Earnings Call: Q1 2017

May 12, 2016

Good afternoon. My name is Claudine. I'll be your conference coordinator today. I'd like to welcome everyone to the NVIDIA Financial Results Conference Call. All lines have been placed on mute. After the speakers' remarks, there will be a question and answer period. This conference is being recorded Thursday, May 12, 2016. I would now like to turn the call over to Arnab Chandra, Vice President of Investor Relations at NVIDIA. Please go ahead, sir. Thank you. Good afternoon, everyone, and welcome to NVIDIA's conference call for the Q1 of fiscal 2017. With me on the call today from NVIDIA are Jensen Huang, President and Chief Executive Officer and Colette Kress, Executive Vice President and Chief Financial Officer. I'd like to remind you that today's call is being webcast live on NVIDIA's Investor Relations website. It is also being recorded. You can hear a replay by telephone until the 19th May 2016. The webcast will be available for replay up until next quarter's conference call to discuss Q2 results. The content of today's call is NVIDIA's property. It cannot be reproduced or transcribed without our prior written consent. During the course of this call, we may make forward looking statements based on current expectations. These forward looking statements are subject to a number of significant risks and uncertainties, and our actual results may differ materially. For a discussion of factors that could affect our future financial results and business, please refer to the disclosure in today's earnings release, our most recent Forms 10 ks and 10 Q and the reports that we may file on Form 8 ks with the Securities and Exchange Commission. All our statements are made as of today, 12th May, 2016, based on information currently available to us. Except as required by law, we assume no obligation to update any such statements. During this call, we will discuss non GAAP financial measures. You can find a reconciliation of these non GAAP financial measures to GAAP financial measures in our CFO commentary, which is posted on our website. With that, let me turn the call over to Colette. Thanks, Arnab. In March, we introduced our newest GPU architecture, PASCAL. This extraordinary scalable design built on the 16 nanometer FinFET process provides massive performance and exceptional power efficiency. It will enable us to extend our leadership across our 4 specialized platforms gaming, professional visualization, data center and automotive. Year on year revenue growth continued to accelerate increasing 13% to $1,300,000,000 Our GPU business grew 15% to $1,080,000,000 from a year ago. Teklar processor business was up 10% to $160,000,000 Growth continued to be broad based across all four platforms. Record performance in data center was driven by the adoption of deep learning across multiple industries. In Q1, our 4 platforms contributed nearly 87% of revenue, up from 81% a year earlier. They collectively increased 21% year over year. Let's start out with our gaming platform. Gaming revenue increased 17% year on year to $687,000,000 Momentum carried forward from the holiday season helped by the continued strength of Maxwell based GTX processors. Last weekend at Dreamhack Austin, we unveiled GeForce GTX 1080 and GTX 1070, our first Pascal GPUs for gamers. They represent a quantum leap for gaming and immersive VR VR experiences delivering the biggest performance gains from the previous generation architect in a decade. Media reports and gamers have been unanimously enthusiastic. The Verge wrote, what NVIDIA is doing with its new GTX 1,000 series is bringing yesteryear's insane high end into 20 sixteen's mainstream. We also extended our VR platform by adding spatial acoustics to our VR Work software development kit which helps provide an even greater sense of presence within VR. We introduced simultaneous multi projection enabling accurate efficient projection of the real world to surround monitors, VR headsets as well as future displays. To showcase these technologies, we created our own amazing open source game called NVIDIA VR Fun House available on Steam. In addition, we announced Anzyl, an in game photography system which enables gamers to capture high resolution and VR scenes within their favorite games. Moving to professional visualization. Quadro grew year on year for the 2nd consecutive quarter. Revenue rose 4% to $189,000,000 dollars Growth came from higher end products and mobile workstations. We launched the M6000, 24 gig and are seeing good success among multiple customers including Toyota and Pixar. Roche is using the M6000 to speed its DNA sequencing pipeline by 8x enabling more affordable genetic testing. We see exciting opportunities for our Quadro platform with virtual reality and NVIDIA iRay, a photorealistic rendering tool that enables designers effectively to walk around their creations and make real time adjustments. Moving to data center, revenue was a record 143,000,000 63% year on year and up 47% sequentially, reflecting enormous growth in deep learning. In just a few years, deep learning has moved from academia and is now being adopted across the hyperscale landscape. We expect growing deployment in the coming year among large enterprises. GPUs have become the accelerator of choice for hyperscale data centers due to their superior programmability, computational performance and power efficiency. Our Tesla M4 is over 50% more power efficient than other programmable accelerators for applications such as real time image classification for AlexNet, a deep learning framework. Hyperscale companies are the fastest adopters of deep learning accelerating their growth in our Tesla business. Starting from infancy 3 years ago, hyperscale revenue is now similar to that from high performance computing. NVIDIA GPUs today accelerate every major deep learning framework in the world. We power IBM Watson and Facebook's Big Sur server for AI and we are in AI platforms at hyperscale giants such as Microsoft, Amazon, Alibaba and Baidu for both training and real time inference. Twitter has recently said they use NVIDIA GPUs to help users discover the right content among the millions of images and videos shared every day. During the quarter, we hosted our 7th Annual GPU Technology Conference. The event drew record attendance with 5,500 scientists, engineers, designers and others across a wide range of fields and featured 600 sessions and 200 exhibitors. At GTC, we unveiled the Tesla P100, the world's advanced GPU accelerator based on the PASCAL architecture. The P100 utilizes a combination of technologies including NVLink, a high speed interconnect allowing application performance to scale on multiple GPUs, high memory bandwidth and multiple hardware features designed to natively accelerate AI applications. The next platform, an enterprise IT site called it a beast in all of the good sense of that word. Among the first customers for our PASCAL accelerator is the Swiss National Computer Center, which will use it to double the speed of Europe's fastest supercomputer. At GTC, we also announced the GGX-one, the world's first deep learning supercomputer, Loaded with 8 P100s in a single box interconnected with NVLink, it provides the deep learning performance equivalent to 250 traditional servers. DDX-one comes loaded with a suite of software designed to aid AI and application developers. Universities, hyperscale vendors and large enterprises developing AI based applications are showing strong interest in the system. Among the first to get DGX-one will be the Massachusetts General Hospital. It launched an initiative that applies AI techniques to improve the detection, diagnosis, treatment and management of diseases, drawing on its database of some 10,000,000,000 medical images. In our grid graphics virtualization business, we are seeing interest across a variety of industries ranging from manufacturing, energy, education, government and financial services. Finally, in automotive, revenue continued to grow, reaching $113,000,000 up 47% year over year and up 22% sequentially, reflecting the growing popularity of premium infotainment features in mainstream cars. NVIDIA is working closely with partners to develop self driving cars using our end to end platform which starts with Tesla in the data center and extends through the deployment with DRiVE PX2. Since we unveiled DRiVE PX2 earlier this year, worldwide interest has continued to grow among carmakers, Tier 1 suppliers and others. We are now collaborating with more than 80 companies using the open architecture of Drive PX to develop their own software and driving experiences. At GTC, we demonstrated the world's 1st self driving car trained using deep learning and showed its ability to navigate on roads without lane markings even in bad weather. Additionally, we announced that DRIVE PX2 will serve as the brain behind the new robo race initiative in the Formula E racing circuit. The circuit will include 10 teams racing identical cars all using DRiV PX2. Beyond our 4 platforms, our OEM IP business was 173,000,000 dollars down 21% year on year reflecting weak PC demand. Now turning to the rest of the income statement. We had record GAAP and non GAAP gross margins for the Q1 at 57.5% and 58.6% respectively. Driving these margins was the strength of our Maxwell GPUs, the success of our platform approach and strong demand for deep learning. GAAP operating expenses for the Q1 were $506,000,000 and declined from $539,000,000 in Q4 on lower restructuring charges. Non GAAP operating expenses were $443,000,000 flat sequentially and up 4% from a year earlier reflecting increased hiring for our growth initiatives and development related expenses associated with PASCAL. GAAP operating income for the Q1 was $245,000,000 up 39% from a year earlier. Non GAAP operating income was $322,000,000 also up 39%. Non GAAP operating margins improved more than 4 70 basis points from a year ago to 24.7%. For the Q1 GAAP net income was $196,000,000 Non GAAP net income was 263,000,000 dollars up 41% fueled by the strong revenue growth and improved gross and operating margins. During the Q1, we'd entered into a $500,000,000 accelerated share repurchase agreement and paid $62,000,000 in quarterly cash dividends. Since the restart of our capital return program in the Q4 of fiscal 2013, we have returned over $3,500,000,000 to shareholders. This represents over 100% of our cumulative free cash flow for fiscal years 2013 through this Q1. For fiscal Now turning to the outlook for the Q2 of fiscal 2017. We expect revenue to be 1 point $35,000,000,000 plus or minus 2%. Our GAAP and non GAAP gross margins are expected to be 57.7% and 58.0% respectively, plus or minus 50 basis points. GAAP operating expenses are expected to be approximately $500,000,000 Non GAAP operating expenses are expected to be approximately $445,000,000 GAAP and non GAAP tax rates for the Q2 of fiscal 2017 are both expected to be 20% plus or minus 1%. Further financial details are included in the CFO commentary and other information available on our IR website. We will now open the call for questions. Operator, could you please poll for questions? Thank you. Thank And our first question comes from the line of Vivek Arya with Bank of America. Please go ahead. Thank you for taking my question and good job on the results and the guidance. Maybe as my first one, Jensen, how do you assess the competitive landscape in PC gaming? AMD recently claimed to be taking a lot of share and they are launching Polaris soon. Just if you could walk us through what does NVIDIA do better than AMD so that helps you maintain your competitive edge in this market and what impact will PASCAL have in that? Vivek, thank you. Our PC gaming platform GeForce is strong and getting stronger than ever. And I think the reason for that is several folds. First of all, our GPU architecture is just superior. We dedicate an enormous amount of effort to advancing our GPU architecture. I think the engineering of NVIDIA is exquisite and our craftsmanship is really unrivaled anyway. The scale of our company in building GPUs is the highest and the largest of any company in the world. This is what we do. This is the one job that we do. And so it's not surprising to me that NVIDIA's GPU technology is further ahead than any time in its history. The second thing, however, it's just so much more than just chips anymore, as you know. Over the last 10 years, we've started to evolve our company to much more of a platform company. And it's about developing all the algorithms that sit on top of our GPUs. GPU is a general purpose processor. It's a general purpose processor that's dedicated to a particular field of computing, such as computer graphics here, physics simulation, etcetera. But the thing that's really important is all of the algorithms that sit on top of it. And we have a really, really fantastic team of computational mathematicians that captures our algorithms and our know how into Gameworks, into the physics engine and recently, the really amazing work that we're doing in VR that we've embodied into VR works. And then lastly, it's about making sure that the experience always just works. We have a huge investment in working with game developers all over the world from the moment that the game is being conceived of all the way to the point that it's launched. And we optimize the games on our platform, we make sure that our drivers work perfectly. And even before a gamer downloads or buys a particular game, we've already updated their software so that it works perfectly when they install the game. And we call that GFE, GeForce Experience. And so Vivek, it's really about a top to bottom approach. And I haven't even started talking about all of the marketing work that we do in engaging the developers and engaging the gamers all over the world. This is really a network platform and all of our platform partners that take it take our platform to market. And so this is a pretty extensive network and it's a pretty extensive platform and it's so much more than chips anymore. Got it. Thank you, gents. And as my follow-up, it seems like data center products were the big upside surprise in Q1, grew over 60% from last year. Could you give us some more color on what drove that upside? Was it the initial PASCAL launch? Is that impact still to come? And just broadly, what trends are you seeing there in HPC versus cloud versus some of these new AI projects that you are involved with? Yes, thanks. You know that I've been rather enthusiastic about high performance computing for some time. And we've been evolving our GPU platform so that it's better at general purpose computing than ever. And almost every single data center in the world and every single server company in the world are working with us to build servers that are based on GPUs, based on video GPUs for high performance computing. One of the most important areas of high performance computing has been this area called deep learning. And this deep learning, as you know, as you probably are starting to hear, is a brand new computing model that takes advantage of the massively parallel processing capability of a GPU, along with the big data that many companies have to essentially have software write algorithms by itself. Deep learning is a very important field of machine learning is now in the process of revolutionizing artificial intelligence, making machines more and more intelligent and using it to discover insight that, quite frankly, is impossible otherwise. And so this particular field was first adopted by hyperscale companies so that they could find insight and make recommendations and make predictions from the billions of customer transactions they have every day. Now it's in the process of moving into enterprises. But in the meantime, hyperscale companies are now in the process of deploying our GPUs and deep learning applications into production. And so we've been talking about this area for some time. And now we're starting to see the broad deployment in production. So we're quite excited about that. And our next question comes from the line of Mark Lipacis with Jefferies. Please go ahead. Thanks for taking my questions. First question, the growth in the Tesla business is impressive. And in looking back, it seemed like that business actually decelerated in 2015, which was a head scratcher for me. And I wonder, do you think that the your customers in that business paused in anticipation of PASCAL? Or do you think it's the AI apps and deep learning applications that are just hitting their stride right now? Well, decelerating, I guess, I'm not sure I recall that. The thing about HPC, about GPU computing is, as you know, this is a new computing model. And we've been promoting this computing model now for close to 7 years. And a new computing model doesn't come along very frequently. In fact, as I know, I don't know if there's a new computing model that's used anywhere that has been revolutionary in the last 20 years. And so GP Computing took some time to develop. We've been evangelizing it for quite some time. We developed robust tools so that make it easier for people to take advantage of our GPUs. We have industry expertise in a large number of industries now. We have APIs that have been created for each one of the developers in each one of the industries. And as of this time, we have quite a large handful, quite a large number of industries that we accelerate applications for. And so I think that I guess my recommendation my recollection would be that it has taken a long time, in fact, to have made GPU GPU computing into a major new computing model. But I think at this point, it is pretty clear that it's going mainstream. It is really one of the best ways to achieve the post Moore's law era of computing acceleration and it's being adopted by all kinds of applications. And the one that, of course, that is a very, very big deal is deep learning and machine learning. This particular field is a brand new way of doing computing for a large number of companies and we're seeing traction all over the place. And our next question comes from the line of Stephen Chen with UBS. Please proceed. Hi, thanks for taking my questions. Jensen or Colette, first of all, I wanted to see if you could provide some color on some of the drivers of growth for fiscal 2Q, whether most of it's coming from Pascal possibly in the gaming market or in the Tesla products or if there's also some non growth in Integra Automotive as well for fiscal 2Q? Yes, Stephen, I would expect that all of our business is growing Q2. And so it's across the board. We're seeing great traction in gaming. Gaming, as you know, has multiple growth drivers. Partly the gaming is growing because the production value of games is growing, partly because the number of people who are playing is growing. Esports is more popular than ever. Sports spectatorship is more popular than ever. And so gaming is just a larger and larger market and it's surprising everybody. The quality of games is going up, which means that the CPU and GPU set to go up. High performance computing is growing and the killer app is machine learning and deep learning. And that's going to continue to go into production from the hyperscale companies as we expand our reach into enterprises all over the world now, companies who have a great deal of data that they would like to find insight in. Automotive is growing, and we're delighted to see that enterprise is growing as well. Great. And as a follow-up, maybe for Colette, on the gross margin side of things, you guys are guiding margins up nicely for the quarter. And just kind of wondering, looking out further across the year, whether you have whether or not the levers that you have available to you currently, if there's further room for expansion, whether it's from product mix, higher ASPs and or maybe even some of the platform related elements such as software or services. I'm just kind of wondering, especially on the software side, how much that can continue to help margins from a platform perspective? Sure. Thanks, Stephen. Yes, our gross margins within the quarter for Q1 did hit record levels, just due to very strong mix across our products, on the Maxwell side, both from a gaming perspective as well as what we have in enterprise for pro visualization and data center. As we look to Q2, a good review of where we also see gross margins and those are looking at a non GAAP at about 58%. Mix will again be a strong component of that as our launch of PASCAL will come out with high end gaming and with data center and the growth essentially all of our platforms will help our overall gross margins. As we go forward, there's still continued work to do. We're here to guide just 1 quarter out, but we do have a large TAM in front of us on many of these different markets, and the mix will certainly help us. We're in the initial stages of rolling out what we have in software services, our overall systems. So I don't expect it to be a material part of the overall gross margin, but it will definitely be a great value proposition for us for what we put forth. Our next question comes from the line of Deepan Nag with Macquarie. Please proceed. Yes. Thanks guys and congratulations on a great quarter. For Q2, can you kind of talk about how much the contribution you expect from PASCAL? And also maybe give us an update on where you think yields are progressing right now? Yes. Thanks a lot, Deepan. We're expecting a lot of PASCAL. PASCAL was just announced with 1080 and 1070, both of those products are in full production. We're in production with Tesla P100. And so all of our PASCAL products that we've already announced are in full production. So we're expecting a lot. Yields are good and building these semiconductor devices are always hard, but we're very good at it. And this is now a year behind when the first 16 nanometer FinFET products went into production at TSMC. They have yields under great control. TSMC is the world's best manufacturer of semiconductors, and we work very closely with them to make sure that we're ready for production. And we surely wouldn't have announced it if we didn't have manufacturing under control. So we're in great shape. Our next question comes from the line of Ambrish Srivastava with BMO Capital Markets. Please proceed. Hi, this is Gabriel calling in for Ambridge. Thanks for taking my question. I think when you recently launched a new Gtek's GPU products, it looks like your pricing or MSRP appears to be higher than your prior generation. And how should we think about your ASP and even gross margin trend as you're ramping this product for the rest of the year? Yes, thanks. Thing that's most important is that the value is greater than ever. And one of the things that we know is games are becoming richer and richer than ever, the production values become richer than ever. And gamers want to play these games with all of the settings maxed out. They would like to play at a very high resolution and they want to play it at very high frame rates. When I announced 10 80, I was showing all of the latest and most demanding games running at twice the resolution of a game console, at twice the frame rate of a game console, and it was barely even breathing hard. And so I think one of the most important things is for customers of this segment, they want to buy a private they can count on and that they can rely on to be ready for future generation games. And some of the most important future generation games are going to be in VR. And so the resolution is going to be even higher. The frame rate expectation is 90 Hertz and the latency has to be incredibly low so that you feel a sense of presence. And so I think the net of it all is that the value proposition we delivered with 10 80 and 10 70 is just through the roof. And if you look at the early response on the web and from analysts, they're quite excited about the value proposition that we brought. And our next question comes from C. J. Muse with Evercore. Please go ahead. Yes, good afternoon. Thank you for taking my question. I guess, two questions around the data center. I guess, first part, how's the visibility here today? And I guess how do you see perhaps the transition hyperscale to ramp in HPC? And then I know you guys don't like to forecast over the next couple of quarters. But looking out over the next 12 to 24 months, this part of your business has grown from 8% to 11% year over year. And curious as you look at 1 to 2 years, what do you think this could be as a percentage of your overall company? Thank you. Yes, C. J, thanks a lot. I think a lot the answer to a lot of your questions is I don't know. However, there are some things I do know very well. One of the things that we do know is that high performance computing is an essential approach for one of the most important computing models that we know today, which is machine learning and deep learning. Hyperscale data centers all over the world is relying on this new model of computing so that it could harvest, it could study all of data that they're getting to find insight for individual customers, make the perfect recommendation, predict what somebody would anticipate, would look forward to in terms of news or products or whatever it is. And so this approach of using computing is really unprecedented. And this is a new computing model and the GPU is really ideal for it. And we've been working on this for coming up on a decade. And it explains one of the reasons why we have such a great lead in this particular aspect. The GPU is really the ideal processor for these massively parallel problems. And we've optimized our entire stack of platforms from the architecture to the design to the system, to the middleware, to the system software, all the way to the work that we do with developers all over the world so that we can optimize the entire experience to deliver the best performance. And so this is something that's taken a long time to do. I have a great deal of confidence that machine learning is not a fad. I have a great deal of confidence that machine learning is going to be to the future computing model for a lot of very large and complicated problems. And I think that all of the stories that you see, whether it's the groundbreaking work that's done at Google and Google DeepMind on AlphaGo to self driving cars, to the work that people are talking about and artificial intelligence recommendation chatbots to, boy, the list just goes on and on. And I think that it goes without saying that this new computing model in the last couple of years has really started to deliver very, very promising results. And I would characterize the results as being superhuman results. And now they're going into production. And we're seeing production deployments, not just in 1 or 2 customers, but basically in every single hyperscale data center in the world in every single country. And so I think this is a very, very big deal. And I don't think it's a short term phenomenon. And the amount of data that we process is just going to grow. And so those are some of the things I do know. And our next question comes from the line of Mark Lipacis with Jefferies. Please proceed. Hi. Thanks for cycling back in for a follow-up. Sometimes in when you introduce a new product and this is probably for technology, there's kind of a hiccup as the transition happens where the supply chain blows out the older inventory and before the new products can ramp in. Some people call that an air pocket. So I was wondering, is that something that you can manage? How do you try to manage that? Did you count for it when you think about the outlook for this quarter? Thank you. Yes. Thanks, Mark. Well, product transitions are always tricky and we take it very seriously. And there are several things that we do know. We have a great deal of visibility to the channel. And so we know how much inventory is where and of which kind. And secondarily, we have perfect visibility into our supply chain. And both of those matters need to be taken into account when we launch a new product. And so anything could happen. The fact that matters, we are in a high-tech business and high-tech is hard. The work that we do is hard. The team doesn't take it for granted and we're not complacent about our work. And so I think that I can't imagine a better team in the world that is to manage this transition. We've managed transitions all the time. And so we don't take it lightly. However, you're absolutely right. I mean, it requires care. And the only thing I can tell you is that we're very careful. And our next question comes from the line of Joe Moore with Morgan Stanley. Please proceed. Great. Thank you. I guess along the same lines, can you talk a little bit about the founders edition of the new gaming products? And is that different from sort of previous reference designs that you've done? And is there any kind of difference in economics to NVIDIA if you saw Founder's Edition? The Founder's Edition is something we did as a result of demand from the end user base. The Founder's Edition is basically a wholly designed by NVIDIA product. A reference design is really not designed to be an end product. It's really designed to be a reference for manufacturers to use a starting point. But the Founders Edition is designed so that it could be manufactured, it can be marketed and customers can continue to buy it from us for as long as they desire. Now our strategy is to support our global network of adding car partners and we're going to continue to do that. And we gave them we gave everybody reference designs like we did before. And in this particular case, we created the founders edition so that people who would like to buy directly from us, people who like our industrial design and people who would like the exquisite design and quality that comes with our products that we can do. And so it's designed to be extremely overclockable. It's designed with the best possible components. And if somebody would like to buy products directly from us, they have the ability to do that. I expect that the vast majority of the adding cards will continue to be manufactured by our adding card partners. And that's our expectation and that's our hope. And I don't expect any dramatic change in the amount of shifting of that. And so that's basically it, Founders Edition, the most exquisitely engineered add in cart the world's ever seen directly from Enviti. And our next question comes from the line of Harlan Sur with JPMorgan. Please go ahead. Good afternoon and solid job on the execution. At the recent Analyst Day, I think the team articulated its exposure to developed and emerging markets and the unit and ASP growth opportunities around EM. Just wondering what are the current demand dynamics that you're seeing in the emerging markets? Clearly, I think macro wise, they're still pretty weak. But on the flip side, gaming has shown to be fairly macro insensitive. It would be great to get your views here. I think you've just said it. Depending on which one of our businesses that you're talking about, gaming is rather macro insensitive for some reason. People enjoy gaming, whether the economy is good or not, whether the oil price is high or not, people seem to enjoy gaming. Don't forget, gaming is not something that people do once a month, like going out to a movie theater or something like that. People game every day. And the gamers that use our products are gaming every day. It's their way of doing engaging with their friends. They hang out with their friends that way. It's a platform for chatting. Don't forget that the number one messaging company in China is actually a gaming company. And the reason for that is because while people are gaming, they're hanging out with their friends and they're chatting with their friends. And so it's really a medium for all kinds of things, whether it's entertaining or hanging out or expressing your artistic capabilities or whatnot. And so gaming, for 1, appears to be doing quite well in all aspects of the market. The second thing is enterprise, however, is largely or hyperscale is largely U. S. Dynamic. And the reason for that is because U. S. Dynamic as well as the China dynamic because that's where most of the world's hyperscale companies happen to be. And so and then automotive, we most of our automotive success today has been from the European car companies, and we're seeing robust demand from the premium segments of the marketplace. However, in the future, we're going to see a lot more success with automotive here in the United States, here in Silicon Valley, in China, we're going to see a lot more global penetration because of our self driving car platform. And our next question comes from the line of Ian Partners. Please go ahead. Yes, thank you. So for July, it looks like you've got some operating expense discipline given some hiring activity in April, you're down sequentially. Is that related to the timing of some tape out activity? And as Pascal rolls out, what's the shape of tape out speed do you think for the upcoming quarters? Well, all of the Pascal ships have been taped down. Out. But we still have a lot of engineering work to do. The differences are minor. We're a large company and we have a lot of things that we're doing. I wouldn't over study the small deltas in OpEx. We don't manage things a dollar at a time. And we're trying to invest in new important things. On the other hand, this company is really, really good about not wasting money. And so we want to make sure that on the one hand, we invest into opportunities that are very important to our company. But we just have a culture of frugality that permeates our company. And then lastly, from an operational perspective, we've unified everything in our company behind one architecture. And so whether you're talking about the cloud or workstations or data centers or PCs or cars or embedded systems or autonomous machines, you name it. Everything is exactly one architecture. And the benefit of one architecture is that we can leverage one common stack of software and that base software. It really streamlines our execution. And so it's an incredibly efficient approach for leveraging our one architecture into multiple markets. And so those three aspects of how we run the company really helps. And our next question comes from the line of Blayne Curtis with Barclays. Please go ahead. Hey, guys. Thanks for taking my question and nice results. Just curious, two questions. Jensen, you talked about the ramp of deep learning and you kind of talked about that you're going to use GPUs for both learning as well as applying inferences. Just curious what stages you mentioned all these customers, what stages are all these customers? Are they actually deploying it in volume? Are they still, more Is that also going to be up year over year? Is that also going to be up year over year? I did ask this question actually. OEM business, will that be up year over year? I OEM business is down year over year, isn't it? Right. And so on Q2, we'll probably follow along in Q2 along with overall PC demand, which is not expected to grow. So we'll look at that as our side product and probably would not be a growth business in Q2. Yes. So Blayne, you know that our OEM business is a declining part of our company's overall business. And not to mention that the margins are also significantly below the corporate average. And so that would suggest that it's just increasingly less important part of the way that we go to market. Now what I don't mean by that is that we don't partner with the world's large OEMs. HP, Dell, IBM, Cisco, Lenovo, all of the world's large enterprise companies are our partners. We partner with them to take our platforms, our differentiated platforms, our specialty platforms to the world's markets, And most of them are related to enterprise. We just do less and less volume, high volume components devices, generic devices like cell phones that we got out of, generic PCs that we've gotten out of, Largely, we tend not to do business like that anymore. We tend to focus on our differentiated platforms. You mentioned some you mentioned learning and learning training and inferencing. First of all, training is production. You can't train a network just once. You have to train your network all the time. And every single hyperscale company in the world is in the process of scaling out their training because the networks are getting bigger, they want their networks to do even better. The difference between a 95% accurate network and a 98 percent accurate network or a 99% accurate network could mean 1,000,000,000 of dollars of differences to Internet companies. So this is a very big deal. And so they want their networks to be larger. They want to deploy their networks across more applications. And they want to train their network with new data all the time. And so training is a production matter. It is probably the largest HPC high performance computing application on the planet that we know of at the moment. And so we're scaling, we're ramping up training for production for hyperscale companies. On the other hand, I really appreciate you asking about the inference thing. We recently well, this year, several months ago, we announced the M4, the Tesla M4 that was designed for inferencing. And it's a little tiny graphics card, little tiny processor, and it's less than 50 watts. It's called the M4. And at GTC, I announced a brand new compiler called the GPU Inference Engine, GIE. And GIE recompiles the network that was trained so that it can be optimally inferenced at the lowest possible energy. And so not only are we already 50 watts, which is low power, we can also now inference at a higher energy efficiency than any processor that we know of today, better than any CPU by a very, very long shot, better than any FPGA. And so now hyperscale companies could use our GPUs for both training and they use exactly the same architecture for inferencing and the energy efficiency is really fantastic. Now, the benefit of using GPU for inferencing is that you're not just trying to inference, only, you're trying to oftentimes decode the image or you could be decoding the video. You inference on it and you might even want to use it for transcoding, which is to re encode that video and stream it to whoever it is that wants to share live video with. And so the processing that you want to do on the images and the video and the data is more than just inferencing. And the benefit of our GPU is that it's really great for all of the other stuff too. So we're seeing a lot of success in M4. I expect M4 to be quite a successful product and hyperscale data centers. My expectation will start to ramp that into production Q2, Q3 and Q4 timeframe. And our next question comes from the line of Ross Seymore with Deutsche Bank. Please go ahead. Hi, thanks for letting me ask a question. On the automotive side, I just wondered, Colette, in your CFO commentary, you mentioned product development contracts as part of the reason it was increasing. Can you give us a little bit of indication what those are? And is the percentage of the revenue coming from those increasing? And then maybe finally, is that activity indicative of future growth in any way that can be meaningful for us to track? Sure. Thanks for the question. So in our automotive business, there's definitely a process even before we're shipping platforms into the overall cars that we're working jointly with the auto manufacturer, startups and others on what may be a future product. Many of those agreements continue and will likely continue going forward. And that's what you see incorporated in our automotive business. So yes, you'll probably see this continue and go forward. It's not necessarily consistent. It starts in some quarters, bigger in other quarters, but that's what's incorporated in our automotive. Nicole, let me just add one thing. The thing to remember is that we're not selling chips into a car. We're not selling you know that Drive PX is the world's first autonomous driving car computer that's powered by AI, it's powered by deep learning. And we're seeing a lot of success with Drive PX. And as Colette mentioned earlier, there are some 80 companies that we're working with, whether it's Tier 1s or OEMs or startup companies all over the world that we're working with in this area of autonomous vehicles. And the thing to realize is you're not selling a chip into that car. You're working with a car company to build an autonomous driving car. And so that process requires a fair amount of engineering. And so we have a mechanism, we have development mechanism that allows car companies to work with our engineers to collaborate to develop these self driving cars. And that's what most of that stuff that Colette was talking about. And our next question comes from the line of Craig Ellis with B. Riley and Company. Thanks for taking the question and congratulations on the revenue and Jensen, I wanted to follow-up on one of the comments that you made regarding PASCAL. I think you indicated that all PASCAL parts are taped out. So the question is, if that is the case, will we see refresh activity across all the platform groups in fiscal 2017? Or in fact, will some of the refresh activity be taking place in fiscal 2018? So what's the duration of the refresh that we're looking at? Well, first, thanks for the question. And we don't comment on unannounced products, as you know. I hate to ruin all of the surprises for you. But Pascal is the single most ambitious GPU architecture we have ever undertaken. And this is really the first GPU that was designed from the ground up for applications that are quite well beyond computer graphics and high performance computing. It was designed to take into consideration all of the things that we've learned about deep learning, all the things that we've learned about VR. For example, it has a brand new graphics pipeline that allows Pascal to simultaneously project into multiple surfaces at the same time with no performance penalty. Otherwise, it would degrade your performance in VR by a factor of 2 just because you have 2 services you're projecting into. And then we can do all kinds of amazing things for augmented reality, other types of virtual reality displays, surround displays, curved displays, dome displays. I mean, there's all kinds of holographic displays. There's all kinds of displays that are being invented at the moment. And we have the ability to now support those type of displays with a much more elegant architecture without degrading performance. And so PASCAL is whether it's AI, whether it's gaming, whether it's VR, it's really the most ambitious project we've ever undertaken. And it's going to go through all of our markets. The application for self driving cars is going to be pretty exciting. And so it's going to go through all of our markets. And so we're in of course, we have plenty to announce in the future, but we've announced what we've announced. And our next question comes from the line of Romit Shah with Nomura Research. Please go ahead. Yes, thanks very much. Jensen, I was hoping you could just share your view today on fully autonomous driving because Mobileye's Chairman has said very recently that the technology basically isn't ready and that in I guess my question is, well, 1, I'd love your view on that. And 2, whether cars are fully autonomous or autonomous in certain environments, say 1 or 2 years out, does it impact the trajectory of your automotive business? First of all, working on full autonomy is a great endeavor. And whether we get there 100%, 90%, 92%, 93%, is in my mind completely irrelevant. The endeavor of getting there and making your car more and more autonomous, initially, of course, we would like to have a virtual co pilot. Having a virtual co pilot is the way I get to work every day. I mean, every single day, I drive my Model S and every single day, I put it into autonomous mode and every single day it brings me joy. And I'm not confessing necessarily, but texting a little bit is okay. And so I think that the path to full autonomy is going to be paved by amazing capabilities along the way. And so we're not waiting around for 2019. We'll ship autonomous vehicles by the end of this year. And so I understand that we're 3 years ahead of other people's schedules. However, we also know that Drive PX2 is the most advanced autonomous computing car computer in the world today. And it's powered by AI fully. And Drive PX2 will be a Drive PX3, there'll be a Drive PX4. And then by 2019, I guess, we'll be shipping Drive PX5. And so those our roadmap is just like that. That's how we work, as you guys know very well. And so I think there's a there's a lot of work to be done, which is the exciting part. The thing about a technology company, I think about any company, unless there is great problems and great challenges that we can help solve, what value do we bring? And what NVIDIA does for a living is to do what to build computers that no other company in the world can build, whether it's high performance computers that are used to power our nation's supercomputers or deep learning supercomputers so that we can gain insight from data or self driving car computers so that autonomous cars can save people's lives and make people's lives more convenient. That's what we do. This is the work that we do, and I'm delighted to hear that we're 3 years ahead of the competition. Our next question comes from the line of Suji Desilva with Topeka Capital Markets. Hi, gents and Eike, congratulations on the impressive results here. On the data center business, is there an inflection going on with deep learning with the software maturity that's driving this point? And can you give us any metrics, Jensen, for how to think about the size of this opportunity for you? I know it's hard, but things like server attach rates, what percent of servers you could attach will it be an M4 and a high end in every box? Or was it or maybe the number of GPUs a single deep learning implementation has, something like that, that would help? Yes. The truth is that nobody really knows how big this deep learning market is going to be. Until a couple of 2, 3 years ago, it was really even hard to imagine how good the results were going to be. And if it wasn't because of the groundbreaking work that was done at Google and Facebook and other researchers around the world, how would we have discovered that it was going to be superhuman? The work that recently was done by Microsoft Research, they've achieved superhuman levels of inferencing that of image recognition and voice recognition that is that's really kind of hard to imagine. And these networks are now huge. The Microsoft Research Network, super deep network is 1,000 layers deep. And so training such a network is quite a chore, it's quite an endeavor. And this is a problem that high performance computing will have to be deployed. And this is why our GPUs are so sought after. In terms of how big that's going to be, my sense is that almost no transaction, my sense is that almost no transaction with the Internet will be without deep learning or some machine learning inference in the future. I just can't imagine that. There's no recommendation of a movie, no recommendation of a purchase, no search, no image search, no text that won't somehow have passed through some smart chatbot or smart bot or some machine learning algorithm so that they could make the transaction more make the inference or request more useful to you. And so I think this is going to be a very big thing. And then on the other hand, the enterprises, we use deep learning all over our company today. And we're not we had the benefit of being early because we saw the power of this technology early on. But we're seeing deep learning being used now in medical imaging all over the world. We're seeing it being used in manufacturing. It's going to be used for scientific computing. More data is generated by high performance computers and supercomputers than just about anything. They generate it through simulation. They generate so much data that they have to throw the vast majority of it away. For example, the Hadron Collider, whenever the protons collide, they throw away 99% of the data and they're able to barely keep up with just that 1%. And so by using machine learning and our GPUs, they could find insight in the rest of the 99%. So, there are just applications go on and on and on. And people are now starting to understand this deep learning really puts machine learning and puts artificial intelligence in the hands of engineers is understandable. And that's one of the reasons why it's growing so fast. And so, I don't know exactly how big it's going to be, but here's my proposition that this is going to be the next big computing model, the way that people compute, that in the past, software programmers wrote programs compiled it. And in the future, we're going to have algorithms write the software for us. And so that's a very different way of computing and I think it's a very big deal. And our next question comes from the line of David Wong with Wells Fargo. Please go ahead. Thanks very much. In automotive, what products are your revenues coming from currently? Is DRiPX at all significant or are your sales primarily DRiPX or something else? The primary parts of our automotive business today comes from infotainment, from the premier infotainment systems, for example, the virtual cockpit that Audi ships. And the vast majority of our development projects today come from drive P and L autonomous projects. We probably have 10 times as many autonomous driving projects as we have infotainment projects today, and we have a fair number of infotainment And so that gives you a sense of where we were in the past and where we're going in the future. And I'm showing no further questions at this time. Mr. Chanda, please I'll turn the call over to you. We had a great start to the year with strong revenue growth and profitability. PASCAL is a quantum leap in performance for AI, gaming and VR and is in full production. Deep learning is spreading across every industry, making data center our fastest growing business. With growing worldwide adoption of AI, the arrival of VR and the rise of self driving cars, we're really excited about the future. Thanks for tuning in. Ladies and gentlemen, that concludes today's conference call. We thank you for your participation and we ask that you please disconnect your line. Have a great day everyone.