NVIDIA Corporation (NVDA)

Investor Day 2019

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Mar 19, 2019

Okay. Well, good morning, everyone. Welcome to NVIDIA's Investor Day. I'm Simona Jankowski with Investor Relations, and it's my pleasure to welcome all of you here today as well as all of those who are joining us on the webcast. Before we kick it off, I would like to read our safe harbor. We will make forward looking statements in today's program regarding our expectations and other future events, which may differ materially from NVIDIA's actual results. I'd like to refer you to our SEC filings for a description of our businesses and associated risks and other factors, which could cause the results to differ materially from these statements. All our statements are made as of today, March 19, 2019, based on information currently available to us. Except as required by law, we assume no obligation to update any of these statements. Also, if we use any non GAAP financial measures, you'll find the reconciliations to GAAP on our IR website. Okay. So with that, let me just go over very quickly the agenda for today. We're going to be starting off with a few minutes with Jensen Huang, our Founder and CEO, who I think you all know, talking about our strategy. We will then move over to our gaming business, which will be covered by Jeff Fisher. Following that, we're going to talk about data center with Jay Perry, automotive with Rob Chonger and finishing up with financials with Colette Tress, our CFO. We are going to have about an hour of Q and A after all of that with Jensen and Colette. And then after that, we're going to have lunch, which is going to be in the gold ballroom if you walk out of the doors and down the hall to your left. In terms of just a couple of closing items, if you need anything throughout the course of today, just reach out to myself or Sean Simmons on the Investor Relations team, and you can find us in the back of the room or just e mail us. And again, I'd like to request that all of you silence your phones. And with that, it is my pleasure to now welcome to the stage, Janssen Huang. Thank you. This is going to be the 5th hour of my keynote. If you missed it yesterday, if you happen to have missed it, you can watch it on YouTube. Put it on 3x speed because it will take about 2 hours if you did that. First of all, welcome. It's great to see all of you. Here's what I'm going to do. I'm going to do a couple of things. I'm going to explain and this for most of you, you know this very well. There's some new faces in the room, so I thought I would do this. I would explain what accelerated computing is. Accelerated computing is accelerating, but it's not an accelerator. And I wanted to find the difference for you, okay? And so as soon as Hey, guys. Hey, guys. Hey, guys. Fish. Hey, guys. I'm trying to give a talk. Are you close that door? It's an analyst meeting. When you guys come to NVIDIA's formal events, it always seems like home cooking, doesn't it? Okay. So, accelerated computing. Accelerated computing is particularly important today because CPU scaling is no longer happening at the exponential rates it used to. At a time when application workload demand on computing is growing incredibly fast. The question is how do we extend Moore's Law? How do we extend Moore's Law? Well, we came about this idea called accelerated computing a decade and a half ago. 26 years ago, when we first started the company, we realized that accelerators could help us achieve performances otherwise impossible with a normal computer, accelerators. And we built we identified one particular accelerator that has what we called at the time, if you saw one of my presentations 26 years ago, it said, bless you, sustainable opportunity. Sustainable opportunity, meaning that this particular application called virtual reality, trying to achieve virtual reality 3 d graphics was going to take nearly forever. And the reason for that is in order to create this environment, you have to simulate physics, life physics, particle physics, material physics. You have to simulate physics and you have to do it so fast that for all practical purposes, it's going to take forever. And the reason for that is because at the time, simulators, supercomputers were doing it, simulating some fluid dynamic simulation or particle simulation. It was taking a week on a supercomputer. What are the odds that we're going to be able to do it at 120 frames per second And to be able to simulate all the interactions with all of the agents performing artificial intelligence capabilities, all interacting together. The odds of that happening within a lifetime is approximately 0. We were not wrong. We identified one problem statement that we said had sustainable opportunity. 10 years into it, we discovered that in fact, in order to continue to expand it, we have to expand the aperture, if you will, of the things we accelerated. No longer was it sufficient to just accelerate graphics. We had to first simulate the physics and then accelerated the graphics because you have to simulate the water. You have to simulate the leaves blowing in the wind. You have to simulate things particle physics as buildings crumbled. And so it was impossible to have animated all of that. We decided that you have to simulate that. So we expanded the aperture of our accelerator and we invented this idea called CUDA, so that we could expand not just accelerating graphics, but the domain of virtual reality, the domain of virtual reality. That time, when we transitioned from a graphics accelerator to a domain accelerator, we became an accelerated computing company. An accelerator accelerates a function. An accelerated computing platform accelerates a domain of applications. Does that make sense? An accelerator is a video accelerator, H. 264 Accelerator. An audio codec is an accelerator. All of the stuff that runs on an audio codec, with the exception of the analog, can run-in software. All of the things, all of the functions in a video decoder or encoder can run-in software. And in fact, the first prototypes of a decoder isn't software, And the first prototypes of an encoder isn't software. So all of these functions, computer functions can run-in software, and it's possible to design an accelerator for that one function. You would use a video decoder to decode video, but you would not use a video decoder to compute molecular dynamics. You would use a video encoder to encode video H. 264, H. 265 or back in the good old days, MPEG-1 and MPEG-two, but you would not use a video encoder to do, for example, a recurrent neural net for deep learning. And if you design a recurrent neural net deep learning accelerator, you wouldn't be able to use that, for example, for random forest machine learning algorithm. If you design a functionality just for an accelerator for one functionality, it would certainly be very good, but it doesn't have the necessary aperture to accelerate a large domain. The challenge, of course, is if you created a product that has an aperture of infinite domains, what you've done is you've created a CPU. The reason why accelerated computing is so wise and the reason why, although many other parallel computing approaches have come before us, the reason why it has lasted the test of time is because it allowed the CPU to do what the CPU is good at and it accelerated the domain of applications that we are good at. And that discipline of trying to figure out how to expand the aperture while reducing the aperture at the same time. That strategic choice is ultimately the strategies you see at GTC. There are several things you could do to test whether something is an accelerator or an accelerated computing platform. Of course, the first thing is it has to be a programmable architecture. On the one hand, one day you have to do molecular dynamics, another day you do quantum chemistry, another day you simulate a large climate science program called WORF, another day you're reconstructing images out of electron microscopy called cryo EM, which won the Nobel Prize in Physics 2 years ago. It's hard to be able to do that if it is only designed for one thing, it has to be programmable. The second thing about all computing architecture is that it has to be an architecture, which means this. An application that you wrote for that computer runs on that computer and on that computer. And you buy a new computer tomorrow and the application runs on it. A computing architecture has some capability of compatibility over time, and it has to have a large installed base. Otherwise, applications can't find computers to run it on. Accelerators don't have that problem. The other characteristic of an accelerated computing platform is it has to have a rich software stack. It turns out the most important thing about our company is our stack. That's why we talk about it all the time. If you look at this, this is our stack. Our stack starts with the system architecture. I'm not showing the chip, I'm taking the chip for granted. The system architectures, the RTX is for graphics, DGX is for scale up high performance computing, otherwise known as deep learning or supercomputing, hyperscale, HGX, and then AGX for autonomous computers, little systems that are intended to live at the edge, largely disconnected from the cloud, largely disconnected from the cloud. We are currently because we're artificial, we're somewhat intelligent, we can perform our jobs disconnected from the cloud. I am currently disconnected from the cloud, okay? I'm autonomous. And so that AGX is designed to be an autonomous machine. On top of that is our most important layer, Kalkuda. I call it Kalkuda here, but that layer is really complicated with a whole bunch of stuff. It's not worthwhile to go into, but it's basically, if you will, our AWS. It's basically our Windows. CUDA makes it possible for an application that runs on CUDA to run on all of these devices. Yesterday, I announced a $99 computer, a $99 full computer. It runs the same software stack as a $1,000,000 supercomputer, as a $250,000 DGX deep learning system or a PC. There's only one computer architecture in the world aside from this that does that, And it's the X86. And so an accelerated computing architecture has a rich software stack. The other thing about accelerated computing that's interesting is this, because of what I said earlier, there's only one computer architecture that can boil the ocean, That's called the CPU. Its general purpose, that's its nature. That's its weakness too. Its strength is that it can run everything. Its weakness is that it doesn't run anything super well. Now, during the time when the performance is increasing by a factor of 2 every year and a half, it was plenty fast enough. And the reason why it was plenty fast enough is because software developers take 2 or 3 or 4 years to complete each round of major innovation. Meanwhile, the computer has already quadrupled in performance by the time that the next build comes along. It's fantastic to just ride that wave, to do nothing and just let the wave take you. That was the whole dynamic of Moore's Law. It was fantastic while it lasted. But now, if that slows down, if that slows down, then all of a sudden, you can't solve new problems. If you can't solve new problems, the software industry will suffocate because they can't obviously introduce new ideas. And that's why the world needs a path forward. We need a way to go forward. Now, you're not going to find a way to go forward by coming up with another general purpose computer. You have to find a way to go into it through domain acceleration, not a function accelerator, not an accelerator, but an accelerated computing architecture, so that we can take the industry forward. Well, this computing this accelerated computing architecture must have vertical domains that are focused on, otherwise known as the counter of horizontal vertical. And so we select verticals strategically, strategically and methodically, so that we can, 1, make a contribution by the time that it's necessary. It's sufficiently large to be able to sustain the enormous investment that we put into it, but it's not so large. It's not so large as essentially a horizontal problem. For example, a web browser is so large, there's no such thing as a web browser accelerator. The only way to accelerate web browsers is to make every web browser faster. However, video games is a little bit of a unicorn. We identified the killer app 26 years ago using exactly the same methodology I just described. 26 years ago, we used the same methodology and we said, if we wanted to be one of the world's most important computer technology companies someday, what is the killer app that we can make a contribution to that will take us all the way? And the killer app we found that we thought of, that we identified and focused on at the time was a $0,000,000,000 market. Electronic Arts was 14 people large. Dollars 0,000,000,000 market. That $0,000,000,000 market is called video games. It's a unicorn because it has 2 characteristics simultaneously. It never happens. It never happens. You have a spreadsheet used by millions and millions of people, but the computation requirement is low. You have a weather simulator. The computation requirement is enormous, but the volume requirement is very low. So in both cases, it's unable to justify an accelerated computing platform. There was this unicorn that stood out there. We imagined that if someday there was such a thing as a video game industry, it would both be large because everybody would be gamers who wouldn't want to play. And then 2, the computation requirement of it would be methods that same method is being applied here. And you can see one segment after another, we're essentially finding verticals that are sufficiently large in domain that could sustain more and more and more and more and more investment as we grow into them. High performance Computing, Scientific Computing, a very important segment. Artificial Intelligence, we can talk plenty about it today. Drive autonomous vehicles, a very, very difficult computation problem, not just a computation problem for in the car, but computation problem before you get to the car. That gigantic computation problem I described a little bit yesterday, DRiV, the whole platform of DRiV, the initiative of DRiV is about creating the autonomous vehicle future, not about making a self driving car. It's a little bit different. One of them is very large in scope, It requires you to be a software defined company. It requires you to have an ecosystem. It requires you to have developers and tools. Isaac, I've described Isaac in the same way. The ultimate AI problem is both a wonderful opportunity when you solve it in the device at the edge, but getting there is a supercomputing problem. And I've shown a couple of examples yesterday where you're essentially creating a virtual reality world where the robot has to learn how to be a robot. That is a supercomputing problem. Clara, named after Clara Barton, who started the American Red Cross, is our platform for medical imaging, computational medical imaging, turning the instruments of medicine into a software defined problem. Today, it's a bunch of instruments and widgets and things like that. Today, tomorrow, it's going to be largely software deploying. Algorithms are going to fly, and they're going to be able to do things that otherwise impossible today. And then lastly, Metropolis. The Metropolis name kind of gives it away. It's really about thinking about cities and places as one gigantic robot in the future. Our city in the future will have three characteristics: Cities of the future, factories of the future, buildings of the future will have 3 characteristics. The first characteristics is tons of sensors. The second characteristic, a bunch of computation at the edge, basically the reflexes of that robotic city doesn't have to go to a cognitive brain in the cloud and then 3rd, connected to a cognitive brain in the cloud. Those three characteristics so that you can make decisions and plan perception, reasoning and planning, the 3 computations of an intelligent beam, otherwise known as the computation loop of intelligence or robotics, is going to be used for Metropolis. At this, I think there are talks at GTC between us and Microsoft where Jetson Nano, our edge computing stack is connected to the Azure IoT stack and some really, really exciting applications could be made possible. So this is the accelerated computing stack, very different than an accelerator. We focus on domains, not functions. There's a couple of things that characterizes a company who's a platform company. If you're a platform company, you talk about design wins less. If you're a chip company, a components company, you talk about design wins a lot. When you're a platform company, you talk about your ecosystem a lot. And the reason for that is because you created the market or you're creating the market and you need a lot of partners to work with you to realize the full potential of that market, you have ecosystem partners that work with you on your platform. And that platform is rich with software. And that software is domain focused, not function focused. It doesn't do CNN. It does accelerated data science. Okay. And so if you look at the comparatives, when you think when you hear us talk, that's the reason why we talk this way, because we're a computing platform company. I announced a couple of things yesterday. 1st, Fish is going to talk more about this, but the big takeaway here is RTX is off to a great start. It is clear now that ray tracing is here. This week is game developers conference and all they're talking about is ray tracing. It is clear that ray tracing is here. Remember this, ray tracing is software. And the reason why you can tell ray tracing is software is Turner Whitted, NVIDIA researcher who invented ray tracing, iterative ray tracing, recursive ray tracing, did the first implementation on a VAX in software. We know it's software because all the movies that are made is in software. It's called rendering software and it runs on CPU farms, otherwise known as rendering farms. What RTX does is not do ray tracing. What RTX does is make ray tracing fast. So we love the fact that people do ray tracing. We just want to make it super fast. And there's no question ray tracing is here and RTX is going to make it super fast. Number 2, take the second thing that I showed you yesterday was the fact that graphics is going to be a new data center workload. This brings us so much joy, as you could imagine. Graphics is going to be a new data center workload. You heard Matt Garman say it on stage yesterday as well, as he was talking about the need, the demand on AWS and one of the major applications is graphics. He'd mentioned Fish will talk and Fish will talk more about this, about our partnership with regional telcos, global telcos, and it's part of our GFN strategy. We call it the GFN Alliance. He'll explain this, but very simply, they buy these servers from us. They buy these super optimized graphics servers from us called RTX. After they buy the servers, we host a service on top of it because the GFN service belongs to us. We can host that service and we share the revenues with them. We share the revenues with them. So, they buy the servers from us, they share the revenues on the subscription fees on top. Does that make sense? That's called the GeForce NOW Alliance. You could imagine the economics. It could be quite good. I talked about data center, graphics in the data center. There's several new workloads in the data center. Of course, graphics is we already talked about high performance computing in the past. We already talked about deep learning in the past. We already talked about inference in the past. We're going to talk more about that today. But some of the new workloads that I talked about this week, graphics is 1 and the second one is a gigantic one. This is the unicorn that we've been looking for in the data center. Let me explain to you why. Remember, I explained earlier that there are 2 types of applications in computing, and that's why there's largely 2 architectures. And if you look it up, it says there are capacity machines and capability machines. That's the way the supercomputer industry talks. The way the hyperscale data center people talk is they say they're scale up machines and then they're scale out machines. Scale out is hyperscale. You take a cost efficient computer and you scale it out linearly so that you could support a whole lot of jobs that are small at the same time, scale out. Scale up says you build the largest computer you possibly can, whether it's the largest amount of computational capability, the amount of storage, the amount of active memory, the amount of networking, you build yourself the largest machine you can so that you can solve the largest problem as fast as possible for one person. Weather simulation, climate science, these things take forever to simulate. That's called a capability machine, a capability machine, a capacity machine, a scale up machine, a scale out machine, a supercomputer, a hyperscale data center. Are you guys following me? All three phrases are identical. Okay. All three are identical. Here's the unicorn. It turns out a supercomputer, the market size for it is not very large. The computational challenge is great, and we're doing fantastic in supercomputers. Here, I showed you a bunch of numbers. People at NVIDIA call this CO math, and I just got to concede, it is not accurate, but it is absolutely right. Okay. It's not accurate, it's right. It's this is intuitive math, and if you go double check it, you'll find that it's probably wrong in some area, but on the large scale, it's perfectly right, right? And so, if you look at the numbers, each one of the numbers, each one of the ticks, if you will, is 3 orders of magnitude. Are you guys following me? This is 3 orders of magnitude here. This is extreme log log, okay? Now Extreme Log Log says, in supercomputing, I need 1,000,000,000 petaflops, not in seconds, in units. I need 1,000,000,000 petaflops in order to perform some of those simulations. In the case of concurrent users, CCUs, a hyperscale data center has to support 100 of millions of people at the same time. In the case of supercomputers, only tens, if not at the very, very most, 100. In the case of hyperscale, the amount of computation that it takes to perform these neural networks is small in total. It's just there's a great deal of them. And the scale goes everywhere from 100 of gigaflops to maybe 100 of teraflops in units not in time. Okay. And so what's interesting is this, data science, data science, it's that bubble in the middle. When we have more time, I'm happy to break it all down so that you guys get a feel for the numbers. But the important thing is this, on the upper right to the lower upper left to the lower right, upper left to the lower right. The upper left is 3 orders of magnitude, more computation from the top to the bottom and to the lower right is 3 orders of magnitude in volume. And the reason for that is this, data science is the only high performance computing problem we know where there's millions of people, millions of people and millions of people in different fields of science, healthcare, financial services, they call them clients, insurance companies, retail, logistics, travel, you name it. Every single industry will benefit from data science. That's why there's so many people. The amount computation you need because the amount of data that you're working on is so gigantic, it's simultaneously a large computing problem. That's why the quants have the largest computers. Now imagine, there are going to be millions of quants. And the reason for that is because there are so many industries where there's domain expertise and finally the technology is capable of being used at a large scale. The frameworks, the algorithms are sufficiently robust now, and the schools are teaching it. You guys know that data science is being taught to every single field of science in a university now. From sociology to oceanography to forestry to agriculture, it is the 4th pillar of the scientific method. This new pillar of the scientific method came about literally made possible in the last 10 years, came about in the last 5 years. And about the same time that deep learning was happening, the same dynamics was happening to data science. It is going to be a very large market And data science as a 4th pillar, theoretical, experimental, computational and now data driven science. Okay. This is quite a large market. For the upper left, am I doing this right, upper left? On the upper left, our strategy is to create something simple for people to use. It's basically an appliance of a supercomputer, because most companies don't have the ability to build a supercomputer. It's too hard. Too much IT, too much system integration, too much software optimization. We containerized it, if you will, turned it into an appliance. On the right hand side, it's a scale out problem. We have to turn basically a data center for an enterprise or a hyperscale data center into a high performance computer. And there, we have to break it all down in a different form factor, build different GPUs, write different software, work with different partners, and our go to market is different. The go to market on the right side are the world's top enterprise makers enterprise computer makers. They're all signed up. They're also excited. On this left side, they're really deep, super quants, super data scientists. And there, the numbers are not in the millions. They're probably in the order of, call it, 50,000, okay, 100,000, but they need the best machine and we reach them through experts in other specialized IT experts like storage companies because it turns out if you want to use one of those machines, you need a lot of storage anyhow, a lot of harmony there. And so it has something to do with our go to market and Jay will talk more about that. So this, the second point is data science is a major new market. I mentioned ecosystem. This is our ecosystem in one slide. Now, when I say ecosystem, I don't mean design wins. When I say ecosystems, these are all partners of ours who are taking the NVIDIA architecture to market. They're not changing it. They're not hiding it underneath theirs. They're taking it to market. They might integrate it with theirs. Okay. So this is our platform and their platform coming together. Sometimes this is our platform going to market by itself. But these are ecosystem partners of ours, and we're super, super happy that literally everybody in the larger IT industry is part of our ecosystem today. We announced 2 new types of computers. We announced a data science workstation and we announced a data science server, both of them software fully integrated. You buy them, you should be able to deploy them, really complicated set of software. However, it's already configured and optimized for you. One of the things that you could see and then in the cloud, we announced a partnership with AWS and Matt Garmon was very, very, very I really appreciate him coming down and celebrating the moment with us. And so we have workstations, servers and cloud to take this data science platform to the world. And then lastly, one of the things that you might notice is if these workloads, work sets are so large that it doesn't fit on one computer, the connectivity between the computer becomes the greatest challenge. And I showed yesterday the performance of a fast interconnect and the performance of a fast interconnect with the right type of CPU offload, the performance difference is 2x. What that says is that the architecture of the networking, not just the speed of the networking, matters a great deal. The architecture of the networking, not just the speed of the networking and our vision is that someday, the computing fabric will not stop at the boundaries of the server. The computing fabric would extend out into the network and the network and the compute will become 1 large computing fabric, especially for data centers, which as we know is the most important computer in the future. And then lastly, we announced autonomous machines. Autonomous machines is both a edge opportunity for us, but we wanted to show you that in fact, the reason why we're part of it is not just because of the edge, it's because getting to the edge is a big opportunity. That getting to the edge is a big opportunity. In order to create the ultimate AI, which is otherwise known as a robot or self driving car or IoT device, AI IoT, when people say those things, they're saying basically a robot, an autonomous machine. In order to achieve that capability and putting intelligence at the edge, the process of getting there involves deep learning systems, machine learning systems, data analytics systems to develop the software, to simulate the robotics before you deploy it, and then of course, to deploy a very complicated set of algorithms and software. Our strategy with the autonomous vehicle is to enable the entire world of AVs to become autonomous, whether it's robot taxis or passenger owned vehicles or trucks or cars or vans or forklifts, construction vehicles, farming equipment, they're all going to have autonomous vehicle capability. We want to enable all of that. And so we created an open software defined accelerated platform, accelerated computing platform. We want to do the same for something that's even larger than that, robotics. There are 1,000,000,000 cars sold each year. But as we know, based on the things that I described earlier, there'll be trillions of things out in the world someday. They're all going to connect into essentially large networks that turn buildings, factories, cities, farms into essentially autonomous robots. The future factory would be a factory, would be a robot that's building other robots. And so that when I say robot, I don't actually mean necessarily somebody who has limbs and walks around. Robot, the concept of a robot, chatbot, an AI assistant is essentially a digital robot, okay? So when I say robot, I just want you to hear something different than what you might be imagining, an autonomous system, an AI system. And so we announced a family of products, and we announced yesterday that we're expanding our partnership with Toyota tremendously. We were already working with them on some early developments of cars and now from end to end, from software development, AI development to simulation to computing to algorithms, we're going to partner deeply with the world's largest car company. They've selected us to be their primary technology partner, and we're very honored by that. And so that's basically it. There are 3 takeaways then that 1, RTX has taken off, it's off to a great start, ray tracing is here. There's a whole bunch of new workloads for the data center. Graphics is 1, data science is another, autonomous vehicles is another and of course, IoT is another, robotics at a very large scale. And then the third is data science is the new driver for HPC, and every data center in the future will be a high performance computing data center. I want to thank all of you for coming. And so with that, I'm going to hand it off to Jeff Fisher. Ladies and gentlemen, this is Fish. Thanks, gents. I need a is there a cushion? I left it there for you. Fish and I have only been working together for 25 years. We were children. Welcome everybody to Investors Day 2019. I want to give you an update on gaming. And there's a lot going on. We had an exciting year last year. I'm sure you guys all know and we look forward to even more exciting year this year. Last year was a record year for gaming. We launched RTX, biggest leap in graphics in 15 years. 15 years ago, we launched programmable shaders in our Fermi architecture. Today, virtually, well, every game is based on programmable shaders. With RTX, we launched a brand new architecture holding in real time ray tracing, and I'll talk a bit more about some of the momentum behind this next generation architecture. Max Q laptops driving thin and light laptops. This year, we've got the thinnest, the lightest, the most powerful laptops driving the laptop market. I'll talk a little bit more about that. Just past last year, most recently, we brought our Turing architecture down into the mainstream to the $2.19 price point. We now have a top to bottom stack of Turing GPUs, millions more gamers coming onto the architecture. And finally, not to be missed, last year, we mentioned crypto came to town, and this past year, it left town. We see the crypto hangover on track to sell through our channel inventory by the end of Q1. That is moving nicely. So this year was a record year, 13% growth year over year and let's dig in a bit more. First of all, the fundamentals of gaming remain very strong. Our basic core business continues to be strong. As we've mentioned before and I continue to say, everybody born today is a gamer. Every child is born a gamer. The demographics are also working in the favor of gaming. Gamers continue to game longer in life. You start out a gamer, you game longer in life, the total population of gamers continues to grow. And what's driving that? Well, eSports momentum is still huge. I wanted to, if you don't mind for a moment, I saw in my inbox this morning, we get a weekly update from my team on what's going on in the world of eSports. I know you guys track that news very closely, but just in case you missed a few things, I'm going to read you a couple of things that came in my inbox this morning just for note. Call of Duty franchise sports franchisee sports to sell at 225,000,000 per team, Call of Duty franchise spots to sell for 25,000,000 per team. Apex Legend is primed to be the next big e sport, okay, no surprise there. Battle Royale blurs the line between entertainment and e sports. It's not just for competitive gaming, but it's also for watching. Snoop Dogg's, you probably missed this. Snoop Dogg Esports Series kicks off tonight. Walmart becomes the 1st major grocer chain to put esports arenas in the stores, dollars 5 for open play and leagues at night. ESPN announces creation of College Esports Championship, of course, Disneyland Paris to host a DOTA 2 major this year in May. And as you know, the DOTA 2 major, the final tournament is being hosted in Shanghai this year. China is one of the biggest markets for esports. In the Mercedes Benz stadium that will hold 185,000 spectators. And the DOTA 2 tournament is the biggest tournament in the world from a price pool standpoint. Last year, it had a $25,000,000 prize pool. So eSports is obviously getting a ton of attention. The momentum continues to grow and it's bringing in new gamers in the U. S. But most importantly in the APAC regions in emerging markets and in China. The viewership for esports is about doubling over the last 4 years, continues to bring in an audience. More people are watching esports online than watching basketball and the number of gamers continues to grow attracted by the competitiveness, the competitive nature, the social nature of eSports and competitive gaming, about 30% over the last 4 years, more gamers coming into PC gaming. And of course, on the AAA gaming side, the cinematic side, game production value continues to increase. We talk about this year, but it continues to grow. Game developers are adding more realism into their games. It takes about a 5 times more powerful GPU to play today's games at 1080p60 frames per second than games that were released in 2014. Games keep getting more realistic, you need a higher end GPU to play them. And these are the fundamentals that continue to drive gaming and we see a strong future for gaming. So let's take a little bit take a deeper look into our business. Specifically within the gaming business, our GeForce GPU The contribution is both from units and ASPs. Our 5 year CAGR for units and ASPs continues at about 14%, total revenue growth over the past 5 years, about 29%. But if you look at laptop, laptop is outpacing desktop in terms of growth for reasons we'll talk about later. Both ASP and units contributed to our laptop growth that drove about a 59% year over year increase. Looking more specifically at RTX, Jensen mentioned that RTX is off to a great start. Well, I would say RTX is on to a great start. You see what I did there? RTX is on? Yes, okay. Anyway, RTX is on to a great start. We've now released RTX GPUs down to 3.49. At CES, we launched the RTX 2,060 at 3.49. I mean, time flies when you're having fun, but that was just about 8 weeks ago. So I look back at our estimated sell through of RTX from $2.99 up, that's a $2,060 up, compared to our estimated sell through of Pascal from $2.99 up, that's about a $10.70 up, starting from time 0 of each of these devices. Turing sell through, RTX sell through as outpacing PASCAL by about 46% in revenue, normalizing the time 0 1st 8 weeks of sales. So RTX Turing is definitely off to a great start, estimated sell through. If you look at our installed base, the installed base is ready to upgrade. About half of our installed base is Pascal, the other half is older architectures. And Turing is just getting its toehold in at 2%. And if I look at the performance of the installed base, 90% of our installed base is below one of our most recent GPUs we announced, the 1660 Ti, is below the performance of the 1660 Ti. I'll tell you in a little bit of why I picked 1660 Ti and why that's relevant. Another fact digging into our sales, the Turing buyers that we're able to track that are upgrading from our installed base are buying up. 90% of the GeForce RTX buyers are buying up from a lower price point. They had a lower price point GPU in their system. They bought it turning and upgraded. So 90% are buying up. So let's take a look at what's driving that. There are 2 types of games and not necessarily 2 types of gamers, but 2 types of games. Esports, simply put, I'll say Esports, which is competitive gaming and cinematic or AAA gaming. Within our installed base, there's about a 50% overlap. About 50% of our gamers will play both. Some will play 1, some will play the other, but they all value the performance of the GPU. Esports gamers value frame rate. Faster FPS means faster response time and faster response time means more wins. We see in our installed base gamers that are playing esports titles want to play at 120 fips or higher. Interestingly enough, looking at our ecosystem, and specifically Fortnite, we can see that gamers that play at higher FPS have a higher, what we call a KD ratio. I'll call it a win loss ratio. Gamers that play at 60 frames per second relative to gamers that are playing Fortnite at 2 40 frames per second win roughly 2.5 more times fat more often in their KD ratio increases about 2.5 times more. They win about 2.5 times more often at higher FPS. It's natural. Faster to point and shoot is the one who is going to win. So there is definitely a relationship between more FPS and more wins. And the pros know this as well. There's a popular site called prosettings.net, if you've not been to it. Prosettings.net has about 900 Gaming Pros and Streaming Pros online where they enter what all of their gaming hardware is, including their system config and GPU. 98% of those on prosettings.net, the pros on prosettings.net are powered by GeForce. And interestingly enough, over 2 thirds of those pros are playing on systems that have the performance of an RTX 2,070 or higher. And over about a third are the performance of an RTX 2,080 or 2,080 TI. So the pros know that the better the rig, the faster the system, the more often you're going to win and the better the gameplay. Looking at our GPU stack, I mentioned the 1660 Ti, 90% of our installed base is below 1660 tie. 1660 tie is what's required to play Apex Legends, as I mentioned, fastest growing competitive gaming title on the planet right now, at 120 fips, 1080p high settings. Gamers, serious gamers as well as pros value FPS. 16/60 ties what we see as a starting point, but they don't stop there. As with the pros, they will upgrade their rig to get the best possible performance. Also mentioned AAA gamers. AAA gamers have different priorities or gamers who play AAA games have different priorities. Their priority shifts to image quality. These games are designed for cinematics, the best possible image quality. And they'll play it, they want it at a smooth frame rate, say 45 to 90 frames per second. In order to get 45 to 90 frames per second on modern game, say Metro, Battlefield V, you need to start with an RTX 2,060. This is at 14:40p. If I were to benchmark this at 4 ks, you would need to start at about an RTX 2,080. So there is definitely upward motivation for gamers to upgrade to play the latest esports titles at very high FPS and to play AAA games at the highest possible image quality. But now is where the fun really begins and that's with RTX and Ray tracing. It was just past this past November that Microsoft launched DXR. Like I said, time flies when you're having fun, but it was just about 4 months ago when the gates opened for ray tracing in games. Microsoft launched DXR. This week, the next shoes big shoes drop. Jensen had mentioned it as well, but Epic is announcing that Unreal Engine 4, the number one engine for AAA games is integrating DXR and RTX support in their game engine and it will be shipping to the game to their thousands of game developers in the next couple of weeks. I think tomorrow they're actually going to give a specific date and show some demos. If you didn't notice from the keynote yesterday, real time ray tracing is definitely the next big thing. The demos that you saw were unbelievable. If you aren't convinced then, take a look at the Control demo from Remedy that was posted last night on YouTube. Control is an upcoming game that looks amazing ray traced. Unity also announced that they're going to be Unity is the number one powers about 50% of the world's games. Unity announced at our keynote yesterday that they're integrating RTX support and DXR into their game engine, and they're going to be handing out builds to developers starting on April 4. In addition, most of the first party engines, including Frostbite, Remedy, CryEngine, engine from Crystal Dynamics, 4A Games are all supporting DXR and real time ray tracing. I'm heading up to the game developer conference after this show. My team is booked solid with devs talking about real time ray tracing coming into games. Real time ray tracing is the most exciting technology we've rolled out. We've seen the best response that I have experienced at NVIDIA from developers to implement real time ray tracing in their next generation games. We're tracking, let's say, about a dozen games that are coming later this year, early next year to implement real time ray tracing, and those are the ones that we just have visibility into. With the release of Unreal and Unity, I expect that to accelerate. As Jensen mentioned, ray tracing is a software algorithm. It will run on CPUs. It's accelerated by GPUs, but we designed RTX to further accelerate to make real time ray tracing possible in fully interactive games. If you look at Metro, we can run Metro on Pascal. And I don't know if you've seen some of the coverage, but we are we announced that we're going to be adding DXR support to our drivers for all of our GPUs, so gamers can play with it. But the fact that it'll run doesn't mean it's going to run interactive. In fact, with Turing RTX, with RT Core and RT Core plus DLS X, RTX will accelerate ray tracing over Pascal about 3x. In order to get fully interactive ray tracing, you need an accelerator, you need a next generation architecture, and that's what GeForce RTX was designed for. Arrow should stop at the green bar, 3x. So we're super excited about the future of RTX. We're super excited about the momentum behind real time ray tracing. Game Developer Conference this week is, I think, when it all really kicks off in earnest, and we're going to see a ton of momentum coming out of the show. We talk about notebooks now. Students want mobility. Students want the game. Starting at CES, we launched RTX Coming 2 notebooks and we launched the next generation of Max Q thinner, lighter, more powerful notebooks than the world has ever seen. MaxQ has been driving the growth of the notebook business for the last several years. This year for the last several years, we estimate the OEM end market revenue of notebooks to be about $12,000,000,000 It's grown about 10x in 5 years. This is what the OEMs are seeing in terms of their total revenue and revenue growth from gaming notebooks. It's easy to think of a gaming laptop as This year, we This year, we expect the number of notebook of Max Q notebook models of thin and light gaming laptops to double to about 45 models. And within each model, there's going to be multiple GPU configurations. So you could easily double or triple that in terms of different notebook configurations that will be in the market this year. Max Q Thin and Light gaming laptops are taking over and driving the growth of the laptop market. Jensen also mentioned GeForce NOW at its keynote and the next 1,000,000,000 gamers that we can address. Today, we have 200,000,000 GeForce gamers. If you look at the entire population of gamers playing on underpowered notebooks, playing or want to play on underpowered notebooks or Macs, can reach another 1,000,000,000 gamers. GeForce NOW has been around for about 2 years now in earnest. We've been perfecting the experience, quality of service, number of games on boarding, got about 500 games now available on GeForce NOW, 15 data centers, 300,000 monthly active users, about 1,000,000 people on the waiting list because we can't service them. The demand among gamers who are on underpowered PCs appears to be pretty huge. Within our current monthly active users, about 90% are playing on PCs that are underpowered, do not have GeForce GPUs in them. What is GeForce Now? GeForce Now is a GeForce gaming PC in the cloud, give users access on low end clients to a high performance gaming PC in the cloud, fully interactive gaming, and we're rolling out VR. It's a simple game launch. We are not a store. It's a PC in the cloud. It's a simple game launch. You launch a game off your desktop just like you would any other game and voila, it's playing in the cloud. We offer an open ecosystem, publishers, developers, direct to gamers. We don't intermediate. We are not a store. The stores, the publishers, the developers 100% of their revenue. We are a service. So scaling out, we've had a ton of interest. We've seen a ton of interest from telcos who are interested in interactive gaming and VR. It's a perfect use case for 5 gs and it's a perfect value added subscription to their broadband customers. So we created a program called GeForce NOW Alliance. And what GeForce NOW Alliance is, as Jensen had mentioned, we've developed a server that is optimized for cloud gaming. We're using that in our data centers and we are packaging it up as an end product for, GeForce NOW Alliance. We'll sell a complete server. And on top of that, we will run our GeForce NOW service, license the telco, share revenue as it scales out. This gives us the opportunity to hit markets that we don't currently address, and it gives telcos the opportunity to bring in more value adding customers into their ecosystem. We announced 2 partners yesterday at keynote. SoftBank focused on Japan, bringing their 6,000,000 broadband customers and ultimately 30,000,000 mobile customers and LG U plus in Korea. And as you know, Korea is a big gaming market as is Japan, bringing their 4,000,000 broadband customers, 4,000,000 cable customers and 13,000,000 mobile customers ultimately into the ecosystem. We expect to see the alliance services starting to roll out in the second half of this year. So that's gaming for me. I hope I touched on some of the things you wanted to hear about. Our growth levers for this year, RTX is off to a great start. 40% 46% initial ramp revenue, sell through revenue, pascal to Turing. GeForce laptops, fastest growing game console. It's the way I think about it. Students, gamers, kids want mobility, they want high performance, they want thin and light. Max Q is driving this growth. GeForce NOW, we can reach another 1,000,000,000 customers. We're super excited about the Alliance partnerships. I think our service is awesome. If you haven't tried it, you can log in. I'm sure that Sean or Simona can get you a code to jump the 1,000,000 gamer waitlist. You can check it out. It's really, it really is amazing. The interactivity is will blow you away on your Mac or enterprise notebook. GeForce Alliance then will let us scale out. We announced LG and SoftBank and expect to have more announcements coming over the course of the year. So that's my story for gaming. Look forward to speaking with you all later if you have any additional questions. Thanks so much. I think Jay is up next for data center. Hey, Jay, before you start, I got to make a quick announcement. I got to make a quick announcement. We are in historic grounds. It turns out this cozy room is the location of the world's first GTC Developers Conference. This is how many developers we had. This is how it all started. This was the first one. Anyways, I'm so excited to tell you that. Wow. All right, great. Good morning, everyone. Welcome. It's nice to see you all. My name is Jay Puri. I am responsible for NVIDIA's worldwide field operations and it's a real pleasure to be here. Today, I'm going to talk to you about our data center business. So we had another record year. We grew over 50%. The business is now $3,000,000,000 and the computing approach that we pioneered is just really taking off. Our business is driven by applications And you're at GTC and you can just see the excitement that all the developers have about NVIDIA's platform. In fact, the number of developers grew more than 50% just last year. So the momentum is really terrific. Of course, we are number 1 in deep learning. We are the de facto platform for deep learning training and we are getting real traction in inference now also. In fact, our inference business last year was a few $100,000,000 So things are actually going very well. There was a bit of a pause with some of the large hyperscalers towards the end of last year as they digested some of their big purchases earlier in the year, but that is temporary. The amount of traction we have with them and all the announcements you heard yesterday with Matt Garman here with T4 and NVIDIA's RAPIDS platform now being incorporated into all of their machine learning platforms and so forth. I mean, the amount of stuff we are doing with these customers is actually quite mind boggling. And so I'm sure the business is going to follow as it has to. Okay, so let me talk a little bit about the size of the market. The overall server market today is about $100,000,000,000 and we feel that $37,000,000,000 of that is right for high performance computing as Jensen described it. So about a decade ago, a little more than a decade ago, we introduced CUDA to scientific computing, which was our first segment and of course at this point we have a commanding position in that market, right. All of the supercomputing centers, every major university, all the research centers, they are now deploying NVIDIA's accelerated computing model. And then about 5 years ago, when deep learning came to the front and the hyperscalers like Google and Microsoft and Facebook and all quickly realized that artificial intelligence deep learning was going to transform their business and they needed a fast computing platform and CPUs were just not going to cut it, they all migrated towards GPUs. We quickly saw that opportunity and leaned into it big time. And so we took our CUDA architecture, widened its aperture a little more as Jensen put it, and we had libraries such as CU DNN and so on. And very soon working with all of the framework developers, we had the best platform for deep learning and we are doing really well with the hyperscalers there. And a couple of years ago after that, I think even the traditional industrial companies in automotive, healthcare, retail, financial services, the leaders began to realize, hey, AI is going to transform my business. And so they all wanted to start using deep learning and we introduced DGX, which is a supercomputing appliance that allows you to do AI really quickly, get off to a good start. And we are starting to make real progress in the enterprise now, okay. But this is just the start. As Jensin mentioned, data science is a new workload that is going to have a major impact on all of these segments and it's going to mean that the high performance computing part of the server market is going to more than double over the next 5 years. And we believe that NVIDIA's addressable opportunity there, our TAM is going to be $50,000,000,000 give or take. So we are really excited about that. Okay, let me talk a little bit more about the platform. Jensen did a great job of explaining to you that there's a real difference between an AI computing platform and just an accelerator. I think all of the computer science world has now understood that Moore's Law is at an end and domain specific acceleration is the way forward. So, obviously this is a big opportunity as I pointed out. Many companies want a part of it and so there is all types of accelerators that are being announced. And perhaps some accelerators like FPGAs and so on that would like to be platforms, but frankly, they're pretty far from that if you use the Prada definition as Jensen pointed out. Now look at our platform, right. It is we've been at it for over a decade, 12 to 15 years. And so we saw this opportunity a lot earlier than most companies. And so we've been investing in it for a long time. At this point, our platform is software compatible from the Jetson Nano to the largest supercomputers in the world. We have been really disciplined about making sure that we maintain backward compatibility through all this time as we continue to innovate at a furious pace every year. And so the number of applications that are here is growing at a really rapid pace and this span multiple domains, as Jensen explained earlier, right? So nobody has the maturity of this platform. Just think about the investment. We have made tens of 1,000,000,000 of dollars worth of investment in this platform ourselves and that's just the tip of the spear. It's really about our ecosystem. The 1,200,000 developers that we have on the platform now, all of the scale out partners, if you count the total investment in NVIDIA's platform at this time, it's got to be, I don't know, 100 of 1,000,000,000 of dollars. So it is not easy for someone to come in at this point and try to duplicate this, right? So, we have all the important applications in the domains that we are addressing now, whether it is scientific computing, AI going forward in data science and the performance is incredible. When you have a new domain like AI, it is important to have some industry specific benchmarks that everybody can look at to compare different options that they have. So, Google led an effort to come up with a set of industry benchmarks called MLPerf recently. And it's a very comprehensive set of benchmarks. They did a very good job. They are tough. In fact, we have lots of companies that are part of the MLPerf consortium, but only about 3 or 4 companies could even submit results that met the requirements of the benchmark. And I'm so pleased that NVIDIA was the leader in all six of the important benchmarks and not only that, but we beat the competition by a fairly healthy margin. So, it shows that not only do we have a very widely adopted platform, but it is the most performant platform that is out there for artificial intelligence. Okay, our value proposition. Actually, if you understand accelerated computing, I think you understand why the value proposition is so compelling. We are able to take we are able to accelerate applications manyfold. If you can accelerate applications manyfold, obviously you don't need as many servers. And so if you don't need as many servers, the acquisition cost is going to be less. And if you don't need as many servers, the energy cost is going to be less. I hope you know that in most data centers, the energy cost actually is more over a 5 year period than the acquisition cost. So as a result, our value proposition is extremely compelling. And now, of course, we have machine learning or data science, that's our latest workload and it's a huge opportunity and you can see our advantage in TCO is 80%. All right, let me talk a little bit about our business model, what do we sell? Okay, so we sell we have 2 types of products. We make our own systems, the DGX line of products that goes from about $40,000 to over $400,000 and then it's stacked up in racks, in pods and so forth and we work with our storage partners and our networking partners to develop a complete solution for our customers. And we also take our technology to market through our OEM partners, through our Tesla product line, for example, where Tesla cars go from $1,000 to about $10,000 and also our architecture is available through every single crowd service providers. So, let me talk a minute about why do we do this, why do we have this product line and what is our business model. We do our own systems for a couple of reasons. One reason is, of course, it's all about the full stack as Jensen mentioned to you. So if you're going to innovate on the complete stack, we have to have a reference architecture and that is our reference architecture, right. It's important for us to continue to innovate and move the technology forward. It's also very important for our development partners, all the developers, they have to have gold standard, if you will, for NVIDIA's architecture, NVIDIA's accelerated computing platform. A second reason and just as important from my perspective is it's a great tool for business development. We have to go and create these markets, which means we have to go and engage with all of these lighthouse customers when we are first getting started and we need a way for us to be able to engage with them. And having our own product line that we can go in with and work with them on creating the first solutions and so forth is very important. So that's another reason why we have our own set of products. But I have a small sales force and really we want our platform to be ubiquitous. So the real go to market strategy actually is through our OEM partners and through our cloud service providers. And every single OEM in the world, every single system builder in the world is now using our platform to build their solutions and we are available in every cloud provider, okay. Finally, we have NGC, the NVIDIA GPU Cloud, our software hub and that sort of unifies everything because it is available, our accelerated applications and know how and so on is available there and that can be deployed whether it's on our systems or it's on systems of our OEM partners or even in the cloud. So that is sort of what we sell and how we sell it. Okay, a minute on our go to market strategy, right. So of course, the foundation is our platform. That's where we add all the value and that's what we are really proud of. But because it is about domain specific acceleration, it's not about general purpose computing, what we do is we go and pick those domains. So we go into vertical industries, whether it's transportation or healthcare or financial services or retail or what have you. We look at those industries, we go meet with the leaders in those industries, We try to understand what are their pain points, what are the applications if you could accelerate would have a major impact on their business and then we work with them hand in hand and see what we can do about accelerating those and our track record of course is very good. So that's kind of how we go to market. We go and look at specific domains in specific verticals and then we go and accelerate those. Once that is done, then we have a tool such as we have our Deep Learning Institute, whereby we can use that capability to go and explain that to all of the other customers in that industry that, hey, we have a fantastic solution for you now. And of course, we spend a lot of time enabling our partners and our ecosystem other ecosystem players to allow us to scale out then and really go and make these solutions available widely. So that's it. Pretty simple, but actually it's a lot of hard work, but it's fun, it's a lot of fun. Because it's fun when you can offer that kind of a transformative solution to the industry. The types of discussions that you have with people, it's really you can just see the joy that we are bringing to people and how impressed they are with what our platform can do. Okay, let me just go back very quickly into the 3 segments that I talked about, Scientific Computing, Hyperscale and Enterprise, and just tell you why we think that our opportunity in each of these markets is actually growing very quickly. So, scientific computing, that's of course our beachhead, that's where we got started and we are very proud of the science that is possible on our platform. Summit Supercomputer is the fastest supercomputer in the world, fastest supercomputer in the U. S. And there's already such fantastic science that is being done on it. I was just reading some articles about what they're doing about more some cancer research, some medical other medical research around addiction and so on, nuclear energy, fusion types of things for renewable energy, weather prediction, just fantastic work is already being done on these supercomputers. We also have the number one supercomputer in Europe with the Piz Dan and last summer when Japan wanted to have a really great AI supercomputer, they wanted to have an AI supercomputer available for all of their industry to be able to use, they chose NVIDIA and so we power that supercomputer also. The number of applications that we are accelerating is going up. We accelerate the top 15 applications that are important in high performance computing and scientific computing, but at this stage, we actually accelerate over 600 applications. So from 450 last year to over 600 applications. And at this point, almost all of the applications that account for the vast majority of the cycles in supercomputing centers are accelerated by NVIDIA. The other reason that this market is actually going to become even larger is because it's not just about simulation anymore. People want to do AI at the same time. Because as you can imagine, it's all about getting to your answers fast, getting to scientific results fast. And if you can use AI to predict where your simulations are going, you can get results faster and so forth. So in every field, an important domain of AI, whether it's precision medicine, renewable energy or all this climate, weather science that's important, they are now doing both simulation and artificial intelligence. And again, because we don't have an accelerator, we have a domain specific accelerating architecture. When they're using our product, they can just do both types of workloads simultaneously, no problem, and it grows the overall TAM for us. All right, next is hyperscale. We are the leader in deep learning training, everybody knows that, but sometimes I get the question, is that saturating? Actually nothing could be further from the truth, just look at the numbers. The amount of petaflops per day of training that is being done is just going straight and up to the right. And not only that, but the complexity of the networks that are now being developed as people want to do more and more sophisticated AI is increasing. So today, when people are benchmarking, training, it's usually MXNet-fifty, which they're looking at. Well, that's about 25,000,000 parameters, okay. But the interesting AI that is going to happen is around the AI assistance and so on. And for that, you need networks like automatic speech recognition, Jasper, that's 200,000,000 parameters or BERT for natural language processing, that's 250,000,000 parameters. And I'm sure we're just getting started. So there is no question that the need for training is going to just continue to increase. In fact, you can just look at the Ku NN downloads and they just continue to go up. And as I mentioned before, MLPerf is proof, if you needed any, that there is no better training platform than NVIDIA's. And we are available in every single hyperscaler. But I still believe that the big opportunity for us in addition to training is inference and we are starting to get traction, but I think it's just going to accelerate and let me tell you why. In the past, when people were doing inference, a lot of that was images and it could be done in batch mode on idle CPU cycles at night. So for example, if you have these Google cars roaming the streets, mapping an area and later on, they want to label their maps with the names of businesses on the route, well, they can do that at night. There's no urgency to that. And if there are plenty of idle CPU cycles, they can use that. But if you want to do the types of interactions with AI assistant, like I think Jensen demonstrated yesterday with an example of that with Microsoft, well then it's a totally different story. You ask a question, the first thing you have to do is you have to go from speech to text in neural network for that. Then the text, you have to have some natural language understanding. What is the meaning of this text? What is the context? You need some kind of a natural language processing network of your run inference on that. After you've done that, then you will do whatever is needed, get a result back or search or whatever. And once you've done that, then you may display it as an image or you may need to then go ahead and take that and put that back into speech. Okay, you get it back into speech, but then that speech sounds like a robot. You don't want that sound like a robot. So you need another network to make it sound more natural sounding. So the complexity and plus, okay, not only do you have to do all this stuff, you have to do this stuff in a few milliseconds, so that it's useful. Not going to wait for idle CPU cycles to do that, right? So you need GPU acceleration for inference going forward in a big way as AI becomes more sophisticated. And so inference is going to be a big opportunity for us. And here is examples of many companies that are already using NVIDIA for inference. ByteDance, I don't know if you know TikTok, it started in China, but now it's everywhere, it's short videos. They're just exploding, maybe one of the fastest growing company. They use us for video moderation, make sure that the content there is safe and nothing that we wouldn't want to have on there. PayPal is using us for fraud detection, billions and billions of transactions, right? But they by using our technology, they can reduce fraud by 10%. By the way, as I was talking to them, it's pretty interesting. The types of fraud that people think of, it's pretty amazing. All kinds of collusion between buyers and sellers and fake stores being set up and whatnot. So people can be pretty creative, but they can find it out now in pretty much real time using our technology and they can save 10%. Not even can they save 10%, they said they can use 8x fewer servers. Again, the TCO is just pretty incredible. And then, I don't know, WeChat, Tencent, I mean, this platform is just absolutely incredible, does everything. And a lot of the inference on that, including, by the way, if you end up using WeChat with somebody in China, it will do all the natural language understanding and provide subtitles in your native language and the results are really, really great. So there's a lot of inference going on already, but as people do more sophisticated inference, it's going to be I think it's going to be a very big opportunity for us. And then finally, in the hyperscale and in the enterprise, as Jensen said, data science is the big opportunity. It is the unicorn that only I think NVIDIA's platform is going to be able to address in the proper way. And already, all of the cloud providers, whether it is AWS SageMaker or Azure ML or Google ML, they have all adopted our rapid acceleration into their platform and it's being it's going to be available to their customers. So hyperscale, I feel very confident that our business in this space is going to just going to keep growing. Okay. So finally, the 3rd segment is enterprise. This was new and first we started working with people. As I said, the leading companies is starting to work in deep learning, but then we realized, hey, these guys are actually already doing a lot of data analytics. I mean, everybody we've been talking about the digitization of the enterprise and they have they all know that to be competitive in this space, they have to collect data about their customers, about their suppliers, about their processes and they have to get business insight from that in this extremely competitive world that we operate in. And so far, there is a lot of open source software, right, for doing the data preparation, the ETL part of it and then Pandas and Scikit Learn and git learn and so on to accelerate the models and then display them in graphs and so forth. So there was a ton of open source software already that these people are using, but frankly, they were just not able to be effective enough. And again, the reason is tons of data, but by the time you actually prepare it, Jensen gave the examples yesterday about the I think it was the Verizon network, whereby it takes 8 days to actually massage the data. So by the time they do that, it's already not current enough before they can even run the models on it and so forth. So it really needs acceleration. It needs NVIDIA's accelerated computing platform and that's what we've been doing. We've been working with all of these open source, the whole open source community, all these algorithms and so on and making sure that they can all be sped up using RAPIDS, so that you can actually work in more of an interactive way, if not interactive, at least get results in a couple of hours rather than days months, so you can really improve your decision making and start making a real difference in your enterprise. And so data science is going to be huge. So that is the big opportunity for us in the enterprise space. I have a few examples here of some of the work we're doing in deep learning. There is great deep learning work being done today. Continental, in the automotive industry, for example, they're a big Tier 1 supplier to almost all the major car manufacturers and they are of course embarked in trying to build self driving cars, a great partner of ours and they are using lots of DGXs to do everything from the data factory, deep learning training, simulation and so forth. We have a great relationship with them, whereby not only do they buy our products, but we help them setting up the end to end flow for doing building these networks for self driving cars. And that's just one example of, I don't know how many companies in the automotive industry that we are now engaged with. And similarly Siemens Healthineers, they are a leader for medical diagnostics and they have lots of AI experts. They have about 40 AI applications that they are ready to deploy and they run hundreds of AI experiments today on their DGX supercomputers. And I'm pretty sure that every instrument company is going to need to do that and follow their example. So we have wonderful stuff going on in the deep learning space, machine learning and data analytics, data science, that is the big opportunity. And already we do have some, what we call, lighthouse accounts, early accounts that we are working with to understand their needs and improve our platform and so forth. So Uber is using our GPUs to just to match the supply of their riders compared to the demand from sorry, supply of their drivers compared to the demand of their riders. I'm using these phrases even though that's kind of how they talk about it. I think about customers and drivers, but anyway, they're trying to match the 2, make sure you're going to get picked up at the right time quickly. And they also use AI and sorry, data analytics and machine learning for things like pricing your ride and so forth and fraud detection and all of those things. So, Uber is a great account we're working with now. Walmart is another account that is very excited about our platform. They're using it for things like forecasting. You can just imagine Walmart is the largest retailer out there. The 100 of 1,000,000,000 of dollars of business that they do, they can improve forecasting just by a little bit so that they have less spoilage or something that you'd want when you go to one of their stores is actually is not out of stock, that has an impact of 100 of 1,000,000 of dollars to them. And so they need to do that in as much real time as possible. Today, they definitely use machine learning for that, but it's days behind. They don't have real time information and this would make so much difference for them and they're very excited. Okay. All right, so you can see why it's very evident that our opportunity in all of these segments is going to be larger and larger as we go forward. So, I'm very, very excited about that. But the next thing that we want to work on, we want to make sure it's easy for people to adopt our technology, right? The easier I can make it for them to buy, deploy, purchase, the faster our business is going to grow. So, one of the key elements of that I think is NGC. NGC is really great. So it's the NVIDIA GPU Cloud. We started by having it as a depository for our containers, but now it's more than that. Now we call it our software hub. So, of course, we have now 50 plus containers that has our HPC applications that we've accelerated. It has all of the DL frameworks. It has many of the frame all of the RAPIDS algorithms, etcetera. There are so many different algorithms and we want it to be end to end. And so, this is not a simple thing to be able to pull these applications together, but we make it easy for people to use because we just take it all, use all the best libraries to optimize the full stack and then we just containerize it and put it on NGC Cloud, right. So and that number is just going to continue to grow, make it very easy for people to go get at AI computing, data science computing, okay. But we're not stopping there. We have not just the frameworks and trainers, but we have the training scripts for these frameworks. We even have pre trained networks so that you don't have to start from a scratch. You can use start with these and then do transfer learning on your own data and come up with networks that are optimized for your own work. And then finally, we are even putting some of the key industry workflows up in the cloud for our customers. So, 2 of them around medical imaging, Clara, some of those libraries, train models for that and for Metropolis, some IVA applications and so forth, they are intelligent video analytics. Those models are available in the NGC cloud now. I think this is going to make it a lot easier for our customers to actually start doing real AI work and that will be good for our business. By the way, you can deploy these NGC Cloud, I just want to reinforce, is available everywhere. You can do it on prem or you can do it in the cloud. And in fact, you can do it in any of the clouds there or on prem on any of our OEM systems that are certified for NGC and of course you can do it on DGX. Okay, some of the other things that we are doing to make our technology easy for people to deploy. One is, I mentioned it earlier, these reference architecture partners. When we first got started, we introduced the DGX appliance and we said that's great. We've got the whole stack all optimized and people can get started right away. And then we would find they would put our DGXs in one room and they would put the storage in another room and connect it by a 1 gigabit Ethernet or something. And then they would say, hey, the performance is not very good. And so we quickly realized that we can't just solve the compute part of the problem. We've got to solve the overall data center problem so people can deploy our technology. And we started having discussions with the leaders in storage such as network appliance, pure, EMC, IBM and so on. And the networking companies such as Mellanox, Arista and Cisco and together we have developed these pods, these reference architectures. By the way, the reference, it's about domain acceleration. So and it's about so it's not that you can have one reference architecture that does everything. This is a pretty this is important work and takes some effort. So we have these rep pods for different workloads. It may be a different pod for training versus simulation versus what have you, data science or whatever. So we are working with them on actually putting these pods together to accelerate not just to accelerate at the data center level. And it's pretty exciting to have these that frankly drive you crazy, they'll buy 1 and then it takes 6 months, that frankly drive you crazy, they'll buy 1 and then it takes 6 months for them to prove it out. Now all of that stuff hopefully can be condensed into just a few days hopefully or at least a week or so. And then you can prove it out that, yes, you're going to get this kind of performance improvement in your workload and people the customers are very happy about that. We have some alliances now with the colo data center providers. There is if you're doing scale up computing, there are certain requirements that traditional IT data centers are not used to handling in terms of the amount of power that is required and just the density of the computing and so on, the cooling systems that may be necessary. And so if our customers are having some difficulty working with their IT department, well, just go deploy it in one of these colo centers, right? They are now available and they know exactly how to build this out for you. Finally, in terms of again, Jensin talked about we have 2 types of computing that we are focused on. Early on in scientific computing sector, we've been focused on those capability machines, scale up computing, the supercomputers. But as we go into deployment, whether it is inference in the data center or for data science, I think it's both, as we said, but today, a lot of the people are just doing inference on all of these volume servers that they have, right? So, by making T4 available in all of the high volume servers from these OEMs, we can allow them to do inference and data science right in their current data center and millions, I don't know, 20,000,000 or something of these servers are sold every year. And of course, you have Spark and so on to try as an attempt to make all of these distributed computing environment work as one and we are accelerating Spark. So that's all great. And then over time, I think as the workloads get bigger and bigger and they want to do it faster, people are going to realize, yes, we can do it in the distributed environment with traditional servers and with T4 in them or there are many times when people are going to want data scientists are going to want the fastest supercomputer with lots of memory and so on to go do data science and we're going to be able to address both of those capabilities. So T4 is now available from all of our all the major OEM suppliers and we are no longer limited to just this the capability machines. We also have the capacity machines, the scale out machines, which really widens the market and that's available for us. And it's again, all of this stuff is NGC certified and so we know that it's going to support our platform and all the applications that have developed on it. Okay. So that's it. A lot is happening in our space. The data center market opportunity is a big one for NVIDIA. I'm very excited about it. At this point, there's no question accelerated computing is the path forward. And if somebody ever talks to you about a new ASIC that came on, please remember it's about the accelerated computing platform, it's not about accelerators and I feel quite confident in our position when it comes to that. It is all about the acceleration stack and data science that is the next big opportunity. And not only is the opportunity big, but we are taking a lot of steps to make it easy for our customers to purchase and deploy our solutions so that the business can grow faster. Okay. Thank you very much. Ladies and gentlemen, Good morning, ladies and gentlemen. We will resume our program in 5 minutes. 5 minutes. Thank you. Good morning, ladies and gentlemen. We will resume momentarily. Please take this opportunity to find your seats and silence your cell phones. Thank you so much. Good morning, ladies and gentlemen. Next, we have Rob Chungo with Automotive. Hi, everyone. I'm Rob Chunger. I'm going to talk to you about automotive. I'll give you an update on our strategy. You guys know our strategy in automotive is an end to end platform. It's an open platform and it's for building autonomous cars. So what I'll do in my presentation is I'll give you an update on how that business is growing. I'll give you an update on the market drivers, what's driving the business, what are the things that are important. I'll give you an update on our strategies and what the size of the opportunity is. And then I'll talk to you about our progress, what are the things you can look at to see whether or not we're making progress towards our objectives, okay. So, first of all, in terms of growth, there's a lot of different growth factors that you can look at in our business, but there's a couple I'll touch on. On the revenue side, I guess it was another record year for automotive, but we're on looking at a much, much bigger opportunity. So the thing that I think I would touch on and highlight as something that was very significant this past year. So if you remember last year at Investor Day, we were just launching Xavier. We had announced that Xavier is going to be going out. We said that Xavier was the processor to power the autonomous vehicle, to power the self driving algorithms, to power the cockpit and we were just launching it. So you know that as part of our open platform, we work with literally hundreds of companies, sensors, Tier 1s, car makers, truck makers, all sorts of different vehicles. And during this past year, we basically went from 0 to over 80 companies that are now building on top of the Xavier platform. So this is, I think, one of the most significant things. The Xavier platform of course is software compatible to the previous platform, yet people who are looking to drive an autonomous vehicle and get it out soon made the move from our previous generation to the Drive Xavier platform and that's really important. Another thing that's important if you look at this past year is that it's not just that people started developing on Xavier, but the different kinds of vehicles that are now being developed using Xavier as the base platform. Of course, Toyota this past year and you saw the announcement yesterday, but separately, just individually, Toyota has selected Xavier as the platform, we announced that. I highlight a few other examples just because they're interesting. Volvo selected Xavier as their platform, but they selected it for Level 2 plus Level 2 plus translates to mass market vehicles. NVIDIA is not only being used or up until then you had seen us in mostly robo taxis or high end level 4 type vehicles. Now they are viewing level 2 plus as an important first platform to engage on to get it out soon and this Level 2 plus is a very high function fully featured autopilot solution that we call Drive AP2X. And there's a number of reasons why Level 2 plus became more important this year, and I'll talk about that. At the other end of the spectrum from Level 2 plus we announced that robo taxis are being built using Xavier as the base platform, Xavier or Pegasus, as we call the development platform for Xavier. We announced this past year that Daimler we're working with Daimler to develop their robo taxi solution. So not just cars ranging from level 2 plus to robo taxis, but also different kinds of autonomous vehicles. This past year, you'll now see that there are forklifts autonomous forklifts being developed on Xavier. There's construction equipment, earth movers now being developed on Xavier. There's last mile delivery vehicles, delivery bots, UAVs, UGVs, a whole world of autonomous vehicles being developed. And that really brings up and illustrates the fact that the world of autonomous vehicles is much bigger to us today than it was last year. It's not just about cars and trucks. We believe that every vehicle will be autonomous. And the reasons are compelling. They're different for every type of vehicle, but in every case, they're not being developed just because this is a new feature that you'd like to add on. There's usually a critical problem or something happening where autonomous vehicles can uniquely solve a problem. So for example, in cars, of course, we are all aware that 3,000 people die every day in the world. We literally have a 9eleven every day in the world due to human caused accidents. In trucking, it's a little bit of a different problem. We live in the Amazon era. Today, there's a shortage of 60,000 truckers in the United States that's expected to triple by 2026. Furthermore, with electronic logging devices that are now required on truckers, that limits the amount of time that they can drive per day and further reduces the amount of productivity that can be brought out into the road. Self driving level 2 plus solutions for truckers allow the truckers to extend the amount of miles that they can drive on the road because the amount of miles where they are not driving but resting doesn't have to be logged as driving time. This is a significant changer, game changer for people in that industry. In the trucking industry, of course, you're feeding the demand for delivery. There's 120,000,000 households in the United States, half of them, 130,000,000, sorry, 65,000,000 of those households are Amazon Prime subscribers. So this is placing a demand on delivery. Mobility services, robo taxis, buses, the cost of ownership, we have an entire generation of people, young adults today who don't want to own a car. The cost of ownership for using services is lower and also the footprint on the planet, the amount of parking lots can be reduced. And then the whole world of autonomous vehicles, It turns out that there's 1,000 accidents, a 1,000 fatalities, sorry, that occur every day related to workplace accidents. 20% of those accidents are specifically related to construction. So this year you saw Komatsu announce that they're using Xavier to develop an earth mover that can look around with cameras placed on the earth mover and make sure that you can detect workers that are around it and make sure that no harm comes to them. Forklifts, delivery bots, tractors, agriculture. In countries like Japan, farming has become a crisis. The average age of a farmer in Japan is 67. The amount of farmers in Japan is going to has dropped in half in the last decade. And autonomous vehicles to help with agriculture and food production are not just a good idea, it's a strategic imperative to develop. The result of all of these things and more is that you are seeing, and this is projected autonomous vehicle shipments by 2025, 30,000 heavy trucks, 750,000 agricultural vehicles, 2,500,000 commercial robots, 1,100,000 UAVs. So the world of autonomous vehicles is much bigger than it was. To address this opportunity, there's really or to address this market, there's really 3 growth opportunities. There's 3 areas where NVIDIA has developed a platform solution and what we call the end to end solution. And the end to end solution really consists of, number 1, you have to build computers that go into the vehicles so that you can autonomously drive. Number 2, you have to train and develop deep neural networks to create the algorithms for those cars, both in the cockpit as well as in the car to drive. And then 3rd, you have to test and validate those algorithms to make sure that the vehicle that you put on the road is safe. These are not 3 individual separate random pieces of equipment. The reason why NVIDIA decided that we will build a car end to end is so that we could deeply understand the problem. And in the process of doing that, we of course learned that all of these things are essential to building an autonomous vehicle. You cannot build or deploy or test an autonomous vehicle without these things. And if we need them, then other people need them. And that turns out to be true. The end result of this is that on the DRIVE computer side, given all of the market dynamics I just described, we have a $25,000,000,000 TAM opportunity, driven in the short term by level 2 plus, level 5. We have a $3,000,000,000 opportunity on the DGX side, just in terms of how many carmakers are there, how many cars do you need to develop algorithms for? Millions of images you have to collect per DNN, 10 plus DNNs that have to be developed per car And then from that, you can do the math. This is only just getting started. Imagine all of these vehicles, more models, more cars, more vehicles coming out, of course, this will just grow for us. And then finally, the testing and validation. The testing and validation really has to do with you need a way to accelerate your testing and validation because otherwise you're going to be spending 100 of 1,000,000,000 of driving miles for 100 of years to test adequately make sure that the car is working. So just based on the kind of engagements we have now, the type of miles that have to be driven, we believe that this is a $2,000,000,000 opportunity for us. At the high level, these are the opportunities. Specifically, there's a couple of things that are really driving the market for us. Over this past year, I think you're aware that Tesla Model 3 became the best selling premium car in the United States. In those cars, in Model 3s, Model Ss, Model these different models, the autopilot function has an attach rate of close to 80%. They are selling that autopilot and generating an estimated roughly $1,500,000,000 of incremental revenue based on the fact that it is an excellent autopilot. It is operating with multiple DNNs, surround cameras, and it has high performance computing that's powering the whole thing. In contrast to very simple ADAS solutions, which can provide assistance, there's nothing wrong with them, but they just simply are not a full function driving autopilot. This is creating a market for a very full featured autopilot level 2 plus So when you look, for example, at our announcement with Volvo of a level 2 plus solution, you notice that this solution is being targeted at mass market. It's not just for a premium car, it's for top to bottom vehicles. We think this is an important market driver. On the training and development side, of course, you have to collect data, you have to label data, you have to train, but not only that, you heard Jensen talk yesterday, you heard Jay talk about the new opportunity of data science. Car makers also collect enormous amounts of data, not just the ones that are about training and developing autonomous vehicles. So they collect data on customer behavior. They do pricing analysis. All of these things we believe are going to be opportunities for us in the data center of the automakers. And then finally, validation. Simulation now is not just viewed as an option for deploying a car. You see increasingly articles coming out now that say that simulation is the key to accelerating the safety and arrival of autonomous driving and we believe that. We also know, if you're aware that Rand Corporation issued a report where they said that, they did a mathematical analysis of what it would actually take to test and validate a self driving car and they came to the conclusion that would be just about impossible. You would have to drive 1,000,000,000 of miles with 1,000 of drivers for 100 of years. So therefore, you need an alternative solution where you can test for corner cases and a lot of the things we announced here at GTC about Drive Constellation are going to be the solution for that problem. So all of these things, the opportunity, the market drivers, of course, form the basis of our products and our strategies. When we say end to end, it means from driving, training and validation. And when we say open, it means that we have a massive ecosystem of 100 of partners that can plug in, they can develop solutions on top of our platform, our customers and partners are welcome to use as little or as much of our solution as they like. For example, let me illustrate. We announced DRiV AP2X yesterday. This is our full autopilot level 2 plus solution. We have 3 Tier 1s that have announced 3 auto suppliers that have announced that they're building on the level 2 plus, 1 is Continental, 1 is ZF and 1 is Deaneer. Those three actually are the perfect example of how the ecosystem has the choice and flexibility to develop on our platform. ZF uses our software top to bottom, not just the DriveOS layer, not just the API layer DriveWorks, but also all the way through into applications. Continental uses part of our software stack. They use our perception and then they supply a lot of their own path planning and a lot of their own parking solution. And then Volvo and Zenuity using Vioneer are developing they develop on top of the DriveOS software layer CUDA, CUDA and TensorRT and then they develop the software stack on their own. Perfect illustration of the difference of 3 different partners building on top of the NVIDIA platform. On the driving side, our solution starts with our platform Drive AGX, with our software and of course, all of the complexity, everything having to do with perception, localization and path planning and all of those break down into a whole bunch of different algorithms and solutions, very, very complex, very compute intensive and an enormous amount of software. Yesterday, you heard us announce, I think we've shown previously that NVIDIA has a world class perception stack based on our artificial intelligence. We've also shown world class localization to HD Map, working with every mapping company in every continent, Zenrin in Japan, Navinfo in China, here, TomTom across North America and Europe. But yesterday, we announced Safety Force Field. We announced the mechanism for doing world class path planning and creating a computationally safe methodology for a car to navigate in a dynamic world with lots of moving objects and then to take that methodology and transform it into driving software that will allow an autonomous vehicle to drive safely. The end result of all of this, together with our tools and then an ecosystem on top of it, makes up our driving strategy and our driving platform, an enormous amount of work. On the training and development side, in the last 2 years, since we first started engaging automotive companies, we went from basically a handful of customers to over 60 automotive companies today that are training and developing using DGX for automotive. This is obviously a significant increase. It makes up collectively in that number. There's 25 carmakers, 15 Tier 1 truck makers, mobility service providers, mapping companies and startups. And by the way, it's not just customers of NVIDIA DRIVE. For example, here at GTC, you can go listen to BMW present on training on a DGX at GTC. And BMW, of course, in their current generation are using Intel. So DGX represents opportunity and a product that the entire world can use to train and develop their self driving cars. And then finally, on the validation and test side, we announced Drive Constellation. And it's really a 3 pronged approach to how we test and validate a car. First of all, we do what we call component level SIL or software in the loop. Imagine that you can take the data that you have and play it back to your computer and then you can do regression testing, you can change things, you can say, hey, let me remove a radar, let me have a camera fail, let's see how the algorithm responds and you can do this in super time, okay. For example, you can have several months of driving that occurs within a fraction of the time. We also allow you to do Drive Constellation Hill or hardware in the loop. So the Drive Constellation box or solution is 2 different boxes. One box that is simulating or synthesizing the world, it is it creates the world. And the other box is where you put your driving computer. It thinks that it's in a self driving car. There are leads that come in that represent sensors. The sensor images and feedback that comes in are simulated. The DRIVE computer drives and sends out actuation signals back to the synthesis box, the simulation box, and as a result, you're now able to test it. When you're driving normally, there's companies that are talking about driving, you drive millions of miles, you know that most of the time nothing's happening. You're driving on 101 and everything's fine, it's a sunny day and you're in the lane and you go. Obviously, in simulation, you can create challenging scenarios much more quickly than waiting for them to occur in real life. So what we show here at GTC and if you have a chance, go check it out, it's amazing. At a touch of a button, we can make it rain, we can make it snow, we can make it nighttime, we can make it foggy and just confuse the bejesus out of the car. So all of this is, I think, essential to accelerating the testing and validation. Now this strategy, which we came up with, was born out of our needs to develop our platform. And as I said earlier, if we need it, then why wouldn't somebody else need it? Now up until yesterday, you might say, how can you prove that or how can you show a validation point that this actually is true or this hypothesis works? And today, the best way I would illustrate it is to just highlight the announcement with Toyota. The Toyota announcement is exactly that engagement model. It is a recognition of the fact that all of these things are essential for the world's largest automaker to recognize that, 1st, we need the computational power in the car to drive the algorithms. 2nd, we have to create the simulations to test and validate it. We have to have the computer, we have to have the development vehicle, and of course, we have to have AI for the AV vehicles. This is NVIDIA's automotive business strategy applied to the world's largest automaker. It is the model for our engagement and it is the end result of what we intended with our strategy. So we're excited to announce Toyota. We obviously believe that this is what's needed in order to scale to create lots of vehicles across all of this different world of autonomous vehicles. And then of course, we look forward to making more announcements in the future. Aside from this, if you ask what are the key things that occurred this year that you could look at that are key individual milestones or accomplishments towards our goals, I would really break it into our innovation, our product milestones as well as partners. So if you look at them, a lot of them I mentioned, Constellation, our simulation solutions, safety force field, which is NVIDIA now moving to the 3rd part of what's required for a self driving car. We've shown world class perception. We've shown world class mapping, localization, HD map, and now we're showing world class solution for path planning. DRIVE AP2X, we believe level 2 plus is now important. I think you'll see a lot of carmakers make decisions on Level 2 plus this year. My route, the reality is that HD maps don't exist everywhere. So where they don't exist, NVIDIA will create a personal map for you. It will be generated by the car based on where you drive so that you can drive safely. All of the things we show 50 Mile Loop Pegasus, we now have taken our graphics expertise and now leveraged it into creating the confidence view so you can trust a self driving car. It's not enough to just have the car drive. The car has to communicate back to you what it sees, so you can trust the car and believe that you're safe. Hyperion, which is the extension of our development strategy from our SDK, we can now put our SDK into a car. The second we make a change on our software stack and do an OTA, you as a partner get it instantly. And that's part of our strategy. Our simulator, TUV Sud, this is if you know TUV Sud, they are one of the safety experts in the world. They certify various processes of developing a car this past year. They certified NVIDIA as being passing a certification for being able to develop a silicon semiconductor solution for a car and this is we're the only semiconductor supplier to be able to reach this. In addition, we are now the only non carmaker company certified to drive self driving cars in China. China, of course, very important not just to us, but to a lot of our customers and partners, global brand companies as well as the local companies in China. And then of course, global mapping. On the partner ecosystem side, you notice I won't go through every one, but you notice that they're grouped into not just cars, but trucks, not just level 2 plus but robo taxis, autonomous vehicles, Yamaha, Komatsu and of course, Chinese companies. All of these are announcements that were made this past year and I believe validate our approach. Okay. So just to wrap up, our strategy is simple. We believe NVIDIA is the only company that is delivering an end to end open platform for building autonomous solutions as evidenced by the things I talked about. On the driving side, we believe the world of AV is bigger than ever. It's not just about cars and trucks, And I've shown you some of the design wins on these new types of autonomous vehicles. It's a big opportunity. And I believe the strategies that I talked about are game changers for a lot of the carmakers and certainly you see some of the evidence of that especially with the announcement with Toyota. For training and development, we're just getting started. Collecting, training and analyzing data are essential for autonomous vehicles. We've now grown to over 60 automotive companies on our DGX business and like I said, it's just getting started. And then finally, on the validation side, DRIVE Constellation Simulation Systems are now available. And the DRIVE Simulation System, like every other part of our platform, is open. We have multiple partners from IPG developing physics models and sensor models to Cognata who is developing traffic scenarios, existing simulation solutions that already exist in the market that can now tie in to our platform because of our open platform strategy. Okay. Thank you very much. And at this point, I'm going to introduce Colette Kreff, our CFO. Okay. Still morning. We're a little bit behind, but we can catch up. I'm going to try and just summarize in total what you've heard throughout the teams, and then we'll take that time afterwards to open up for Q and A. But let's just talk through a couple of numbers. How about that? All right. So another record year. This is actually our 5th consecutive record year in terms of revenue as we finished fiscal year 2019 at $11,700,000,000 and growing more than $2,000,000,000 year over year a growth rate of about 20% fueled by all of our different platforms, which we'll talk about. Our gross margin, also a record in terms of its overall growth and reaching 61.7 percent. Keep in mind, there is still in there, we would have been higher except having to write down some of the overall inventory later in the year. But since the absence of our overall IP licensing, our value added platforms continue to drive our overall gross margin up. Our operating income, also a record year and reaching $4,400,000,000 and growing faster than our overall revenue at 22%. Overall profit, whether you look at overall net income or EPS, growing significantly faster at approximately 35% as well. Now when we think about the market platforms that we just addressed throughout the room, you heard from 3 of them, 4 of them in terms of here, all reaching overall record level. And this is in a view to look at our overall growth rate over the last 3 years and the compounded growth rate that we have seen. 1st, starting with gaming. Gaming, in terms of its long term growth rate, has been growing 30% over this period of time, even this last year growing 13%. But as you think about this going forward, you should think about the overall gaming as being an overall entertainment industry. Fish was up here talking about what you should see in terms of the growth drivers as we move forward. RTX is here, a new overall architecture to take us forward for the next couple of years, and we now have a full portfolio of RTX available. I talked quite a bit in terms of the overall ASPs and how they have overall helped our portfolio in the past, but as you can see, there's even more opportunities when we move forward. The overall unit growth in terms of gaming is definitely there as well, as we think about the refresh opportunity of our existing gamers. And as we know, there are more gamers coming on board every single day. Those in terms of starting at a younger age and also staying in terms of longer of well in terms of their 40s. So this in terms of will continue as we hope moving forward. We also talked about opportunities and things that we have seen most recently, the growth of overall notebooks and the use of notebooks and the mobility to continue their overall gaming experience. Additionally, we talked about streaming gaming. And now we have an opportunity to again address this very wide and growing market in a new form factor and for gamers that have not actually been in touch with it. Now, pro visualization. Pro visualization, also extension in terms of the graphics that we see on the gaming side, but taking that to the overall enterprise. We've seen an expansion of this market as well, largely focused in terms of the mobility of the overall workstations, provis 15% over the last 3 years, growing quite nicely. But you also have RTX coming to overall Pro Visualization. You also have heard in terms of yesterday our focus in terms of the creatives out there and how they can improve the overall rendering process with ProVis. Data center, a business over the last 3 years has pretty much almost 10x increase. Just 3 years ago, this is a $300,000,000 business and we're now approaching 3,000,000,000 dollars I think the whole day today as well as yesterday was really focused about the breadth and depth in terms of the overall solutions that we have for overall data center. That means in terms of focusing not only on supercomputing, focusing on high performance computing, something that we've been working on for 10 years, but the addition of hyperscales over the last couple of years, but now the growth that we can see in terms of the enterprise. That focuses on many different types of workloads, focus in terms of deep learning, which you know is very well by in terms of overall training, but also what we have been able to do in terms of expanding to overall inferencing, our growth in terms of high performance computing and adding overall AI and acceleration in there as well. But then lastly, we're focusing on many of the different workloads that the overall enterprise uses in the expansion of the market from data scientists to the overall focus in terms of rendering as well. Automotive, on the surface, in terms of we're just getting started, we're still looking at a 3 year CAGR of 26%. That 26% is largely due to our base of overall infotainment systems. But over the last couple of years, you've seen us also grow in terms of incorporating AI within terms of the cockpit and our initial overall work in terms of what we can do for autonomous driving. This is going to be broad and far in terms of where we can actually address the market using our solutions in terms of automotive, not just thinking about what will be inside of the car, but what will be in their data centers and what we will do to help them as they continue to have these cars on the road in terms of the testing, the validation and other pieces. So again, our overall portfolio, all in terms of in growth opportunities as we move forward. Our gross margins. Our gross margins continuing to grow over this 3 year period of time and our value added platforms continuing to be the most important part of our overall gross margin and what has driven that. We'll talk about this further in terms of the need of overall software in terms of our platforms to bring them to market to allow people to overall use that. But as you know, the software is not necessarily included in terms of our gross margin that will be incorporated in terms of our OpEx. So overall growth in terms of our gross margins and definitely an opportunity to continue overall growing. So we broke out here our gross margins in a slightly different view in terms of our overall gross profit. Where do we get the majority of our overall gross profit? More than 70% of our overall gross profit stems from gaming and overall data center, which obviously takes up a good portion of our overall business. But keep in mind, one of the highlights that we talked about on our last earnings calls was the impact of inter and intra overall segments in terms of their mix is the largest driver in the near term of our overall gross margins. Mix both in terms of between our overall segments as well as in our overall segments. The black lines here indicate in terms of the ranges that we can see based on the portfolio that we could sell in those 2 major overall segments. So these overall drive our gross margins as we continue to build a larger and larger a larger and larger proliferation of products in terms of the data center, as well as the different overall gross margins and ASPs that we have in terms of our gaming business. Operating expenses. Our operating expenses business, our operating expenses here grew about 27% this last year, trying to keep up with the growth that we have in terms of our product portfolio. Very well structured overall OpEx because we can have an overall architecture consistent across, and that unified architecture allows us to be quite efficient in terms of the amount of spending that we need to do. Our outlook for fiscal year 2020 as we move forward is a slightly lower rate in terms of what we had seen in this last couple of years. We're expecting about a high single digit growth rate or a little bit over $3,000,000,000 $3,100,000 overall growth. Our operating leverage. So we talked about this a bit in terms of what we have seen in terms of the leverage that we get from having a single overall architecture. Just 5 years ago, our engineers that we had were mostly focused in terms of our hardware, meaning we had a larger organization in hardware than we did in terms of software. As you've seen us talk about the overall software over the last couple of days, you'll see now in fiscal year 2019, we have a larger percentage of software engineers, a significantly larger amount of overall software engineers than we do overall hardware. When we think about our R and D, therefore, by those platforms and starting at the bottom in terms of the underlying architecture of the GPU architecture, that makes up 40% of our overall hardware excuse me, our overall R and D costs. Our software layer is therefore about 30% of the overall costs as we string that across all of the different GPUs and all of the different systems that we have. On top of that, we just have a small percentage, about 25% that allows us to go industry specific, market specific in terms of building out our individual solutions, whether that be for automotive, whether that be focused on AI or whether that be focused on in terms of what we need for graphics as well. Our operating margin expansion has been focused on this unified model. It allows us to overall expand our margins quite nicely over the last 3 years and continue to effectively invest in our businesses without having to worry about the overall margin increase in. We'll probably see this continue as we go forward as well as we look at this as a very key area for us to focus in terms of growth. Our cash flow and overall cash balances. Our cash flow has grown quite about 3x over the last 3 years, and we're reaching about $3,000,000,000 or $3,100,000,000 of this last year. That's allowed us to produce an overall cash balance of $7,400,000,000 by continuing though with our overall capital return program. Our capital return program is an integral part of our overall shareholder value in delivering. And since 2013, we've delivered more than $7,000,000,000 to shareholders or approximately 70% of our free cash flow. What this has allowed us to do in this last year is we started out the year with a little bit smaller in terms of capital return. We initiated our intent for capital return for the new year and started that at the end of fiscal year 2019. So what we have remaining in terms of our intent for fiscal year 2020 is about $2,300,000,000 to return to shareholders over this period. Where and use of our overall cash. As we look backwards in terms of 2019, very in line with where we had talked about the last time we had met, we'd focus primarily in terms of investing back into the business. You can see us with $2,800,000,000 invested back. We focused in terms of also CapEx. A lot of that CapEx is focused on our engineers and allowing them to give the tools, the supercomputers that they need to build in order for them to eventually sell them. But also our focus in terms of the capital return is the key areas that we focused on. As we move into fiscal year 2020, fiscal year 2020, you'll see about the same side of overall OpEx, a little bit higher, maybe about $100,000,000 to $200,000,000 more. You'll see about the same amount of CapEx of approximately about $600,000,000 focused not only on our internal engineers, but also in terms of the facilities that we need. But you'll see a large amount that we'll be able to take the cash that we have on the balance sheet to execute our overall transaction for $6,900,000,000 We'll continue with our capital return and finish that out as well as the use of our overall cash. Highlighting here, the title says, our outlook remains unchanged. We're in the middle of Q1. Just to remind you that our Q1 was not necessarily about a normalized and in terms of overall returning back to where we believe we have in terms of the growth opportunities in front of us. It's $2,200,000,000 in overall revenue. We are still working through the excess channel inventory that we have in gaming. We indicated back in November that we thought that would take about 1 to 2 quarters to work through. We're on track and we feel confident by the end of Q2 that we will be completed with our overall excess inventory that we have in the channel. You've seen the initial signs of that as we've continued to start selling in our newer platforms into the market from the 2,060, the 1660 and the 16 60 TI. Our overall gross margin for the current quarter is at 59%, which is up 300 basis points from where we just finished this last quarter as well. Our operating expenses will remain flat with last quarter. We'll see that slightly uptick in the next couple of quarters as we go, but that's what you'll see to get and reach that overall growth rate for the full year. We get questions quite a bit that says you're often giving us overall full year guidance on overall OpEx to help steer us on something that you can definitely control. We provided our full year in terms of operating expenses in terms of looking at high single digit overall growth over the prior year. But we also took this opportunity to provide full year revenue range of overall guidance. We look at that to be flat to slightly down. The flat to slightly down was to help the teams understand what we saw in fiscal year 2019. We took this opportunity after overall cryptocurrency to find a quarter that was not tainted with cryptocurrency to come up with what we believe is a normalized run rate for overall gaming. That means we took Q2, Q3, Q4 as well as our Q1 guidance and looked at that in terms of the overall desktop business and concluded on average we'd look at about a $900,000,000 quarter. On top of that, we have our overall console and notebook business, which equates to approximately $500,000,000 That's a $1,400,000,000 normalized gaming baseline for us to start. And again, remember Q1 doesn't necessarily reflect our overall normalized as we're still working through that excess inventory. But that allows us as we move forward to grow from this point forward. It allows us to look at the back half of the year as reaching some of the growth potential of the great opportunities that we have produced today. So that's what we have in terms of our full year overall guidance for revenue. Mellanox. We're excited to announce that we have signed an agreement to acquire overall Mellanox. This is part of the overall transaction summary and the key points of that. We will purchase it for $6,900,000,000 in overall enterprise value. We expect this deal to close at the end of our overall calendar 2019. And right now we will work through the overall regulatory approvals that we need in terms of in the U. S. And overall China. We're excited to bring the company on board and we'll be working now to get to better understand how we'll overall integrate them forward. But again, we'll have to wait in terms of the overall regulatory approval. At the time that we close, we'll have a discussion in terms of what we expect in terms of guidance afterwards, how we will incorporate overall Mellanox in terms of our reporting structure. Okay. That was our short summary. And we are here for Q and A. I'm going to invite Jensen up here, and we will open up hopefully turn on the lights out here because right now it's a little dark for us to take questions from the group. Is it possible for us to turn on the lights so we can see their eyes? I just see they are much Thank you for the presentation. Toshiya Hari from Goldman Sachs. Hi, Toshiya. Hi, Jonathan. In one of Jeff's slides, I think you showed the trailing 5 year CAGR for the gaming business, both in terms of units as well as ASPs. It was encouraging to see the ASP number, I think it was 14% accelerate from what you had showed last year. More importantly, considering all the things you guys talked about in terms of the esports momentum, the Max Q initiative, the traction you've seen so far in Turing, how do you think about the next 5 years for that business, both in terms of units and ASPs? And related to that, does Intel's intention to reenter the market over the next couple of years impact how you think about or how does that impact your thought process, if at all? Thank you. I'll answer the second one first. We have to pay respect to all of our competition. I mean, we stay alert and we compete with we've competed with 120 graphics companies in our company's history. At one point in time, we competed against 30 5 at the same time. And there were large companies, there were small companies. And so we're quite adept at competition. And this is you're looking at a company that's incredibly focused and incredibly intense. And from the leadership all the way down, there's just so much technical depth and so much passion for this business, I think we're going to remain quite competitive. But nonetheless, we always should stay alert. In terms of growth rate, here's the way I think about it. There's a couple of there's some numbers that should inform us. On the one hand, it is recognized that the PC is a gaming platform, a host for a gaming platform and that GeForce is essentially a game console. A game console has a reasonable price point in people's head of somewhere at the end of a life, at the end about $300 and at the beginning around $400 to $500 That's kind of an ASP in the head of a gamer. Does that make sense? If you're a gamer, you're going out to buy a game platform to play games. In the case of a PC, because it's a good host for the game console, they can upgrade that host several times with a new game console. So every couple of years, they could buy into a new GeForce and they can imagine paying some $300 to $500 somewhere in that range for something that delivers performances much better than a game console would. A very logical, sensible thing for them that informs it. There's a couple of other ways to inform it. Unlike a game console that's largely for playing games, PCs could be used for eSports. And there's 2 types of people not 2, types. There are many ways that you can play sports. You can play sports because you enjoy it and you can play sports because you want to win. And I think that another way to think about ASPs is for the people who are athletes or aspirational athletes or they just really love to win. They need to have better gear. And that's one of the reasons why you see in esports, the high end GPUs are like 2080 TIs. And they want 2080 TIs because they want to run, never missing a heartbeat at 120, 150 frames per second. Many gamers can click 300 clicks a minute. And so when they click, when they click, they want to make sure that they get a shot off before the next person. And so at that kind of frame rate, you're not going to miss a click. And so this buying the best gear is another reason for that. The rest of it is production value is increasing all the time. Max Q increases ASPs. Max Q increases ASP because you're using higher end GPUs running at a much lower voltage, much higher end GPUs running at a much lower voltage to deliver a great performance. Max Q's great innovation is really about running using silicon in a way that is about running it slower at the most energy efficient point. MAXQ increases ASPs as well. So production value increases ASP, MAXQ increases ASP, competitive gear increases ASP, and those kind of factors don't play into game consoles. And the game consoles, that sensibility provides for me, I think, the long term floor. So I don't know if these numbers all help you, but it's kind of in that space for us. And that's those dynamics is what's causing ASPs to grow over time. Yes, thanks. Aaron Rakers with Wells Fargo. Great presentation. I think one of the most interesting things that we heard is this idea of revenue sharing, this GeForce NOW alliance. So I'm curious, kind of first question, how do we think about the proliferation of your partnership ecosystem? Or how are you thinking about it in terms of the service providers? And can you help us understand the attributes of the revenue sharing model, how we should think about that from a financial perspective? And then one real quick follow-up question. Any updated color on your visibility on the data center side would be helpful. Thank you. Sure. Every country has a different telco. From many countries in Eastern Europe to Western Europe to Asia, Southeast Asia, Latin America, India. This is the first time we've been able to create a game platform that could scale out to those regions, the other 1,000,000,000 gamers. Most of the gamers we've been able to reach are in the Western and China. But there are so many emerging countries that would love to have access to PC gaming. PC gaming is particularly great because it's free to play. It's social. It's easy to access. It's open. They want a PC anyways. They need a PC anyways. There are a lot of characteristics about PC gaming that makes it vibrant and unique. Using the GeForce NOW alliance, we can reach them. You buy a server from us, and then we operate the network on top of it for you. You buy the server from us, we operate the network on top of it. We take in terms of relative to the when we go into a subscription model, when we go right now it's in beta. When we go into a subscription model, say out of a few dollars, call it $10 a month of subscription fee, maybe they'll keep more than half and we'll keep less than half. And the reason for that is because they bought the server, they're operating it, they're running it on their network. Does that make sense? All of the capital investment is theirs. On top of it, we're bringing the network. We're bringing the service. We're developing all the software. We're operating it for them. We're enhancing the QoS. We're onboarding all the games. We're doing all the marketing because NVIDIA is the gaming platform. And so they get to benefit from it as well. One of the things that's really great for them is in order to capture gosh, that's a terrible way of describing it. In order to win a new customer, the economic benefit, as many of you know, is quite significant in lifetime. And so for them, this is a pretty fantastic way to differentiate their service over somebody else's service. How many the way that we see it is we'll probably enter into, at least one of these relationships per country. And for the larger ones, maybe 2 or 3. So this is quite a scalable approach. That's one of the reasons why we built the whole stack. We could do this. Nobody else on the planet can. We built the whole system, architected the whole server, developed all the software, everything is in one and everything is in one shop. And then of course, we've been operating the service now for a couple of years, and we're getting quite good at it. You had a second question, data center visibility. Our data center business is in a grid in my mind. There's high performance computing, There's high performance computing. There's CSP for training, CSP for inference, CSP for cloud, and now enterprise high performance computing, enterprise high performance computing, for example, data sciences. Cloud computing, all the things that we do on CUDA today. Deep learning, you know very well. Inference, Jay talked about. Last year, we kicked it off. We're doing fantastic. And then supercomputing, you know very well. So that's one way to think through it. And then you have all of the go to markets by industries, and you overlay that across. We have we monitor the intersections of this for every one of those grids because how they use it and how they go to market is different. Jay told you our way of going to market basically several ways. One is, of course, direct sales to the cloud service providers. 2nd, basically a high performance convergence, hyperconverged, high performance computing solution. We call it reference architectures, DGX pod reference architecture. We also go through the market through enterprise partners. And so we have all these different ways of going to market, and we just track the pipeline for each one of those. Some of those, we get better visibility and some of them, we get lesser visibility. For example, last year, we had a little bit less visibility in the hyperscale data center because they in retrospect, we all realize now, they bought too much capital earlier in the year and they had to really, really slow down. We didn't know about that at the time. And by the time we found out, it was well into the quarter. And so some areas, we have less visibility, but we try to have as much of a pipeline as we can and monitor the pipeline on a weekly basis. So we feel pretty good about the year. Yes. John Pitzer with Credit Suisse. Thanks for the presentation. A couple of questions. One kind of near term, one longer term. On the near term front, you spent a lot of time yesterday and today really focusing on your investments in software, platform and ecosystem. There's one of your competitors that places a bunch of emphasis on process technology and line with nodes. Would love to hear you kind of talk about where that sits in kind of your quiver of IP and maybe talk about the path to 7 nanometer for you? That's the near term question. And I guess longer term, last week you clearly demonstrated that you think interconnect is going to be very important going forward in data center architecture. Wondering if you can make the same sort of comments around memory, because clearly there's another one of your competitors who's looking at memory and persistent memory as perhaps a way to really lower the TCO. How do you view that as a competitive threat? And what could you do on the memory side of things to help out? Yes. You don't hear us talk about process technology, packaging technology, memory technology. And the reason for that is even though we are world class at using it and oftentimes the earliest, For example, 3 d packaging, the world's first is SXM, the largest chip that the world makes, HBM. We used it before anybody else. The reason why we don't talk about it that much is because we are just as good at buying all that stuff as anybody else. I don't find it particularly differentiating to be able to buy 7 nanometer. It's available for anybody who wants to buy it. They want to sell it to you. And so that is not a point of differentiation to me. What is a point of differentiation is architecture efficiency. For example, the fact that 2,080 TI or 2,080 or Turing is so much more energy efficient compared to somebody's 7 nanometer GPU It's shocking to everybody, but not to me. Not to me. That's the whole point. To be able to use something cost effective so that we could and cost effective, cost efficient and get the most architectural innovation out of it, that's what we hire our engineers for. TSMC hired their engineers for building 7 nanometer. Our job is to get the most efficiency out of any silicon that we purchase. And our goal is to be able to deliver the best energy efficiency, the best performance, the best functionality at any given point in time. Turing is just crushingly good. Just got to measure it. It is that good. And that's one of the reasons why it's off to a great start. In terms of the data center, where you see us really differentiate is, of course, we buy all the best. We're one of the world's largest consumers of HBM2. In fact, we are the world's largest consumer of HBM2. We're the world's largest consumer of 3 d packaging at TSMC, COWAS. We ship more 3 d packages than anybody. We just don't talk about it because our customers don't care. What they care about is the functionality they get, the efficiency they get, the performance they get, the TCO they ultimately get, that's what they care about, and that's what we focus on. In order to overcome the slowing Moore's Law, in order to overcome it in a dramatic way, and I don't mean improve it by 10%, If you want to overcome it by X factors, which is what we're about, if you overcome CPUs by 10%, you might as well just wait for the next CPU because accelerated computing requires software optimization. You would only do so if there's an X factor in there. And I mean 10X factor because it's a fair amount of work. That's 600 plus applications, all of those frameworks, all of those deep learning neural network models we now accelerate, engineers worked on it really, really hard, ours, theirs, the ecosystems, everybody working super hard. If it wasn't because of the pervasiveness of CUDA, nobody would lift their finger to do it. And so now that they've done it, they want to achieve the promise that ultimately accelerated computing delivers, which is 10x, 15x, 20x, 50x. That's how you move the needle. And you can't do that by stacking up chips differently. You can't do that by just buying a special node. You can't do that by just getting memory, pay a little premium for it and do that with memory. You've got to do that only in one way, the good old fashioned way, which is software, rewriting, refactoring, coming up with new algorithms, good old fashioned software. That's where the computational magic of our company is. It's well known in the industry, we have a very large team of computational mathematicians. When you see all of those breakthroughs in computer graphics or you see all these new algorithms in the libraries that sound strange like cuGraph and cuBLAS and cuFFT. And well, it turns out inside it is an enormous amount of expertise in refactoring mathematics in such a way that it's both accurate and fast. It's no different than people talking about a breakthrough in sorting algorithm. It's no different than MapReduce. MapReduce is no different. MapReduce is for Hadoop, what essentially what we just announced called RAPIDS for accelerated Hadoop. Think of it that way. Okay. Hadoop comes into memory. It's called patching memory. On top of it, it's called RAPIDS. RAPIDS, the way to think about RAPIDS is essentially map reduce except accelerated by GPUs. Well, you don't build that unless you have a great deal of computer science expertise. And that's what NVIDIA is. That's our differentiation. That's why we're not addressing a percentage share of a market someone else created. That's why our company is always talking about new markets that we're creating, and those new markets tends to be tens, if not 100, 1,000,000,000 of large industries. You can't do that unless you go and reshape it, refactor it, come up with new algorithms. You can't build faster chips to do that alone. Hi, Mark Lipacis from Jefferies. Thanks a lot for the presentation. I found the accelerated computing platform, framework and vision particularly compelling. But it seems like some of your customers, your biggest customers also use that same Lexicon platform and they also have lots of resources. And I was wondering if you could help us maybe share with us a framework for thinking about the platform that some of your customers are developing. Is that is the NVIDIA platform, is it is that is your customer platform sitting on top of the NVIDIA platform or is it sitting next to the NVIDIA platform, let's just say 5 or 10 years down the line? Thank you. Yes, excellent. Excellent. And the reason for that, Mark, is if you look at you look across CUDAX, 2 of the squares are horizontal platforms. And in that case, a customer, a partner excuse me, a partner of ours, ecosystem partner, would tend to jigsaw puzzle and interweave with it. Parts of our platform will stick out. Parts of our platform will not stick out but accelerate parts of their platform. Okay. So let me give you an example. In the case of cloud machine learning platforms like Google Machine Learning Cloud or AWS SageMaker or Microsoft Azure ML, okay? In those cases, our XGBoost library sticks all the way up to the top. Our RAPIDS, which is essentially the modern version accelerated version of MapReduce, goes all the way to the top. Our QDF is basically like pandas for 1 user or Spark for data centers. QDF is basically like Spark, but accelerated in the Python ecosystem. CuML is basically psychic learn, okay? So these our platforms go all the way to the top in some cases. In many cases, like TensorFlow, our Tensor Core architecture, Tensor Core AMP, basically, and CU DNN, CUDA, CU DNN, Tensor Core AMP, it sticks into and is deeply integrated with TensorFlow. But what you see is TensorFlow. And so it just depends. The way we come out of it is this. We try to create a platform where if the ecosystem prefers another platform suppliers approach, we would integrate into theirs. If one doesn't exist and one never will exist, For example, if we didn't write RAPIDS, the map reduce of GPU accelerated data centers will never exist. Nobody knows how to do it, nobody has enough body of engineers to do it, and nobody has the will to go do it. It's too much work. MapReduce sitting on top of Yarn, sitting on top of Hadoop is very complicated stuff. To GPU accelerate that is beyond comprehension. Nobody is going to go do it. That's why we had to go do it. And it took about 4 years to go do that. And so we the first part is when there is a platform like data science, we integrate into it depending on how they like, okay? And so Google has some of our stuff sticking out. Notice RAPIDS is now in virtual machines on the Google Cloud Platform for their machine learning. It sits next to TensorFlow. In the case of SageMaker, some of it more of RAPIDS integrate into SageMaker and some of it sticks out. In the case of Azure, the vast majority of it sticks out. Okay. So that's one answer. The second answer is, in some vertical markets, like, for example, large scale medical imaging, computational software defined medical instruments, the future of medical imaging, multi modality, image reconstruction, AI, visualization, segmentation in 2 d and 3 d, multiple disease, multiple sensor modalities. We've created a platform for that because one doesn't exist on the planet. We call that Clara. We will now integrate that platform into our partners. For example, GE has their medical imaging platform. It's very, very good. Siemens has an excellent one. Sony has parts of it. Canon has some of it. Toshiba has some of it. So, Philips has a lot of it. And so, we will integrate Clara in pieces into those. We'll integrate all of it into Nuance, which is the text annotation standard practically of radiologists, Okay. So that's a Clara example. We also gave you a DRIVE example. We developed a whole stack from top to bottom and end to end. And then everything is open so that if somebody would like to use our simulation platform, but not our physics platform, they'd rather have their own car physics simulator, for example, IPG, we're delighted to plug that in. If somebody would like to have our visualization and our physics simulation, but they would like to have somebody else's traffic AI simulator, Cognata. We're delighted to plug that in. And so we create APIs all over our platform so that the ecosystem could adapt to it. Now, the positive way of thinking about it, which is the way we think about it, is, of course, we would like to enable the ecosystem to shape our platform in the way they like to use it, okay? The benefit to us, of course, is, we're more central part of the ecosystem. If you look at the transportation ecosystem, every day that goes by, more and more and more and more people have some of our stuff all over their company, whether they're buying our chips for the car or not buying our chips for the car, they have our development systems. Sometimes they built their own development systems, but they have our chips in the car. Sometimes they have our software in the car as well. And so all kinds of ways of working with people. So, Mark, the answer is this. There's nothing more powerful than a platform or platforms. That's how we can reach that's why the NVIDIA ecosystem is sticky. That's why the platform is sticky because we have other people's platforms integrated with our platforms. Our platforms are also out on its own. And together, we're helping the ecosystem, helping that industry move forward, okay? Simplistically, that's how. Hi, it's Tim Arcuri at UBS. Thanks. I had two questions. First, in gaming, Jensen, if you read a lot of the websites, they sort of talk about the fact that most of the gamers that are playing AAA games, they have pretty old monitors, 3 to 5 year old monitors. So how do you think about maybe whether the display technology becomes a ramp or a gate on how fast Turing might ramp, number 1? And number 2, in terms of manufacturability, you're already radical limited on a lot of your designs. So how do you think about how to combat that? Do you move to a chiplet design? And Intel is already sort of moving in that direction. So can you talk about that too? The vast majority of the world's gamers are currently at 1080p. And the first thing that they want to do is in the world once the market is at any given resolution in the case of 1080p, the first thing that they want to get to 1080p, but then they want to increase their frame rate within that 1080p and they want to increase their frame rate and then they want to increase the beauty of the images at 1080p, okay. Now increasing their frame rate is not just about seeing it smoothly, it's about reducing latency. So 100 frames per second is much, much lower latency than 30, right? 30 frames per second is 33 milliseconds, which is quite a large number of milliseconds in the world of competitive sports. And so, that's within 1080p. Once they achieve over 100 frames per second and the visual fidelity, all the options are turned on, Then the next thing is they would like to go to the next resolution, which is 1440p. When you go to 1440p, everything gets cut in half. And then now you've got to double you've got to increase your graphics processor so that you can start getting your frame rate back. Meanwhile, we just added ray tracing. And so, we're going to keep on making their game experience better. Every 2 or 3 years, the resolution of monitors kind of clicks up another 2x. The next one after that is 4 ks. But right now, people are at 1440p. So, I don't find that monitors are an obstacle at all because there there are as you know, I just mentioned there are 4 factors. There's monitor resolution, there's latency and frame rate, there's visual fidelity and then there's new features. And we've got some really great new features coming. I'm sorry, your second question? I just turned 56 and it's like, boy, you know? Oh, yes, right. Chiclets. I think Chiclets are good. They're yummy. I like the orange version. The we are at your question was actually reticle limits. We are at radical limits. Pascal, the P100 was near radical limits, Volta radical limits, Volta is radical limits. It is the reason why we invented NVLink, so that we could take 16 GPUs that are radical limits and connect them all together. Okay. And then that's number 1. There are limits to 3 d fast fabrics because the data size is so gigantic today, you can't fit it in one node no matter how big that node is. And so we have to find a way to connect it through smart interconnect. And that's the reason why we decided unit growth in total, and we'll probably announce that as we work through the rest of the year. And the I'm sorry, just the inventory flush, Q1 or Q2? Yes. So we indicated in 1 to 2 quarters starting back in November that we would work through our overall inventory. So that means at the end of this quarter, that's the 2nd quarter, 1 to 2 quarters that we get through, not Q1, Q2, but starting back with where we started in November. Got it. So by the end of Q1 then, it should be done? That is correct. Stacy, there's you might have an assumption that, that isn't quite right, whereas or we've not been very clear. We have a GPU at every price point. One of the confusions that we created for ourselves is the price point of $10.70 to $20.70 There's an impression that we have those two numbers should always be the same price, that somehow a BMW 5 Series would be exactly the same price over the history of time. And unfortunately, that's not possible. Dollars 10.70 was higher priced than $9.70 was higher priced than $5.70 And so this is it's just that because people are paying so much attention now these days, they just nobody paid any attention to it in the past. These numbers were only numbers to us in the past. It has become numbers to society. It's a little bit like the 3 series, the 5 series, the 7 series. It's a bit it has gotten that kind of notoriety. And so that was I think that, that was partly surprised me, too. But we have a GPU at every price point. There's a GPU at 299, 399,499, 499, 59, just like the 4. And at every single price point, it's way better. And so if people were buying the 45%, the simple answer is it could have been just all from ASPs. It has to be from a lot from units because our ASPs didn't go up that far. The simple answer is yes. I mean, the simple answer is yes. Yes, the simple answer is yes. Hi, Ambrish from BMO Collett. I had a question on the op model. And as we think through gross margin for this year, and I wanted to go back to especially in context of qualitatively, you've talked about the positive impact from the crypto business. So and you gave us a delta between the inventory and then the 300 bps improvement. But as we think through the year, so near term, what's the right way to think about gross margin trajectory? And a little bit longer term, when you talk about the op margins, focus to get those margins up, OpEx is kind of growing in line with what you've said consistently that you would be investing in the business. So is it going to be more on the margin front, mix? What's the right way to think about it? Thank you. So when you think about our gross margins as we move forward, moving from the conversation we just had about gaming, as we look at the ranges of overall gross margin in that business, As we see people upgrade and upgrade higher into a higher overall GPU, that helps us in terms of overall margins. Additionally, when we think about our overall data center business, we know that there's a significant amount of software that is incorporated in the platforms that we sell. Now you have an opportunity again to improve the overall gross margins as our data center business becomes more a larger percentage of our business as a whole. Let's not forget our overall automotive business. We're continuing that transition from just our overall infotainment business to move to AI within the cockpit, move to the overall development services that we are working with them and then long term when we think about the overall production piece of it as well. All of these things continue to change both the mix of what we're selling and overall improve our overall gross margins, okay. When we focus your focus in terms of on the OpEx and where we are focusing on the OpEx, is that the nature of the question? Meaning how do they in terms of the 12 pieces. In this most current year, we take a look at this on a yearly basis to say what is the appropriate amount of spend. We have great opportunities in front of us that we need to make sure that we have properly invested in, but we have the uniqueness of that unified architecture to probably get the most out of the spend that we do in terms of OpEx. Going forward, we're not here at a model that says we look at OpEx as a percentage of revenue. It's a little bit too massive company and numbers focus. What we actually do is look at the workloads. What is it going to take us to get that work done? We focus in terms of redeploying even our internal headcount towards these projects, so that we can better utilize our workforce for more greater things. So right now, I would look at we will always keep OpEx front and center as a key area of investment, but keep that in mind in terms of focusing on operating profit in terms of how we can produce the best overall profit and leverage that we can, as well. Those are the 2 things that we keep in mind rather than just an absolute overall OpEx or an OpEx as a percentage of revenue. Yes. Hey, it's Matt Ramsay from Cowen. A couple of questions. I guess, first, Jensen, you guys made a bid from Mellanox last week, and no big secret that there were a couple of other folks that were also involved in that bid, and I think you guys came out a bit late. So I wonder if you could give us a little bit of an update as to the industry and partner reaction to you attending to inquire that business and what steps you're making to keep InfiniBand standard open? And then Colette, maybe you could talk to us a little bit about the infrastructure your group may be putting in place to monitor inventory levels across the business and across the channel, you may be getting a little bit less granular information now from the GeForce software stack as to when GPUs are actually activated for gaming. So whatever infrastructure you put in place there to monitor inventory, an update would be helpful. Thank you. We are super excited that Mellanox decided to accept our offer. Wow, was it competitive. And the reason for that is because they're such a unique company, 20 years in the making, 100% focused on high performance computing networks, a software stack that's been integrated into high performance computing software stacks all over the world. You know that when you're building a high performance computing system, this is a great company to work with. They have a lot of expertise. It is the only you should also highlight that when you look at all these press releases of systems being built, they seem to be the only other company aside from us mentioned. It's actually kind of interesting. And the reason for that is because their engineers work hand in hand, at the data centers, on all the software engineering that's necessary to get the performance, lowest latency processing, the best performance, the offloading necessary. And the data structures being moved around in these in distributed computing these days is really, really complicated stuff. And so they're really a super special company. The customers and the industry is just delighted. They're delighted because they really feel that this important company is going to be, well cared of in our hands because we understand computer architecture. This is a computer architecture question. This is a system architecture question. This is not a chip question. It's not a components question. It's an architecture question. And they understand that we care about this area very, very much. From the highest points of leadership all the way through this company, Mellanox knows, the industry knows, this is something that we're very good at, something we care very much about. And then we're going to continue to invest in this, And we're going to invest in it, leveraging many of the things that our company has. They can take advantage of all that to accelerate their development. And so this is an area that, the industry is just delighted by. And we're going to, of course, keep it open. And our whole platform, as you know, is an open platform. What NVIDIA is about is creating open platforms that everybody else can build their companies, their markets, their applications, their data centers around. This is an open platform company. So the comments on the overall inventory and our process that we have done both in terms of our inventory that we have on hand, with this sudden drop off in terms of overall cryptocurrency, many of the work that we had started for the demand that we felt followed that had started as early as 6 months prior in terms of the work with our overall fabs, our works in overall purchasing, the components and the pieces that we need to put that together. So at the time that Q3 and Q4 came around, and we had seen the drop off of crypto, it became that opportunity to look through primarily just the components and the over amount of components that we had associated with there. We feel that's a thorough process that we do from time to time, and this was even more of a thorough process to make sure we fully understood. But again, looking in hindsight, probably nothing we could do, given a lot of those purchases were done more than 6 months ago. The other focus is focus in terms of on our channel and our focus in terms of where they are in this process in terms of channel. Now what we've done is looked at not only just the weeks or what can we get in terms of reporting in terms of the weeks, but where they are in the life of the overall product. Where are they before it launches? Where are they at the time that it launches? Where are they 6, 8 weeks into it to assure they have the appropriate amount to both feed the market and that we have enough inventory and that we haven't gapped out, but also on the side that says, is there the right amount levels if we are a year or 2 years down. The overall cadence is continuing, but the rigor in terms of at the life cycle at any stage is probably where we've put more of the focus. Jensine probably have more to add here. No, I know every ship by name now and I have a relationship with every one of them. And so I monitor all of them from birth to the next life. Good morning. Thanks for hosting this presentation, Harlan Sur with JPMorgan. We had the head of your healthcare team, Kimberly Powell, present at our healthcare conference recently. And the team is doing a lot here, right? Medical imaging, patient diagnosis, drug discovery, genomics, and they're leveraging all of the systems platforms within your And I And I know that you're targeting the platform approach across other verticals, industrial, retail, agriculture. So wondering if you can just size these vertical targeted businesses with your TAM outlook of $50,000,000,000 Could the vertical focus represent 20% to 30% of the overall $50,000,000,000 TAM? That's my first question. And then second question, if you could just give us an update on China, have you seen demand fundamentals starting to improve with the more relaxed government stance on gaming bans? I know the second question better, yes. The first question, the way we do it is this. We never talk to you about TAMs until we have clear sight of it. So notice we've not one time talked to you about Claritam, which is a Sumit 0. Until we really, really understand deeply like Drive and we're engaged deeply with the ecosystem, that's when we start sizing it. Otherwise, we go to 0. Industrial, we assume 0. But there's no question it's not 0. There's no question it's not 0, but we largely assume it's 0. Let's see what others robotics, we assume 0. There's no question it's not going to be 0. There's no question it can't be 0. It will very likely be the largest AI market. Everything sensors literally everywhere, temperature sensors, vibration sensors, camera sensors, microphone sensors, unfortunately sometimes. And so it's going to be everywhere. And so I think we assume it's largely 0 until we have a really clear side of it. And then we can talk to you about it with some amount of expertise. Until then, we just assume it's very large based on intuition. We have to most of the markets we go into in the beginning, it's all based on intuition. Let me give you an example of the intuition that led us to Clara that is very clearly the right intuition. The intuition was that in the future healthcare its most important instrument imaging medical imaging of any modality will be software defined. That was the intuition that it will be software defined in the future. And that was spot on. Now we start we had that intuition about 5 years ago and we started working on it. And we tried to not overinvest in it in the beginning so that we could do a lot of discovery work and do some prototyping work. And if you take a look at some of the early versions of Clara that I showed you, I mean, it was rickety, but it at least gave us the opportunity to engage with doctors and research universities all over the world and get a lot of feedback. And now we're in deployment. And so that's kind of how we do it. 10 years ago when I started working on Drive, the early version of it was kind of rickety. But I knew that there's no question in my mind that a self driving car was going to be a software defined problem. You're not going to connect 17 chips, separate chips from 14 different vendors together into what is apparently a self driving car. That's not how it works. And so there was no question in my mind it was going to be software deploying. And so we just kind of take it methodically and the timing has to be right. There's some other things that we're working on, but I don't think the timing is quite right. And so we underinvested slightly and but I keep an eye on it and dabble on it, so that this company has a future beyond what we currently described to you. And we have a future 10 to 15 years out that we're working on at all times. Okay. So that's the thing I really love about our company is this ability to, on the one hand, execute incredibly well on today's work, realize the dream for tomorrow and start to explore the day after that and to find the right balance of all of that. That's I just love working with the management team on this. I think we have time for one last question. Sure. Oh boy, the pressure is high, sir. Yes, thanks. Mitch Shees from RBC. So I just want to turn back to the gaming. I really had 2 questions. So first, it's good to hear that the Turing launch has gone well for the beginning. But how do we get comfortable, I guess, around the content increases going forward without any visibility in the kind of the games you made? And then secondly, if I recall, about a couple of years ago, you guys used to really emphasize VR, the unit opportunity there, what the ASPs would be. But I noticed now it's not as topical. So I'm wondering why that is and what the unit opportunities going forward since that would be somewhere around a 2020 opportunity. Yes, great. Let's see. Why am I so absolutely certain? I'm as certain about ray tracing as I am that this is the last question, because I'm in total control of it. Because you said so, because Simone said so. So number 1, the reason for that is this. There's no question that ray tracing is the right answer because it was always the right answer. Using mimicking the physical behavior of light, the physical modeling of light is what computer graphics is all about. And the issue with ray tracing was never the right answer, was it the more elegant answer, is it the simpler answer, it's all true. It's just that it just was too computationally intensive. And so we found a way to use this hybrid rendering approach of half some rasterization, some ray tracing And that's what RTX means, mixed mode rasterization and ray tracing. We invented this technology, invented this approach and we evangelized it to the ecosystem, to the world. And you saw some of the things that happened, Microsoft with Direct XR, Vulcan RT, engines built on top of it, Epic's engine is now 4.22 is now DXR ready, RTX ready and Unity's next build coming out on April 4 is also RTX and DXR ready. These are the engines of the game industry. This is the operating system of the game industry. And if the engines has it and it works fast, you just use it. That's how it works. You just use it. You don't have to invent it. You just use it. It's in the toolkit. And so there's no question in my mind that it's going to happen. I'm absolutely certain of it, okay. And so games keep coming out. Games keep coming out. They come out on almost monthly cadence, several 100 games a year, as you know. And not to include not to mention China, I mean there's a whole bunch of games being in Korea, a bunch of games being made. So there's lots of games being made. There's no question in a year's time, ray tracing will be literally everywhere. This conversation is worthwhile to capture. In a year's time, we'll come back and say, gosh, you were right. It was the last question. That is world class humor, sir. And so what was the second question? I just turned VR. We don't talk about VR very much, but VR is really, really still very important. It's particularly we work with Microsoft on HoloLens because industrial design in a professional market between the 2 of us, we do really great work there. VR is used in industrial design all over the place, styling, architectural engineering. It is a very important part of our Quadro business. It's probably one of the reasons one of the drivers that's causing ASPs to go up. In the consumer world, I think what we really would love to have is a VR headset that is less cumbersome with less cables. And Turing has a special connector that comes out of it that it's called virtual link that connects into a head mount display that reduces the amount of cables and the weight of the cable tremendously. And so a whole bunch of new head mount displays are coming out. It's starting to show up now. And I think you'll be surprised. I think there's no question that the experience is fantastic. And then the next step beyond that will likely be some form of head mounted display that is VRAR ish and stream from the cloud. If somebody could figure out how to stream VR from the cloud, and you might have seen some of our work in this area, Some collaboration we've done with AT and T and Verizon to test NVIDIA's wireless VR from GFN, from GeForce NOW. We could stream VR directly out of the cloud. This technology is still in development. We're still very early, I would say beta quality, but the experience is really quite phenomenal. When you take that and you connect it up to a head mount display now that's wireless, then you have no cables at all. And if it's semi translucent, then where AR starts and VR starts and ends is going to be quite interesting, Okay. So don't take your eyes, keep asking me this question. We're continuing to work on it. The ability to mix reality and virtual reality is going to come. It's absolutely going to come and I'm excited about it. I want to thank all of you guys for joining us today. And, GTC, we're at this is all where it started, and it's kind of fun to sit up here and chat with you guys where it started. And now you guys know what GTC turned into. Last year, we had over 30 1,000 GTC attendees, and I'm looking at 200. That's fairly fast growth in a matter of 10 years. So I want to thank all of you for your support. Have a great GTC. And lunch, if you head out the doors, turn to the left and the Gold Room, and we'll be here as well as with the executives from the company, and we'll join you for lunch. Thank you.

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