NVIDIA Corp (NVDA) — Q2 2020 Earnings Call Transcript

Thank you. Good afternoon, everyone and welcome to NVIDIA’s conference call for the second quarter of fiscal 2020. With me on the call today from NVIDIA are Jen-Hsun Huang, President and Chief Executive Officer, and Colette Kress, Executive Vice President and Chief Financial Officer. I would like to remind you that our call is being webcast live on NVIDIA’s Investor Relations website. The webcast will be available for replay until the conference call to discuss our financial results for the third quarter of fiscal 2020. The content of today’s call is NVIDIA’s property. It can’t be reproduced or transcribed without our prior written consent. During this call, we may make forward-looking statements based on current expectations. These 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 Form 10-K and 10-Q, and the reports that we may file on Form 8-K with the Securities and Exchange Commission. All our statements are made as of today, August 15, 2019, 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, Simona. Q2 revenue was $2.58 billion, in line with our outlook, down 17% year-on-year and up 16% sequentially. Starting with our gaming business, revenue of $1.31 billion was down 27% year-on-year and up 24% sequentially. We are pleased with the strong sequential growth in the quarter when we launched our RTX SUPER lineup for desktop gamers, wrapped up our greatest ever number of gaming laptops, and launched our new RTX studio laptops for creators. In July, we unveiled 3 GeForce RTX SUPER GPUs, delivering the best-in-class gaming performance and power efficiency and real-time ray tracing for both current and next-generation games. These GPUs delivered a performance boost of up to 24% from our initial Turing GPUs launched a year earlier. The SUPER lineup strengthens our leadership in the high end of the market and the response has been great. We look forward to delighting gamers with the best performance in ray tracing as we get into the back-to-school and holiday shopping seasons. Ray tracing is taking the gaming industry by storm and has quickly come to define the modern era of computer graphics. A growing number of blockbuster AAA titles have announced support for NVIDIA RTX ray tracing, including Call of Duty: Modern Warfare, Cyberpunk 2077, Watch Dogs: Legion and Wolfenstein: Youngblood. Excitement around these titles is tremendous. NVIDIA GeForce RTX are the only graphic cards in the market with hardware support for ray tracing. They deliver a 2 to 3x performance speed-up over GPUs without a dedicated ray tracing core. The laptop business continues to be a standout growth driver as OEMs are ramping a record 100-plus gaming laptop models ahead of the back-to-school and holiday season. The combination of our energy-efficient Turing architecture and Max-Q technology enables beautifully crafted thin and light form factors that can deliver the performance of high-end gaming desktops or our next-generation console. At Computex in May, we unveiled NVIDIA RTX Studio laptops, a new design artist platform that extends our reach to the large, underserved market of creators. In the age of YouTube, creators and freelancers are a rapidly growing population, but they have traditionally not had access to professional-grade workstations through online and retail channels. RTX Studio laptops are designed to meet their increasingly complex workflows such as photorealistic ray tracing, AI image enhancement, and ultra-high-resolution video. Powered by our RTX GPUs and optimized software, RTX Studio laptops deliver performance that’s up to 7x faster than that of the MacBook Pro. A total of 27 RTX Studio models have been announced by major OEMs. Sequential growth also benefited from the production ramp of the two new models of Nintendo Switch gaming console. We are expecting our console business to remain strong in Q3 before the seasonal production slowdown in Q4 when console-related revenue is expected to be fairly minimal, similar to last year. Moving to data center, revenue was $655 million, down 14% year-on-year and up 3% sequentially. In the vertical industries portion of the business, expanding AI workload drove sequential and year-over-year growth. In the hyperscale portion, we continue to be impacted by relatively weak overall spending at a handful of CSPs. Sales of NVIDIA GPUs for use in the cloud were solid. While sales of internal hyperscale use were muted, the engineering focus on AI is growing. Let me give some color on each of these areas. We are building a broad base of customers across multiple industries as they adopt NVIDIA’s platforms to harness the power of AI. Public sectors, higher education, and financial services were among the key verticals driving growth this quarter. In addition, we won Lighthouse account deals in important industries that are on the cusp of being transformed by AI. For example, in retail, Wal-Mart is using NVIDIA GPUs to run some of its product demand forecasting models, slashing the time to do so in just 4 hours from several weeks on CPUs. By accelerating its data science workflow, Wal-Mart can improve its algorithms, reduce development cycles, and test new features. Earlier this week, we announced breakthroughs for the fastest training and inference of the state-of-the-art model for natural language process understanding called BERT, or Bidirectional Encoder Representations from Transformers, a breakthrough AI language model that achieves a deeper sense of language, context, and meaning. This can enable mere human comprehension in real-time by chatbots, intelligent personal assistants, and search engines. We are working with Microsoft as an early adopter of these advances. AI computing leadership is a high priority for NVIDIA. Last month, we set records for training deep learning neural network models on the latest MLPerf benchmarks, particularly in the most demanding areas. In just 7 months, we have achieved up to 80% speed-ups enabled by new algorithms and software optimizations across the full stack while using the same hardware. This is a direct result of the productive programming environment and flexibility of CUDA. Delivering AI at scale isn’t just about silicon. It’s about optimizing across the entire high-performance computing system. In fact, the NVIDIA AI platform is getting progressively faster, with every month publishing new optimization and performance improvements to CUDA-X AI libraries, supporting every AI framework and development environment. All in, our ecosystem of developers is now 1.4 million strong. In setting these MLPerf records, we leveraged our new DGX SuperPOD AI supercomputer, demonstrating that leadership in AI research demands leadership in computing infrastructure. This system debuted in June at #22 on the TOP500 list of the world’s fastest supercomputers at the annual International Supercomputing Conference. Used to meet the massive demand for autonomous vehicle development programs, it is powered by more than 1,500 NVIDIA V100 Tensor Core GPUs linked with Mellanox interconnects. We have made DGX SuperPOD available commercially to customers, essentially providing them with the turnkey supercomputer that they can assemble in weeks rather than months. It is roughly 400x smaller in size than other similarly performing TOP500 systems, which are built from thousands of servers. Also at the conference, we announced that by next year’s end, we will make available to the ARM ecosystem NVIDIA’s full stack of AI and HPC software, which accelerates more than 600 HPC applications and all AI frameworks. With this announcement, NVIDIA will accelerate all major CPU architectures, including x86, POWER, and ARM. Lastly, regarding our pending acquisition of Mellanox, we have received regulatory approval in the U.S. and are engaged with regulators in Europe and China. The approval process is progressing as expected, and we continue to work toward closing the deal by the end of this calendar year. Moving to pro visualization, revenue reached $291 million, up 4% from our prior year and up 9% sequentially. Year-on-year and sequential growth was led by record revenue for mobile workstations with strong demand for new thin and light form factors. We had a great showing at SIGGRAPH, the computer graphics industry's biggest annual conference held in Los Angeles. Our researchers won several Best in Show awards. In just a year since the launch of RTX ray tracing, over 40 design and creative applications with RTX technology have been announced by leading software vendors, including Adobe, Autodesk, and Dassault systems, and many others. NVIDIA RTX technology has reinvigorated the computer graphics industry by enabling researchers and developers to take a leap in photorealistic rendering, augmented reality, and virtual reality. Finally, turning to automotive, Q2 revenue was $209 million, up 30% from a year ago and up 26% sequentially. This reflects growing adoption of next-generation AI cockpit solutions and autonomous vehicle development projects, including one particularly sizable development services transaction that was recognized in the quarter. In addition, in June, we announced a new partnership with the Volvo Group to develop AI and autonomous trucks utilizing NVIDIA’s end-to-end AI platform for training, simulation, and in-vehicle computing. The strategic partnership will enable Volvo Group to develop a wide range of autonomous driving solutions for freight transport, recycling collection, public transport, construction, mining, forestry, and more. This collaboration is a great validation of our long-held position that every vehicle, not just cars but also trucks, shuttles, business taxis, and many others, will have autonomous capability one day. Autonomous features can bring enormous value to the trucking industry, in particular, as the demand for online shopping puts ever greater stress on the world’s transport systems. Expectations for overnight or same-day deliveries create challenges that can only be met by autonomous trucks, which can operate 24 hours a day. To help address these needs, NVIDIA has created an end-to-end platform for autonomous vehicles, from AI computing infrastructure to large-scale simulation to in-car computing. Multiple customers from OEMs like Mercedes-Benz, Toyota, and Volvo to Tier 1s like Bosch, Continental, and ZF are already onboard. We see this as a $30 billion addressable market by 2025. Moving to the rest of the P&L, Q2 GAAP gross margins were 59.8% and non-GAAP was 60.1%, up sequentially, reflecting higher automotive development services, a favorable mix in gaming, and lower component costs. GAAP operating expenses were $970 million, and non-GAAP operating expenses were $749 million, up 19% and 8% year-on-year, respectively. We remain on track for high single-digit OpEx growth in fiscal 2020 while continuing to invest in the key platforms driving our long-term growth, namely graphics, AI, and self-driving cars. GAAP EPS was $0.90, down 49% from a year earlier. Non-GAAP EPS was $1.24, down 36% from a year ago. With that, let me turn to the outlook for the third quarter of fiscal 2020. We expect revenue to be $2.9 billion, plus or minus 2%. GAAP and non-GAAP gross margins are expected to be 62% and 62.5%, respectively, plus or minus 50 basis points. GAAP and non-GAAP operating expenses are expected to be approximately $980 million and $765 million, respectively. GAAP and non-GAAP OI&E are both expected to be income of approximately $25 million. GAAP and non-GAAP tax rates are both expected to be 10%, plus or minus 1%, excluding discrete items. Capital expenditures are expected to be approximately $100 million to $120 million. Further financial details are included in the CFO commentary and other information available on our IR website. In closing, let me highlight upcoming events for the financial community. We will be at the Jefferies conference, hardware and communications infrastructure summit, on August 27 and at the Citi Global Technology Conference on September 25. With that, we will now open the call for questions. Operator, would you please poll for the questions?

Good afternoon. Thank you for taking the questions. I guess first question on gaming, how should we think about your outlook into the October quarter vis-à-vis kind of normal seasonality? How are you thinking about Switch within that? And considering now that you have a full Turing lineup as well as content truly coming to the forefront here, how do you think about trends beyond the October quarter? Thank you.

Sure. Colette, why don’t you take the Switch question? And then I will take the rest of the RTX questions.

Sure. From a gaming perspective, the overall Switch or the overall console business definitely is a seasonal business. We usually expect to see production ramping in Q2 and in Q3, with it coming down likely in Q4. So you should see Switch to be a portion definitely of our gaming business in Q3.

Yes. C.J., thanks for your question. RTX, as you know, is – first of all, RTX is doing great. I think we have put all the pieces in place to bring ray tracing into the future of games. The number of games, the blockbuster games that adopted RTX is really snowballing. We announced several 6 games in the last couple of months. There are going to be some exciting announcements next week at gamescom. It’s pretty clear now the future of gaming will include ray tracing. The number of software developers that create – with creative tools that adopted RTX is really quite spectacular. We now have over 40 ISV tools that were announced at SIGGRAPH that have accelerated ray tracing and video editing. And some of the applications’ amazing AI capabilities for image optimization enhancement support RTX. And so looking forward, this is what I expect. I expect that ray tracing is going to drive a reinvigoration of gaming graphics. I expect that the over 100 laptops that we have RTX designed – RTX GPUs designed into is going to contribute to our growth. Notebook gaming is one of the fastest-growing segments of the gaming platform world. The number of notebooks that are able to game is only a few percent, so it’s extremely underexposed. And yet, we know that gamers are – like the rest of us, they like thin and light notebooks, but they like it to be able to run powerful games. And so this is an area that has grown significantly for us year-over-year, and we’re expecting it to grow through the end of the – through the second half and through next year. One of the things that’s really exciting is our RTX Studio line that we introduced recently. We observed, and through our discussions with the PC industry, that the creatives are really underexposed and underserved by the latest technologies. They want notebooks and PCs that have powerful graphics. They use it for 3D content creation, high-definition video editing, and image optimization and things like that. We introduced a brand-new line of computers that we call RTX Studio. Now the OEMs were so excited about it. At SIGGRAPH, we now have 27 different laptops shipping and more coming. So I think RTX is really geared for growth. We have great games coming. We got the SUPER line of GPUs. We have all of our notebooks that were designed into that we are ramping and, of course, the new RTX Studio line. I expect this to be a growth market for us.

Very helpful. If I could follow-up on the data center side, perhaps you can speak directly just to the hyperscale side, both internal and cloud, and whether you’re seeing any green shoots, any signs of life there and how you are thinking about what that rate of recovery could look like over time?

With the exception of a couple of hyperscalers, C.J., I would – we’re seeing broad-based growth in data centers. In the area of training, the thing that’s really exciting everybody, and everybody is racing towards, is training these large gigantic natural language understanding models, language models. The transformer model that was introduced by Google, called BERT, has since been enhanced into XLnet and RoBERTa and, gosh, so many different versions of these language models. NLU, natural language understanding, is one of the most important areas that everybody’s racing towards. These models are really, really large. They are over 1,000x larger than image models that we’re training just a few years ago, and they’re just gigantic models. It’s one of the reasons why we built the DGX SuperPOD so that we could train these gigantic models in a reasonable amount of time. The second area – so that’s training in the hyperscalers. The second area where we are seeing enormous amounts of activity has to do with trying to put these conversational AI models into services so that they could be interactive and in real time. Whereas photo tagging and photo enhancement is something that you could put off-line and you could do that while you have excess capacity when it’s off of the most busy time of the day. You can’t do that with language and conversational AI. You better to respond to the person in real time. And so the performance that’s required is significant. But more importantly, the number of models necessary for conversational AI, from speech recognition to language understanding to recommendation systems to text-to-speech to wave synthesis, these 5, 6, 7 models have to be processed in real-time – in series and in real time so that you can have a reasonable conversation with the AI agent. These types of activities are driving interest and activity at all of the hyperscalers. My expectation is that this is going to continue to be a big growth opportunity for us. But more importantly, in addition to that, we’re seeing that AI is – the wave of AI is moving from the cloud to the enterprise to the edge and out to the autonomous systems. The place where we’re seeing a lot of excitement, and we talked about that in the past and we’re seeing growth there, has to do with the vertical industry enterprises that are starting to adopt AI to create new products, whether it’s a delivery robot or some kind of chatbot or the ability to detect fraud in financial services. These applications in vertical industries are really spreading all over the place. There are over 4,000 AI start-ups around the world. The way that we engage them is they use our platform to start developing AI in the cloud. We’re the only AI platform that’s available on-prem and in every single cloud. They can use our AI platforms for – in all the clouds, which is driving our cloud computing and external cloud computing growth. They can also use it on-prem if their usage really grows significantly. That’s one of the reasons why our Tesla for OEMs and DGX is growing. We’re seeing broad-based excitement around AI as they use it for their products and new services. These 4,000, 4,500 start-ups around the world are really driving consumption of that.

Alright, thanks for taking my questions. I actually had 2 as well, one quick one for Colette and one for Jensen. Colette, good to see the gross margin recovery getting into October. Is this 62% to 63% range a more sustainable level and perhaps a level you could grow off of as sales get to more normalized levels? And then just a bigger question is for Jensen. Again, on the data center side, Jensen, when I look back between 2015 to 2018, your data center business essentially grew 10x. The last year has been a tough one with the slowdown in cloud CapEx and so forth. When do you think your data center starts to grow back on a year-to-year basis? Can that happen sometime later this year? And then just longer term, what is the right way to think about this business? Does it go back to prior levels? Does it go at a different phase? This is the one part of the business that I think is toughest for us to model, so any color would be very helpful.

Great, so let me start first with your question, Vivek, regarding gross margins. Yes, thanks for recognizing that we are moving towards our expectations that, over time, we’ll continue to see our overall volumes improve. Essentially, our business is normalized. We’ve reached normalized levels through the last couple of quarters. This quarter, just very similar to what we will see going forward, is the mix is the largest driver, what drives our overall gross margins and our gross margin improvements.

Yes, Vivek, if you look at the last several years, there’s no question our data center business has grown a lot. My expectation is that it’s going to grow a lot more, and let me explain to you why. Aside from a couple – a few uncontrollable circumstances and the exception of a couple of large customers, the overall trend, the broad-based trend of our data center business is upward, to the right. It is growing very nicely. There are a couple of different dynamics that are causing that on first principles to grow. Of course, one of them is that AI is well known to require accelerated computing, our computing architecture is really ideal for it. AI is not just one network. It’s thousands of different types of networks, and these networks are getting more and more complex over time. The amount of data you have to process is enormous. Like all software programs, you cannot predict exactly how the software is going to get programmed. Having a programmable architecture like CUDA, yet optimized for AI like Tensor Cores that we’ve created, is really the ideal architecture. We know also that AI is the most powerful technology force of our time. The ability for machines to learn and write software by themselves and write software that no humans can write is pretty extraordinary. The applications of AI, as you guys are watching yourself, are spreading in every single industry. The way we think about AI is in waves, if you will. The first wave of AI is developing the computer architecture, the second wave is applying the AI for cloud service providers or hyperscalers. They have a large amount of data and many consumer applications, many of them are not life-critical, so the application of an early technology – early-adoption technology was really viable. You saw hyperscalers adopt AI. The thing that’s really exciting for us is beyond recommendations, beyond image enhancement, and the area where we believe the most important application for AI is likely conversational AI. Most people talking and asking questions to their mobile devices rather than having a list of options respond with an answer that is very likely a good one. The next phase of AI is what we call vertical industry enterprise AI, and this is where companies are using it not just to accelerate the business process internally but to create new products and services. That’s the next phase of growth. It affects companies from large industrials, transportation companies, retailers, healthcare companies, you name it. This phase of growth of AI is the phase that we’re about to enter into. The longer-term vision is enormous, but it takes time because it’s life-critical, and it has to do with transportation. It’s a $100 trillion industry. We know it’s going to be automated. We know everything that moves in the future will be autonomous or have autonomous capabilities. This is a big market, and I’m super enthusiastic about it.

So to answer your question regarding gross margin in a little bit more detail, probably our largest area that we expect improvement in terms of our mix is our mix return regarding our overall gaming business. We expect to have a full quarter of our SUPER lineup within the next quarter including our RTX as well as our notebook to become a bigger mix as it grows. These drivers are one of the largest reasons why we see that growth in our gross margin. We always think about our component cost and our overall cost of manufacturing, so this is always baked in over time, but we’ll continue to see improvements on that as well.

Good afternoon. Thanks for taking my question. Again on your data center business, many of your peers on the compute and storage side are seeing spending recovery by cloud and hyperscalers in the second half of this year after a similar weak first half of the year. You guys saw some growth in Q2 driven primarily by enterprise. It seems like you had some broadening out of the customer spending this quarter. Inferencing continues to see strong momentum. Would you guys expect that this translates into a double-digit percentage sequential growth in data center in Q3 off of the low base in Q2?

Our hyperscale data center with a few customers doesn’t give us very much – we don’t get very much visibility from a handful of customers in hyperscale. However, we’re seeing broad-based growth and excitement in data centers. When you look at our data center from that perspective and these pieces, although we don’t see as much – we don’t get as much visibility as we like in a couple of the large customers, the rest of the hyperscalers, we’re seeing broad-based growth. We’re experiencing the enthusiasm and energy that maybe the others are, and so we will keep updating you guys as we go. We will see how it goes.

Thanks a lot. I had two. I guess first for Jensen, Volta’s been around now for about 2 years. Do you see signs of demand maybe building up ahead of the new set of nanometer products, whenever that comes out? I guess I’m just wondering whether there’s some element of this is more around product cadence that gets resolved as you do roll out the product. That’s the first question. And then I guess, the second question, Colette, is of the $300 million growth into October, it sounds like Switch is pretty flat, but I’m wondering if you can give us maybe some qualitative sense of where the growth is coming from, is it maybe like two-thirds gaming and one-third data centers, something like that?

Well, Volta data center products can churn that fast. We gamers could churn products quickly because they’re bought and sold one at a time. But data centers data center infrastructure really has to be planned properly, and the build-out takes time. We expect Volta to be successful all the way through next year. Software still continues to be improved on it. In fact, just one year in just one year, we improved our AI performance on Volta by almost 2x, 80%. Building the software is just an enormous undertaking. It’s pretty hard to get a new processor into a data center and it takes time. The last time a new processor entered into a data center was an x86 Xeon, and you just don’t bring processors into data centers that frequently or that easily. When we’re able to deploy into data centers as quickly as we do, I think we lose sight of how hard it is to do that in the first place. The way to think about Volta is that it’s surely in its prime, and it’s going to continue to do well all the way through next year.

In regard to our guidance on revenue, and we do guide in terms of the total. You have seen, in this last quarter, we executed a sequential increase really focusing on moving to a normalization of our gaming business. We’re now approaching the second half of the year getting ready for the back-to-school and the holidays. You should expect our gaming business to continue to grow to reach that full normalization by the end of Q3. We also expect the rest of our platforms to likely grow. We have a couple of different models on how that will come out. But yes, we expect our data center business to grow, and then we’ll see on the rest of our businesses as well.

Thank you very much. Good afternoon. A couple of questions. I guess the first one is Jensen, if you have any, I guess, high-level qualitative commentary on how the new SUPER upgrades of your Turing platform have been received in the market and how you might think about them progressing through the year. And then, I guess, the second question is a bigger one. Intel’s talked quite openly about One API. The software stack at Xilinx is progressing with Versal ACAP. I mean you guys get a lot of credit for the decade of work that you’ve done on CUDA. But I wonder if you might comment on if you’ve seen any movement in the competitive landscape on the software side for the data center space.

SUPER is off to a great start. Goodness, SUPER is off to a super start. If you do channel checks all over, even though we’ve got a lot of products in the channel and we last quarter was a transitional quarter for us actually, and we shipped SUPER later in the quarter. Because the entire ecosystem and all of our execution engines are so primed, we were able to ship a fair number through the channel. If you do spot checks all around the world, they’re sold out almost everywhere. The pricing in the spot market is drifting higher than MSRP. That just tells you something about demand. SUPER is off to a super start for us. At this point, it’s a foregone conclusion that we’re going to buy a new graphics card, and it’s going to the last 2, 3, 4 years to not have ray tracing is just crazy. Ray tracing content just keeps coming out. Between the performance of SUPER and the fact that it has ray tracing hardware, it’s going to be super well positioned for throughout all of next year. Your question about APIs and software programmability, APIs is just one of the issues. The large issue about processors is how do you program it. The reason why x86s and CPUs are so popular is because they solve the great challenge of software developers: how to program a computer. It’s an area of tremendous research. The last time a new processor entered into a data center was an x86 Xeon, and you just don’t bring processors into a data center that frequently or that easily. I don’t really know how one programming approach or a simple API is going to make 7 different types of weird things work together. I think it’s just time will tell whether one programming approach could fit 7 different types of processors when no time in history has it ever happened.

Great. Thank you. I wonder if you could talk about the strength in the automotive business. It looks like the services piece of that is getting to be bigger; what’s the outlook for that part of the business? And can you give us a sense of the mix between services and components at this point?

Sure. Thanks, Joe. Our approach to autonomous vehicles comes in basically 2 parts. The first part is a full stack, which is building the architected processor, the system, the system software and all of the driving applications on top, including the deep neural nets. The second part of it, we call that a full stack self-driving car computer. The second part of DRIVE includes an end-to-end AV development system. For those who would like to use our processors, use our system software but create their own applications, we created a system that allows basically shares with them our computing infrastructure that we built for ourselves that allows them to do end-to-end development from deep learning development to the application of AV to simulating that application to doing regression testing of that application before they deploy it into a car. Although the cars will take several years to go into production, we’re seeing a lot of interest in working with us to develop self-driving cars using our development systems and entering into development projects. The number of autonomous vehicle projects is quite large around the world as you can imagine. My sense is that we’re going to continue to do well here. The additional part of autonomous vehicles and where the capability has been derived and is going to seal up more near-term opportunities has to do with things like delivery shuttles, self-driving shuttles, and maybe cargo movers inside walled warehouses. We are seeing a lot of excitement around that area.

Yes, thanks for taking the questions and congratulations on the improved performance. At your Analyst Day back a couple of months ago, you highlighted the installed base opportunity for RTX. At that point in time, you talked about 50% being Pascal base, 48% being pre-Pascal. You also alluded to the fact that you were seeing a positive mix shift higher in terms of the price points of this RTX cycle. So, I’m curious, where do we stand on the current product cycle? What are you seeing currently as we go through this product cycle on the Turing platforms?

We launched well, first of all, the answer is that RTX adoption is faster than Pascal’s adoption if you normalize to time 0 of launch. The reason for that is Pascal launched top to bottom on the same day. And as you guys know, we weren’t able to do that for Turing. But if we did that for Turing, the adoption rate is actually faster. To me, it’s rather sensible. The reason for that is because Pascal was basically DX12. Turing is the world’s first DXR, the first ray tracing GPU, brand-new functionality, brand-new API and a lot more performance. I think it’s sensible that Turing’s adoption is going to be rapid. The second element of Turing is something that we’ve never talked about before. We’re mentioning it more and more because it’s such an exciting market for us is notebooks. The install base of Pascal has a very, very little notebook in it. The reason for that is because, in the past, we were never able to put a high-performance gaming GPU into a thin and light notebook until we invented Max-Q. In combination with our energy efficiency, we’re now able to put a 2080 into a laptop, and it's still beautiful. This is effectively a brand-new growth market for us. So, with so few people who are gamers that are able to game on a laptop, I think this is going to be a nice growth market for us. The new market we launched this last quarter is called RTX Studio. This is an underserved segment of the market where consumers, enthusiasts, they could be artists working on small firms. They need powerful computers to do their work, to do rendering, and high-definition video editing, and yet it’s underserved by workstations. We aligned with OEMs and created a whole new line of notebooks called RTX Studio. The enthusiasm has been great. We’ve launched 27 different laptops, and I’m looking forward to seeing the results of that. This is tens of millions of people who are creators; some of them are professionals, some of them are hobbyists. They use Adobe suites, Autodesk, SolidWorks, and some of them use all kinds of renders like Blender. These digital content creations are the modern way of creativity in the entertainment space.

Hi, guys. Thanks for taking my questions. I have two for Colette. My first question is on data center. I know you say that you have a broad-based growth except for a few hyperscalers. But you only grew at 3% sequentially, about $20 million. That doesn’t sound like broad-based growth to me unless the hyperscalers get worse or are they just still so much bigger than the rest of it? I guess, what’s going on in data center? How do I wrap my head around broad-based growth with relatively minimal growth observed?

So, to answer your question here, Stacy, on what we refer to when we’re discussing the broad-based growth is the substantial expansion that we have on the types of customers and the industries that we are now approaching. Even a year ago, we had a very, very small base in terms of industry-based AI workloads that they were using. Over this last quarter, we’re continuing to see strong growth we roll out all different types of AI solutions worldwide. Our hyperscalers, a couple of them, are not necessarily growing; some of them are flat, and some are growing. We believe our continued growth with the industries is important for us for the long term to expand the use of AI, and we are just really pleased with what we’re seeing in that growth this quarter.

Thanks, everyone. We are happy with our results this quarter and our return to growth across our platforms. Gaming is doing great. It’s great to see NVIDIA RTX reinvigorating the industry. GeForce has several growth drivers. Ray traced games continue to gain momentum. A large number of gaming laptops are rolling out, and our new Studio platform is reaching the large underserved community of creators. Outside a few hyperscalers, we’re seeing broad-based growth in data centers. AI is the most powerful technology force of our time and a once-in-a-lifetime opportunity. More and more enterprises are using AI to create new products and services while leveraging AI to drive ultra-efficiency and speed in their business. With hyperscalers racing to harness recent breakthroughs in conversational AI, we see growing engagements in training as well as interactive conversational inference. RTX, CUDA accelerated computing, AI, and autonomous vehicles, the work we’re doing is important, impactful, and incredibly fun. We’re just grateful there is so much of it. We look forward to updating you on our progress next quarter.