think we ought to just do it ourselves. And the reason for that is because there's nobody else with the street cred to do it. First of all, you have to build the basic foundational technology. And if we didn't do it as the world's largest visual computing company and the company with so much might in this area, if we didn't build it, it would have never been done. And so one, we had to go build it. Well, then the next question goes, how do we get those platforms, those processors and the software stack into data centers? Well, it turns out data centers all over the world now in Amazon have these NVIDIA processors in them. And the only way the only reason why Amazon has it in them isn't because I told them to put it in there. They bought a bunch of GPUs themselves because it's used for cloud graphics for SaaS, it's used for high performance computing, all these researchers around the world who are doing research, it's being used for machine deep learning companies who are trying to start these platform companies for deep learning and they want to host it from Amazon. There's so many different applications that have aggregated demand to these cloud cloud service providers. And as a result, those GPUs are there. And then of course, we have the additional demand of grid and cloud gaming. Now, if you were to rely on somebody else to do this, who would do it? Who would do it? Number 1, who has the street cred to do it? Who has the capacity to do it in terms of having these supercomputing resources all over AWS? And who has the ability to go work with game developers to adapt if you will all of those games so that they're perfect for streaming from the cloud and then just on and on and on and on. By the time that we're done, really we just had to do it ourselves. And of course, the last part of it is such a fantastic opportunity. We've invented all the pieces, we have all the skills, Fish's group, we know the game developers so well. It's just such a fantastic opportunity. Why let somebody else do it? And so that's kind of how I reason through it. We're we're not a we're not a chip company whose destiny is just to sell to OEMs. If I start from first principles and ask myself, you know, what is the best way, most efficient way, what is the fastest way, what's the best way to deliver this value to a customer, I think the answer is I had to just do it myself. Okay. So that's kind of where we came down. Now here's the other question, the flip side question. Now that you're doing it yourself and that you've decided to do it yourself, are you would you be open to providing the grid platform for other people to build it? The answer is just about everybody who's even thinking about cloud gaming is using NVIDIA's platform. Okay. The answer is yes. If somebody else can do it better than I can, fantastic. The most important thing is we just love for this Netflix of games to happen. But if it doesn't happen, we'll just do it ourselves. Okay? Your OEM business, if I take out IP revenue, I think OEM revenue declined 19%. My question is at what rate do you see it declining faster at the same rate? And is all of this revenue going away? Or some part of it is something you could stabilize and keep it? Or all of it is going away? We're winning OEMs. We've won big OEMs. We've lost some OEMs. And the number of PCs mainstream PCs with discrete GPUs is declining. So it's a combination of all that stuff. So there's no guarantee that it's going to decline. There's no guarantee that it's going to go up. The only guarantee that I tell you is, it will become increasingly less important to our business. Okay. And so I think that the OEM I've not told the OEM team, you're allowed not to win anymore. I mean, I don't know. I just maybe it's just my nature. It just depends on which of our managers you put in front of me. The work that I'm talking to that manager about is the most important thing in the world at that moment. And I'll continue to that. We ought to go win everything we can win. We ought to go win everything we can win. And there's no harm in the OEM business. It's just that we can't rely on it. We just can't rely on it. We had to go build valuable businesses that deliver a great deal more value that has much, much larger TAMs. As you can see that's why the growth and that's why the increase in gross margins. We had to go focus on that. But in the meantime, my OEM team out of fight as hard as they can. Okay? Hey, Jason. Just a quick question on the game console. One of the reasons why Netflix do so well is that they're able to deliver through a browser. And we saw on Live try to build a micro console as well several years ago. Why not just why did you need to build a micro console? Why not if you look at Google Chrome, right, it can address the GPU directly. So why not build a micro console when you like Netflix, if you use it as a compare, they do it directly through browser. Why can't you do that? Yes. Good question. First of all, we're not building a micro console. In fact, we're not building a console game console at all. First of all, Shield is a Android smart TV first. It's not about trying to build a game console. It's about trying to reinvent, reimagine the way you enjoy television. And we have an opportunity to add a lot of value here. The reimagining of how people enjoy television includes of course a smart TV device that's really, really snappy. It's going to have a lot of apps. And it can't not include, it cannot include games, of course. And so be important. So that's our starting point. Now what was wrong with OnLive? Oh my God, the list goes on. Comparing what we're doing with Shield and on Live is just impossible. Now of course, they were pioneers. I mean, Steve is a pioneer and he tried a lot of things at the time. And he only had a few things that he could work with. He couldn't virtualize the GPU like I could. He didn't have the benefit of Android TV. There were so many things that Perelman didn't have access to. But he did he did he did, really great work. I mean, Steve is fantastically clever. And and, but it was just arguably, he just doesn't have the resources in his within his grasp to do the type of things that we're doing. And arguably, maybe a little bit too early. I think in the last slide that Colette had up and she had used the term redeployment. I'm just curious if that's related to Unified Architecture. And if so, how do we think about the potential for operating margins over time as these growth initiatives take foot? Well, there's a couple of things that you noticed last year. 1, the level of investment we brought to our growth businesses skyrocketed. Enormous new investments in growth businesses, yet our OpEx stayed flat. The way that happened was the entire management team worked incredibly hard to unify the architecture of 2 previously separate teams. They were separate for good reasons and now they're unified for good reasons. We took the GPU team and the TEGRA team and we unified it into 1. The process started about 3 years ago. We accelerated tremendously last couple of years. As a result, instead of doing 2 GPUs, instead of implementing 2 flows, instead of implementing 2 chips we're now implementing 1 if you will Uber GPU. The same GPU that goes into Tegra for TX1 is the same GPU I announced today. Titan X, the difference is 256 cores versus 3,076 cores. Okay. But the 2 of them are both incredibly energy efficient. That was the incredible benefit of unifying the 2 teams. As a result, we have one architecture, one design, one implementation flow. And we were able to redeploy and I think was the word you redeploy many of the engineers that otherwise we would have had to hire new into incredible growth drivers that you guys are experiencing now. So for example, that would be an Uber example. I mean a big example. I mean they ruined their word. It was going so well for so many years. That would be the ultimate management team is constantly thinking about how we could reshift and reshape. And that's one of the advantages in our company. The management team is constantly thinking about how we could reshift and reshape. And that's one that's one of the advantages in idea of shifting resources from one group to another group to another group. We don't think anybody belongs to us. They belong to the projects. They belong to the they're not they don't belong to organizations. They belong to the strategy. And so we change the strategy, people move around. Was that helpful to you? Yes. Okay. It's over here on your left. I wanted to ask a quick question about Grid and the enterprise. The vSphere announcement is to me a big deal. Things tend to move slower in Enterprise, but it occurs to me that this might be an opportunity where there's significant about the about the potential adoption curve and what that growth rate can look like over the next 24 to 36 months? Thanks. Maybe Shankar knows the answer better than I do. But here let me just let me go on the limb and show you how little I know. And let me tell you why. And it's actually truthful. The fact of the matter is we've created something new. When you invent something new, you have no idea how fast it's going to grow. Now we have some early indicators that shows us that in fact the number of people who are trying it, who are in pilot, who have deployed is now growing about 100% a year. We know that we used to engage about 10% of the market and now we're engaging about 80% of the market, 90% of the market, okay, because of the support of VMware and vSphere. We know some things. We know that in fact the number of servers that are grid certified at OEMs and it takes 1,000,000 of dollars to engineer a new server. The number of servers that have been created to support Grid has grown tremendously over the last year. We know that even ODMs who don't who are not branded, who don't call directly kind of architectures. It's going down all the way through this kind of architectures. It's going down all the way through the supply chain. So we know that this must be something important. Now, of course, we knew that it was important before anybody else showed us it was important. That's why we built it. And it's simple logic. It goes like this. Anybody else showed us it was important. That's why we built it. And it's simple logic. It goes like this. We have been GPU accelerated. Not one person in this room is having an office experience that's not GPU accelerated since 1997. Since 1997, Intel has been into integrated graphics business. It is GPU accelerated. How is it possible that we are enjoying VDI with no graphics acceleration? And so the answer is because the technology doesn't exist to virtualize graphics is a very hard problem. Finally, we have done that. And you're right, there is pent up demand. The problem is severe. People want to be mobile. Enterprises are heterogeneous. You can't control it end to end anymore. Your new employees bring all kinds of crazy computers to work, and you've got to make them all as an IT manager, make them all productive. And so there are many reasons why there's huge pent up demand. And how the laws treat your contractors, for example, in And how the laws treat your contractors, for example, in Germany has a big deal as it turns out in how they see this technology. For example, any contractor that sits on approximately the same levels. And of course, there's so much contracting work in some industries. The answer therefore is don't let those your contracting firms on your campus. And so they want to virtualize that. There's so many different reasons. The film industry has a workforce that go from 0 to 500 people at the hot times of creating a film and then it ramps back down to 0. How do you deal with workstations for them? And so there are so many different reasons why industry after industry that virtualization, remote remoting, the pent up demand is quite large. And so how do we how big do we think it is? I think the relatively simple math we did is and this is the question. Suppose we were to virtualize the graphics experience of 250,000,000 people, let's just kind of roughly call it 200,000,000 people. If we're to virtualize 2 100,000,000 people's enterprise computing capability, what would the opportunity be worth? That's kind of how they did it. We could illustrate how bad we were. When we launched early access for VMware vSphere in August, we expected 150. 100. 100. We ended up at 500 within 2 months. So if you want to trust our forecast. Yes. So because it's so new, it's really hard to tell. It's really hard to tell. But I think that the pent up demand is surely there. And I think our solution is clearly a good one. Otherwise, why would all of these companies from VMware to Citrix to IBM Cisco be out promoting it. It's legitimately fantastic technology. Hi. This is Shankar from Bank of America. I have a question on the upgrade cycle for PC games. I know you guys mentioned that about 100,000,000 gamers who have systems that have less performance than 960. And you guys work closely with the game developers like EA and Activision to kind of market once they release new games. So how often or how quickly do the ecosystem going to upgrade to the next greatest GPU once those games come up? Is that every 2 years or every year? How do we think about that in terms of the upgrade cycle as for the PC gaming? Well, we just I guess simply we view the installed base of having an average age of about 3.5 years, somewhere between 3 4 years. So when there's a big disconnect between new games and what's in the installed base that will shrink. There are periods of time that it may extend, but in the gaming segment, I would estimate it at about 3 to 4 years. So first of all, thanks for the presentations. Very useful. On Grid, on the partnership with VMware for their customers that take advantage of vGPU and your grid based servers. Obviously, you guys benefit from the hardware sale. But the other component to that is these same customers renew their vSphere vSphere licenses and their hypervisor licenses on a fairly regular basis. So the question is, does NVIDIA benefit as well? So do you not only get the hardware economics, but do you also benefit from the ongoing licensing of the overall seats that accompany that as well? One of the things that before I answer that, let me just make an observation. We because graphics is so complicated and you guys know, for example, we update our platform software literally every month. There's continuous performance tuning. There's new applications that come out. There are new features that are added. There are bugs to be fixed, so on and so forth. In this new environment, you take all of that complexity and you have to multiply by one more thing. And that one thing is the complexity the complexity of enterprise network. We are going to be intensely software rich in this area. And so it stands to reason that there will be a software layer and a software part of the licensing. And what I've advised my grid team to do is to think about the GPU, the hardware as not their business. And for them to think about their business model in the context of that and not to use the GPU as a hedge, if you will. The hedge is not the right answer, but as a bad habit. The GPU was already sold to the OEMs and the GRID software platform runs on this hardware platform. There's an ongoing quite a bit of care improvement, continuous tuning that will go on. And so there will be a software component to the business model. Okay, great. And then just one question on the automotive business, obviously, great growth there. There's 2 parts to it. There's the penetration, which I think Rob gave us some good ideas about penetration on a go forward basis. But the other component to that is content up lift. So if we rewind 3 years ago when we were talking about number of Tegras per car, so sort of looking up the value chain as you guys layer on more software and firmware and talk about compute level solutions, how do we think about the dollar content step up over time? No, no, no. I was listening to you. Here it comes. I just we're always making fun of each other guys. One of the things that you'll notice, that's why we're able to work with each other for like 24 years, okay? Perpetual state of entertainment and abuse. So, here's the simple logic. Today's cars are largely powered by embedded controllers. Calling them computers is like calling my rice cooker a computer. And it does have a computer inside. And but it's but we don't think of it as a computer. A computer has several characteristics about it that makes it much more valuable to us over time. For example, 3 days ago, I got an OTA from Elon and man, it gave me so much joy. And there's no question there were more features. And apparently in 2 more days, he's going to give me another OTA that's going to give me an amazing amount of joy. I'm not sure what it is, but I'm not going to tell you anyways. It's and that is what a computer Your computer, your PC downloads applications. It's getting better all the time. That's why we use our computers for so long. My tablet, I'm downloading it. My shield tablet is, man, one of my favorite computers. I use the living daylights out of it. I'm using it for all kinds of applications that I didn't realize existed when I first created it, right, a few years ago. And so, And therefore, a computer has a living, breathing nature to it. And therefore, a computer is always increasing in computational capacity. For a very, very, very, very, very long time, the PC industry's ASP continued to grow until one day, of course, in the case of the PC, a lot of the computational went into the cloud, okay? And that's the reason why it's starting to decline. Cloud, okay? If you were to measure Intel's ASPs, their ASP is clearly increasing as a company, but just not for PCs. I believe now in the case of the car, it's just the beginning of that cycle. It's got to be 20 years before we're going to put all of that stuff in the cloud. In the meantime, that rice cooker is about to get more computational capability. And I do know that the processors, the Tegra processors we have in the Teslas need to be a lot faster. And I know Elon wants it to be lot faster because all of the software that he's piled on it ever since has caused the performance to not be as good, not be as snappy as when it first started, okay? So when that happens, we're of course the ASPs will increase. When the number of pixels of displays increase, of course, this ASP should increase. And so I think over time that will pan out. I believe that. Jensen, a question on your IP revenue stream. Obviously, think you're doing about $260,000,000 a year currently. And given the situation with Intel and also, I guess, you mentioned that there's no update on the litigation. I'm just wondering, if you assume the best case scenario from the litigation, I don't know what that is, but just hoping that you could help us with that. Do you expect the current run rate to sustain in the best case scenario? I think that we should answer that in 1 or 2 ways. First of all, the question is what do I believe? And then second, what should you assume? What I believe is this. I've always believed in first principles. And I believe on first principles. We have made greater contributions and invented more modern graphics technology than all of the other companies combined. And I believe they're using that IP. And we have no trouble with that idea. Now we have no trouble people buying our chips which is my IP. It's just IP. The chip I didn't make chip TSMC made. It's their IP. My IP the NVIDIA IP is a bunch of software that's sitting on top of the chip. It's no different than the patents. It's no different than all of the innovation that isn't sitting on a chip. We're a technology company finally. And so, I have no trouble people using our chips so long as they compensate us fairly for it. We did work very hard for it and our engineers worked really hard for it. And it's upsetting for everybody if including our shareholders if we don't make an attempt to monetize it. So I think on first principles number 1, we have an incredible treasure chest of intellectual property in this field and a lot of people are using it and we are serious about being fairly Well, I think first of all that's a hard question. But I have no trouble with you guys assuming it's nothing. And just think about it from that perspective. And then when everything shows up, it will just be upside. Because maybe it's just a lot easier as a way to think about it. Okay? Does that make sense? Yes, it does. Thank you. And if anybody questions the value of our IP, if anybody questions the value of the IP meaning, gosh, Jensen that IP can't be worth very much, I would like to do something on behalf of all of us. I would like to make a tender offer of $100 for all of that IP. Since it's worth nothing to everybody anyways, if you guys wouldn't mind, I'll just buy it for $100 How about that? How about $100,000,000 Not one of you would sell it to me. Not one of you would sell it to me. There's the test. Isn't that right? Free market test. Now let's go to $1,000,000,000 I buy it. That's the free market test. So obviously it's worth a lot. Obviously, it's worth a lot. And so we just have to we have to go prove it. And the way to go prove the value of IP is the way we're currently doing it. If you're serious about it, if you believe in yourself, go litigate it. Great. And then maybe for Colette, you said you have about $1,700,000,000 onshore. What's the minimum cash balance you need to run operations? And then maybe Jensen, given all the M and A in the semi industry, what's your philosophy on M and A? Thank you. So our U. S. Cash balance, yes, is at $1,700,000,000 It's down from where it was in the prior year as we focus on a good portion of that being returned to shareholders. We received cash flow, both from our known overall our overall operations, but making sure we're not carrying more than that, and we can return as best as we can to overall shareholders. Stand today. M and A. We work with startup companies. We work with other companies all over the world. And we have no trouble working with them without owning them. And so number 1, we work with a lot of people and you guys can see a lot of startups. We've made investments in a lot of very innovative companies. Sometimes, sometimes the world is just better if you just left those companies alone. Sometimes the world's better if you made an in them and turbocharge them. Sometimes it's better for the world if we were to own them. And we're thoughtful about all of that. We're thoughtful about that entire spectrum. Now in terms of semiconductor acquisitions, we're very different than a normal semiconductor company. We're not exactly like Broadcom. For example, we don't have a large portfolio of things. I think SGS Thompson excuse me, Micro once told me they have 500,000 things on their catalog. I can't even fathom that. We have like 4 things on our catalog. And so acquiring semiconductor companies, the first question is semiconductor company, is it possible to integrate that semiconductor company into yours, my substrate that we today call Pascal. It is incredibly hard to do that. The nature of our company's business is just very, very different. We're not like Broadcom where you can have a large portfolio of components. We're not like Avago. We're not like STMicro. It doesn't work quite like we're not like TI. I mean they have wonderful businesses, but we're not like that. We're not a catalog of products company. We are a platform company. We really only have 1 or 2 platforms. That's also the reason why it's possible we can understand the markets that we serve so well. We're really only doing 1 or 2 things. It's just that we apply it deeply for 4 applications, 4 large applications. Okay. Is that helpful to you? Yes, ma'am. So you mentioned that the PC gaming industry market has been growing at about an 8% CAGR and that you've grown at about 17% over the same time frame. Are you still trying to grow it twice as fast as the PC gaming market? And if so, what are the main drivers that would enable you to grow twice as fast? I'm not going to let Fish answer that. And the reason for that is because I insist that he grows faster than even that. See, if he were to answer that, he would have gone on record and said, that sounds pretty good. I don't know about you guys, but that would be I would that would take a whole week of correction. Okay? But on first principles, I actually don't know how we growth isn't something you can control. That's the score, if you will, of the game. The game you can play, and here's how the game is played. This is basic recipe. When the production value of games go up, production value mean the richness of the beauty, the richness of the graphics, the number of characters, the people that you can connect into it, just the incredible lushness if you will of the world. When the production value goes up, the GPUs needed goes up and the ASPs go up. Largely, that's the reason why we're growing faster than the overall game market. That's probably the most significant bit. The most significant bit is that the production value of games have gone up quite dramatically even though it's the same. It's just a game, another $60 game. So even though the game industry didn't Okay. That's number 1. The production value of the game that was sold is so much higher. Does that make sense? Okay. That's number 1. The production value of games have gone up. Now, of course, there are other factors that have caused the GPU ASPs to have gone up. Resolutions have gone up. 4 ks, oh my gosh, that's a lot more pixels. And so, it's true that, there's a replacement cycle, but I actually think the replacement cycle has now shortened. And the reason for that is because our expectations have gone up. We see beautiful things We know what they can do with their computers and we don't know why our computers are so lousy. There's the number of games that require that benefits from higher richer graphics and better GPUs has really gone up. And so even if the overall market is growing on a certain level, I do believe that it's possible for us to continue to grow faster than that, but we'll see. Our job now do I translate that to action? Instead of hoping, the strategy is this. We will go and invest in things like GameWorks that naturally through our abilities and our understanding of science and math and computer graphics by creating these modules called GameWorks that Tony was talking about earlier and becoming the and being the industrial light magic of the whole industry, we can lift the production value of the whole industry. If it wasn't because of industrial Line Magic, how would we have Star Trek? How would we have Star Wars? How would we have they lifted the production value of CGI. We are lifting the production value of the whole game industry. 1st of all because it's great for games. 2nd of all, because it's great for our ASPs. Okay? And then lastly, I can't wait till VR goes out. I'm excited about Crescent Bay. I'm really super excited about Crescent Bay, DK3, the new Oculus display. Hopefully, they go to market this year. When that goes to market this year, I think they sold something like 250,000 DK2s, the last dev kit. If they were to sell in production, call it just a 1000000 enthusiasts with VR glasses, that alone would turbocharge our gaming business in a way that hasn't been felt recently. Okay. So there's a lot of different ways that I think we can outgrow the market. Thanks for a great day. I really, really appreciate it. You talked about Grid being at a liftoff. So with VMware win, when do you think we're going to see this inflection point where we'll really see Grid become a meaningful percentage of the overall revenue? In terms of absolute numbers, it's in the 10 ish 1,000,000 of dollars, okay? It grew in terms of percentage faster than any other business in the company last year. But it was approximately 0 the year before that and now call it tens of 1,000,000. I think it has the opportunity to grow quite fast, but it genuinely is hard to guess. It genuinely is hard to guess. If I could have guessed, I would have. But here's what we know we're tracking. We track 3 things. We track basically how many are in trials, how many have gone to pilot and how many that have been deployed. Okay. So we track that. We track the number of engagements that we are partnered directly with VMware we call direct access, where the enterprise has direct access to both of our teams, engineering teams, marketing teams, so on so forth, so that we can accelerate. They want to be direct access customers because they want to accelerate the deployment. And we've analyzed the situation, analyzed the account, and we think that they should be a perfect customer for rapid adoption. And so as Brown was saying earlier, we were expecting about 100 and we have 500. Now anybody who could miss a forecast like that doesn't deserve to do the next forecast. Okay. I stopped asking these guys. Hey, what do you think it's going to be like next year? And then he 200. Yes, 200. 200. He's going to double every year and it makes no sense. I stopped asking him. And on first principles, it actually makes sense. On first principles, he has no clue. He has no clue. The only thing that he knows is that the customers have real suffering. They do have real suffering. They have real needs. We have a real solution. There's real success. And everybody around us are equally excited. That's right. Okay. That's really all he knows. And I think on first principles that's actually for me enough. Now I know we have something of great value. We have partners that are aligned and we're operationalizing their go to market and we're excellent at that. And then on the other hand, I know that the market opportunity is very large, very, very large. Now in the middle, some probably some kind of an S curve, but I just don't know where it's going to take off. Okay. So automotive, you guys did a phenomenal job, dollars 183,000,000 from $99,000,000 And thank you very much for the transparency on the model. Really appreciate that. You've talked in the past, Jensen, about how much backlog you have in automotive. It's the clearest line of sight that you have. So when can we expect that inflection quarter in automotive? We have to ship these 25,000,000 cars say in the next 3 to 4 years. That's kind of because most of our design pipeline is about that long. And most of the things that Rob is winning now is being layered on to year 3 4 year out. And so people are trying to accelerate their product development. People are trying to accelerate because there's so much changing. But it's still going to take longer than 2 years to build with the exception of some really fast engineering companies. It will still take longer than 2 years to build, but it won't take longer than 3 or 4 years to build. And so most of the design wins that he has now would layer on top of that. But for the next couple of 3 years, 4 years, I think he's going to have to ship 25,000,000 cars, whereas since the beginning of I think Rob has been working on it since the Jurassic age. But all of that he's So he's cumulatively shipped 8. So he's cumulatively shipped $8,000,000 in the last call it 6, 7 years. Now he has to ship another $25,000,000 more in the next 3 to 4 years. That kind of gives you a I are as they go and like kind of like a user generated video scene. Video seems I mean, YouTube videos still look like YouTube videos, not very good. It seems like it's a harder problem than photos. Is that something where is that something that interests you that you think NVIDIA could bring solutions to the market that could make that easier accelerate that process? There are many interesting video applications. Let me give you one. Suppose an Internet service provider who provides a lot of video were to sign on a new advertiser and the new advertiser says, I'm willing to advertise on your platform. However, I want to know that all of the previous content that has been streamed using our to the to the history of time to look for every single video, look through every single video to to find a particular mark or a particular move a movie or particular character or something. That particular application is very sensible because these companies don't want their proprietary content to be used in an improper way. They want to know that people are not taking their IP and mashing it up with their video and turned it into something else. Okay? That's another way of infringing So they want to go through the entire library and do that if they were going to become an advertiser. Well, the Internet service provider would be more than happy to do that. Well, it turns out it's rather computationally intensive in searching through all of those videos to find those marks. That would be a perfect application for example of deep learning today. Now on the streaming side, I could imagine that in the next by the year's end, you will be enjoying on the Shield console 4 ks 60 Hertz video from some very, very prominent providers. And looking at 4ks 60 Hertz is a lot different than 1080p30. It's just shockingly different. It's twice the frame rate and 4 times the pixels. Yes, 8 times fidelity and that will happen by the end of the year. If I could follow-up on that with big data analytics and deep learning, one thing you mentioned surveillance or maybe kind of quickly mentioned surveillance. Can you talk about that potential there as a long term business opportunity in terms of governments as a vertical, looking at not just images, but obviously signal and sound and other sorts of processes which we could use as a security application for example? We're engaged with a lot of municipals on what is known as intelligent video analysis. And we engage them on a platform that includes 2 parts, a little Tekra chip that takes the video in and is doing pre processing of your deep neural network analysis of things. They don't know exactly what things they're looking for all the time, but they're looking for so if they download software running on a deep intelligent video analysis intelligent video analysis on a very large number of video feeds. Country. It's called Smart City. Smart City, Safe City is an initiative in nearly every single country. Somebody mentioned earlier about London, I think it was Shankar was talking about London earlier. But the model of London is about to be replicated except with high definition, except with more cameras, using computational methods. How long would this take is hard to say, but you can just imagine the potential of the applications. It will be in airports, it will be in train stations, it will be in bus stops, it will be all over the place. And my sense is that it will be done in a way that doesn't cross that line. I mean they will be smart about these about the way to do it. Yes. Yes. The crowd behavior crowd behavior analytics at the Super Bowl, which will be held at Santa Clara next year. Perfect. Perfect. Let's take one more and Khaled, you have FinFET transition coming in down the line. Could you is that a meaningful impact on your OpEx? And also, could you tell us your OpEx expectation for this year and next year? It's our transitions are part of every day in terms of what we've been working on, multiple year discussion in terms of those transitions. So I wouldn't consider it to be any type of a onetime event as we think about the future and what we may offer. When we think about our overall OpEx level for this year and what we think about next year, We'll continue the same focus of what we've done in the last year, assuring that we make the right investments in the expansion of the TAMs that my peers absolutely talked about that are so important and vital for the overall future. But at the same time, the look in terms of efficiencies that we can find and how we work every single day will be top of our list to do. Every single quarter and predicting what that exact number is going to be. 1, I'm probably not going to be perfectly accurate, but I'm going to work the hardest that we can just as we've guided to do that throughout the full fiscal year. As I look past that, I think that's just too far out to really think about. Let us get through fiscal year 'sixteen and go through. So no, I'm not answering the question specifically with an exact number. But again, we're going to continue to follow what we did in terms of last year. And again, focus really on these business models and growing their towns. Okay. Thank you. I think that's Colette's way of saying, I guide once a quarter. So with that, that, let me thank all of you for the great day. It's a privilege to spend the day with you guys. I hope that you guys take away several things. 1, this is a company that has more capabilities in the field of visual computing than any company on the planet. We have reshaped our company so that we focus on 4 large vertical initiatives despite the fact that the overall computing market is largely flat. And as a result of the high value products, our gross margins are improving. Okay. So I hope that those were the takeaways you had as well and look forward to seeing you guys again soon. Thank you very much. Good job, guys.

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