NVIDIA Corp (NVDA) — Q4 2020 Earnings Call Transcript

Thank you. Good afternoon, everyone, and welcome to NVIDIA's conference call for the fourth quarter of fiscal 2020. 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 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 first quarter of fiscal 2021. 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 forms 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 February 13, 2020, 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. Q4 revenue was $3.11 billion, up 41% year-on-year and up 3% sequentially, well above our outlook, reflecting upside in our data center and gaming businesses. Full year revenue was $10.9 billion, down 7%. We recovered from the excess channel inventory in gaming and an earlier pause in hyperscale spending and exited the year with great momentum. Starting with gaming. Revenue of $1.49 billion was up 56% year-on-year and down 10% sequentially. Full year gaming revenue was $5.52 billion, down 12% from our prior year. We enjoyed strong end demand for our desktop and notebook GPUs. Our gaming lineup was exceptionally well positioned for the holidays, with the unique ray tracing capabilities of our RTX GPUs and incredible performance at every price point. From the Singles Day shopping event in China, through the Christmas season in the West, channel demand was strong for our entire stack. Fueling this were new blockbuster games like Call of Duty: Modern Warfare, continued esports momentum and new RTX SUPER products. With RTX price points as low as $299, ray tracing is now the sweet spot for PC gamers. Gaming is thriving and gamers prefer GeForce. The global phenomenon of esports keeps gaming momentum with an audience now exceeding 440 million, up over 30% in just two years, according to Newzoo. The League of Legends World Championship brought more than 100 million viewers on par with this month's Super Bowl. Ray tracing titles continue to come to market and GeForce RTX GPUs are the only ones that support this important technology. This quarter Wolfenstein: Youngblood and Deliver Us The Moon were the latest titles to support ray tracing, as well as NVIDIA's Deep Learning Super Sampling technique, which also uses AI to boost performance. With the proliferation of RTX-enabled games and our best ever top-to-bottom performance, we are solidly into the Turing Architecture upgrade cycle. Gamers continue to move to higher-end GPUs, seeking better performance and support for ray tracing. Gaming laptops posted double-digit year-on-year growth for the eighth consecutive quarter. The category continues to expand, driven by appealing thin and light form factors with fantastic graphics performance. This holiday season retailers stocked a record 125 gaming laptops based on NVIDIA GPUs, up from 94 last year, with our Max-Q designs up 2x. At CES we launched the world's first 14-inch, GeForce RTX laptop with ASUS. We also continue to expand our Studio lineup of laptops for the fast-growing population of freelance creators, designers and YouTubers with 13 new RTX Studio systems introduced at CES. Powered by Turing GPUs, these systems are optimized for over 55 creative and design applications with RTX accelerated ray tracing and/or AI. Last week, we launched our GeForce NOW cloud gaming service. Powered by GeForce, GeForce NOW is the first cloud gaming service to deliver ray trace games. It's also the only open platform, so gamers can enjoy the games they already have and use their existing store accounts without having to repurchase games. GeForce NOW enables PC games on Mac, Windows, PCs, TVs, mobile devices and soon Chromebooks. GFN has a freemium business model that includes two membership plans: a free membership with standard access and the Founders tier with a starting price of $4.99 per month which gives priority access and RTX ray tracing support. Our goal with GeForce NOW is to expand GeForce gaming to more gamers. About 80% of GeForce NOW gamers are playing on underpowered PCs or devices with Mac OS or Android. With GeForce NOW, they are able to enjoy PC gaming on a GeForce GPU in the cloud. GeForce Now can expand GeForce well beyond the roughly 200 million gamers we reach today. Separately, we entered into a collaboration with Tencent, the world's largest gaming platform, to bring PC gaming in the cloud to China, the world's largest gaming market. NVIDIA GPU technology will power Tencent's Start cloud gaming service, which is in early testing stages. Moving to data center, revenue was a record $968 million, up 43% year-on-year and up 33% sequentially, our strongest ever sequential growth in dollar terms. Full year fiscal year 2020 data center revenue was a record $2.98 billion, up 2% from the prior year. Strong growth was fueled by hyperscale and vertical industry end customers. Hyperscale demand was driven by purchases of both our training and inference products, in support of key AI workloads such as natural language understanding, conversational AI and deep recommenders. Hyperscale demand was also driven by cloud computing. AWS now makes the T4 available in every region. This underscores the versatility of the T4 which excels at a wide array of high-performance computing workloads, including AI inference, cloud gaming, rendering, and virtual desktop. Vertical industry growth was driven primarily by consumer Internet companies. Other verticals, such as retail, healthcare, and logistics continue to grow from early-stage build-outs, with a strong foundation of deep learning engagements. We see an expanding set of opportunities across high-performance computing, data science and edge computing applications. T4, our inference platform, had another strong quarter, with shipments up 4x year-on-year driven by public cloud deployments, as well as edge AI video analytics applications. T4 and V100, reflecting strong demand for inference and training respectively, set records this quarter for both shipments and revenue. Even as NVIDIA remains the leading platform for AI model training, NVIDIA's inference platform is gaining wide use by some of the world's leading enterprise and consumer Internet companies, including American Express, Microsoft, PayPal, Pinterest, Snap and Twitter. The industry continues to do groundbreaking AI work for NVIDIA. For example, Microsoft's biggest quality improvements made over the past year in its Bing search engine stem from its use of NVIDIA GPUs and software for training and inference of its natural language understanding models. These DNN transformer models, popularized by BERT, have computational requirements for training that are in the order of magnitude higher than earlier image-based models. Conversational AI is a major new workload requiring GPUs for inference to achieve high throughput within the desired low latency. Indeed, Microsoft cited an inference throughput increase of up to 800x on NVIDIA GPUs compared with CPUs enabling it to serve over 1 million BERT inferences per second worldwide. Just this week, Microsoft researchers announced a new breakthrough in natural language processing with the largest ever publicized model trained on NVIDIA DGX-2. This advances the state-of-the-art for AI assistance and tasks such as answering questions, summarization, and natural language generation. Recommendators are also an important machine learning model for the Internet powering billions of queries per second. The industry is moving to deep recommendators, such as wide & deep model, which leverage deep learning to enable automatic feature learning and to support unstructured content. Running these models on GPUs can dramatically increase inference throughput and reduce latency compared with CPUs. For example, Alibaba's and Baidu's recommendation engines run on NVIDIA AI boosting their inference throughput by orders of magnitude beyond CPUs. Deep recommendators enabled Alibaba to achieve a 10% increase in click-through rates. We also announced the availability of a new GPU-accelerated supercomputer on Microsoft Azure. It enables customers for the first time to rent an entire AI supercomputer on-demand from their desk, matching the capabilities of large on-premise supercomputers that can take months to deploy. In Europe, energy company Eni announced the world's fastest industrial supercomputer based on NVIDIA GPUs. AI has even come to pizza delivery. At the National Retail Federation's Annual Conference last month, we announced Domino's as a customer deploying our platform for deep learning and data science applications helping with customer engagement and order accuracy prediction. More broadly in retail, we have seen a significant increase in the adoption of NVIDIA's edge computing offerings by large retailers for powering AI applications that reduce shrinkage, optimize logistics, and create operational efficiencies. At the SC19 Supercomputing conference, we introduced a reference design platform for GPU-accelerated ARM-based servers along with ecosystem partners, ARM, Ampere computing Fujitsu and Marvell. We made available our ARM-compatible software development kit consisting of NVIDIA CUDA-X libraries and development tools for accelerating computing. This opens the floodgates of innovation to support growing new applications from hyperscale cloud to exascale supercomputing. We also introduced NVIDIA Magnum IO, a suite of software optimized to eliminate storage and input/output bottlenecks. Magnum IO delivers up to 20x faster data processing for multi-server, multi-GPU computing nodes when working with massive data sets to carry out complex financial analysis, climate modeling and other workloads for data scientists, high-performance computing, and AI researchers. Finally, we introduced TensorRT 7, the seventh generation of our inference software development kit, which speeds up components of conversational AI by 10x compared to running on CPUs. This helps drive latency below the 300 millisecond threshold considered necessary for real-time interactions supporting our growth in conversational AI. Moving to ProVis. Revenue reached a record $331 million, up 13% year-on-year and up 2% sequentially. Full-year revenue was a record $1.21 billion, an increase of 7% from the prior year. ProVis accelerated in Q4 as the rollout of more RTX-enabled applications is driving strong upgrade cycle for our Turing GPUs. RTX is also opening up new market segment opportunities such as rendering and studio for freelance creatives. In November, V-ray, Arnold and Blender software renderers began shipping with RTX technology. These joined our leading creative and design applications including Premier Pro, Dimension, SolidWorks, CATIA, and Maya. With RTX, these applications enable enhanced creativity and notable productivity gains. In Blender Cycles for example, real-time rendering performance is boosted 4x versus a CPU. RTX is now supported by more than 40 leading creative and design applications reaching a combined user base of over 40 million. Finally, turning to automotive. Revenue was $163 million, flat from a year ago and up 1% sequentially. Full year revenue reached a record $700 million, up 9% year-on-year. During the quarter, we announced DRIVE, AGX Orin, the next-generation platform for autonomous vehicles and robots, powered by our new Orin SoC and delivering nearly 7x the performance of the previous generation Xavier SoC. The platform scales from level two plus AI-assisted driving to level five fully driverless operation. Orin is software-defined and compatible with Xavier allowing developers to leverage their investment across multiple product generations. Moving to the rest of the P&L. Q4 GAAP gross margins was 64.9% and non-GAAP was 65.4%, up sequentially, largely reflecting higher contribution of data center products. Q4 GAAP operating expenses were $1.02 billion and non-GAAP operating expenses were $810 million, up 12% and 7% year-on-year respectively. Q4 GAAP EPS was $1.53, up 66% from a year earlier. Non-GAAP EPS was $1.89, up 136% from a year ago. Q4 cash from operations was $1.46 billion. Fiscal year 2020 cash flow from operations was a record $4.76 billion. Let me share the outlook for the first quarter of fiscal 2021. This outlook does not factor in any contribution from the pending acquisition of Mellanox. We are currently working with China on obtaining regulatory approval and believe that the acquisition is likely to be completed in the first part of calendar 2020. Before discussing the figures, I want to address the impact of the coronavirus. Although it's early and the overall effect is challenging to predict, we have lowered our Q1 revenue outlook by $100 million to reflect this potential impact. We anticipate revenue to be around $3 billion, with a margin of plus or minus 2%. We expect GAAP gross margins and non-GAAP gross margins to be 65% and 65.4%, respectively, with a margin of plus or minus 50 basis points. GAAP and non-GAAP operating expenses are expected to be approximately $1.05 billion and $835 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 9% plus or minus 1% excluding discrete items. Capital expenditures are expected to be approximately $150 million to $170 million. Further, financial details are included in the CFO commentary and other information available on the IR website. In closing, let me highlight an upcoming event for the financial community. We will be at the Morgan Stanley Technology Media and Telecom Conference on March 2nd in San Francisco. With that, we will now open the call for questions. Operator, will you please poll for questions.

Hi guys. Thanks very much for the question. I guess on data center, Colette or Jensen, can you speak to some of the areas that drove the upside in the quarter? You talked about inference in both the T4 and the V100 having record quarters, but relative to your internal expectations. What were some of the businesses that drove the upside? And if you can also speak to the breadth of your customer profile today relative to a couple of years ago, how that's expanded. That would be helpful as well. Thank you.

Yes. Toshiya, thanks a lot for your question. The primary driver for our growth is AI. There are four fundamental dynamics. The first is that the AI models that are being created are achieving breakthroughs and quite amazing breakthroughs in fact in natural language understanding, in conversational AI and recommendation systems. And you know this, but for the others in the audience, recommendation systems are essentially the engine of the Internet today. The reason for this is that there are countless items worldwide, whether they are products in stores, content online, websites, or information being searched for. This amounts to hundreds of billions, trillions, and potentially hundreds of trillions of items. Additionally, there are billions of people, each with unique characteristics, and various contexts to consider. The interplay between these items, the people, the users, and the different contexts—such as location, search intent, weather, and environmental factors—significantly influences the search query results. The recommendation system has become a fundamental component of search. And some people have said this is the end of search and the beginning of the era of recommendation systems. Work is being done everywhere around the world in advancing recommendation systems. The first thing is just the breakthroughs in AI. The second is production AI, which means that whereas, we had significant and we continue to have significant opportunities in training because the model is getting larger and there are more of them, we're seeing a lot of these models going into production and that business is called inference. Inference, as Colette mentioned, grew 4 times year-over-year. It's a substantial part of our business now. But one of the interesting statistics is TensorRT 7, the entire TensorRT download this year was about 500,000, a doubling over a year ago. What most people don't understand about inference is, it's an incredibly complex computational problem, but it's an enormously complex software problem. And so, the second dynamic is moving from training or growing from training and models going into production called inference. The third is the growth not just in hyperscale anymore, but in public cloud and in vertical industries. Public cloud because of thousands of AI start-ups that are now developing AI software in the cloud. And the OpEx model works much better for them as they're younger. As they grow, they may opt to construct their own on-premise data center infrastructure, but most start-ups begin their journeys in the cloud. We're also experiencing significant success in various sectors, particularly in logistics, retail, and warehousing. Recently, we announced partnerships with major companies like USPS, American Express, and Walmart, which all deal with vast amounts of data for analytics and predictive purposes. The third dynamic is the growth beyond hyperscale and public cloud as well as in vertical industries. The last dynamic, which is generating a lot of excitement, is Edge AI. Previously, we referred to it as industries and AI where the action is, but now it is commonly known as Edge AI. There is significant enthusiasm in this area because it requires low latency inference. Often, it is not feasible to stream data to the cloud due to cost considerations or data sovereignty issues, and quick response times are essential. And so those four dynamics around AI really drove our growth.

Great. Just following up on that. As you look back at the last 12 months and the deceleration that you saw in your HPC cloud business now that you have the perspective of seeing what's driving the rebound any thoughts on what drove it to slow down in the first place? Was it just digestion? Was it sort of a handoff from image recognition to these newer applications that you just talked about? Just help us what happened there? And I guess as it pertains to the future do we think of this as a business that will have that kind of lumpiness to it?

Yes. That's a really good question. In fact, if you look backwards now we have the benefit of history. The deep recommendation systems, the natural language understanding breakthroughs, the conversational AI breakthroughs, all happened in this last year. And the velocity by which the industry captured the benefits here and continue to evolve and advance from these what so-called transformer models was really quite incredible. And so all of a sudden the number of breakthroughs in AI has just grown tremendously and these models have grown tremendously. Just this last week Microsoft announced that they've trained a neural net model in collaboration with the work that we did we call Megatron, increased the size's of the model from 7.5 billion parameters to 17.5 billion parameters. The accuracy of natural language understanding has significantly improved. AI is experiencing remarkable breakthroughs, with models expanding in size and number. Reflecting on when these breakthroughs occurred, they essentially took place over the past year. The second, we've been working on inference for some time. And until this last year very few of those inference models went into production. And now we have deep learning models across all of the hyperscalers in production. And this last year we saw really great growth in inference. The third dynamic is public clouds. All these AI startups that are being started all over the world there's about 6,000 of them, they're starting to develop and be able to put their models into production. And with the scale out of AWS we now have T4s in every single geography. So the combination of the availability of our GPUs in the cloud and the startups and vertical industries deploying their AI models into production the combination of all that just kind of came together. And all of that happened this last year. And as a result we had record sales of V100s and T4s. And so we're quite excited with the developments and it's all really powered by AI.

Thanks for taking my question and congratulations on returning the business back to the strong growth. Jensen, I wanted to ask about how you are positioned from a supply perspective for this coming year? Your main foundry is running pretty tight. How will you be able to support the 20% or so growth here that many investors are looking for? If you could just give us some commentary on how you're positioned from a supply perspective that will be very helpful.

I believe we are in a solid position regarding supply. While we may not have an excessive amount, the industry is definitely tight. We are managing a variety of processes and multiple factories through our partnership with TSMC, encompassing different manufacturing nodes that are qualified. I think we're in good shape overall, but we need to monitor the situation closely. We are also collaborating closely with all of our customers and doing forecasting. That gives us better visibility, but we all need to improve our forecasting, and we are collaborating closely with our customers and our foundry partner TSMC.

Hi. Thanks. Colette, could you provide some insight into the mix of industries and hyperscale in the data center segment? Last quarter, hyperscale was just under 50%. Can you share the current mix or any changes in that number? Thanks.

Yes. Tim, thanks for the question. Similar to what we had seen last quarter with all things growing as we moved into this quarter growth in terms of the hyperscales, continued expansion in terms of those vertical industries and even in the cloud instances, we're still looking at around the same split of 50-50 between our hyperscales and our vertical industries and maybe a little bit tad below 50 in terms of our total overall hyperscales.

Yeah. Thanks for taking the question and congratulations on the results. When I look at the numbers the growth on an absolute basis sequentially in data center was almost 2x or north of 2x what we've seen in the past as far as the absolute sequential change. Through the course of this quarter you were pretty clear that you would expect to see an acceleration of growth in the December quarter. I'm just curious of how you think about that going into the April quarter? And how we should think about that growth rate through the course of this year? If you can give us any kind of framework. And Jensen just curious, I mean as you think about the bigger picture where do you think we stand from an industry perspective today in terms of the amount or the attach rate of GPUs for acceleration in the server market? And where do you think that might be looking out over the next three years or so? Thank you.

Thanks Aaron. Colette do you want to go first?

Sure. When we think about going into Q1 and our data center overall growth, we do expect to see continued growth both going into Q1. We believe our visibility still remains positive quite well and we're expecting that as we move into it and go forward.

Yes, I believe that every query on the Internet will eventually be accelerated. Most queries will incorporate some aspect of natural language understanding. Nearly all queries will need to sift through and make recommendations from the vast number of possibilities, narrowing it down to a few suggested answers. Whether it's shopping or movies or just asking locations or even asking a question, the number of the possibilities of all the answers versus what is best answer needs to be filtered down. And that filtering process is called recommendation. That recommendation system is really complex and deep learning is going to be involved in all that. That's the first thing. I believe that every query will be accelerated. The second is as you know CPU scaling has really slowed and there's just no two ways about it. It's not a marketing thing. It's a physics thing. And the ability for CPUs to continue to scale without increasing cost or increasing power has ended. And it's called the end of Dennard scaling. And so there has to be another approach. The combination of the emergence of deep learning and the use of artificial intelligence and the amount of computation that's necessary to for every single query, but the benefit that comes along with that and the end of Dennard scaling suggests that there needs to be another approach and we believe that approach is acceleration. Now, our approach for acceleration is fundamentally different than an accelerator. Notice we never say accelerator, we say accelerated computing. And the reason for that is because we believe that a software-defined data center will have all kinds of different AIs. The AIs will continue to evolve the models will continue to evolve and get larger and a software-defined data center needs to be programmable. It is one of the reasons why we've been so successful. And if you go back and think about all the questions that have been asked of me over the last three or four years around this area the consistency of the answer has to do with the programmability of architecture, the richness of the software, the difficulties of the compilers, the ever-growing size of the models, the diversity of the models, and the advances that these models are creating. And so we're seeing the beginning of a new computing era. A fixed function accelerator is simply not the right answer. And so we believe that the future is going to be accelerated. It's going to require an accelerated computing platform and software richness is really vital so that these data centers could be software defined. And so I think that we're in the early innings, the early innings, very, very early innings of this new future. And I think that accelerated computing is going to become more and more important.

Thank you very much. Good afternoon and congratulations on the data center success. I wanted to ask you, Colette, about the $100 million you took out for coronavirus and how you arrived at that figure. Specifically, could you remind us what percentage of your gaming business comes from China in terms of units or revenue? Additionally, regarding the $100 million you adjusted in the guidance, are you considering it from a demand disruption perspective, or is it related to supply chain issues that might impact your sales? Thank you.

Sure. Thanks for the question, Matt. So, it's really still quite early in terms of trying to figure out what the impact from the overall coronavirus may be. So, we're not necessarily precise in terms of our estimate. Yes, our estimates are split between an impact possibly on gaming and data center and split pretty much equally. The $100 million also reflects what may be supply challenges or maybe overall demand. But we're still looking at those to get a better understanding of where we think that might be. In terms of our business and our business makeup, yes, our overall China business for gaming is an important piece. We have about 30% of our overall China gaming as a percentage of our overall gaming business. For data center, it moves quite a bit. They are a very important market for us, but it moves from quarter-to-quarter just based on the overall end customer mix as well as the system builders that they may choose. So, it's a little harder to determine.

Good afternoon and congratulations on the strong results and guidance. It's great to see the recent launch of your GeForce NOW service. Regarding the partnership with Tencent on cloud gaming, it seems that Tencent should have a relatively smooth transition to the cloud model since they are the largest gaming company in the world and have many games under their belt, along with their own data center infrastructure. How is the NVIDIA team planning to support this partnership? Will it involve your GeForce NOW hardware framework, or will you be providing support with your standalone GPU products? Additionally, when do you anticipate the service will become mainstream?

Let's see. Tencent is the world's largest publisher. China represents about a third of the world's gaming and transitioning to the cloud is going to be a long-term journey. And the reason for that is because Internet connection is not consistent throughout the entire market. And a lot of application still needs to be onboarded and we're working very closely with them. We're super enthusiastic about it. If we're successful long term, we're talking about an extra one billion gamers that we might be able to reach. And so, I think that this is an exciting opportunity just the long-term journey. Now here in the West, we've had a lot more opportunity to refine the connections around the world and working through the beta centers, the local hubs as well as people's WiFi routers at home. And so, we've been in data for quite some time as you know. And here at West our platform is open. And we have several hundred games now, and we're in the process of onboarding another 1,500 games. We're the only cloud platform that's based on Windows, and allows us to be able to bring PC games to the cloud. We have gained more experience in the West regarding reach, and we have many more games to onboard. I am very excited about our partnership with Tencent. Overall, GeForce NOW has seen a fantastic launch and reception, with excellent reviews. Our strategy consists of three components. There's the GeForce NOW service that we provide ourselves. We also have GeForce NOW alliances with telcos around the world to reach the regions around the world that we don't have a presence in. And that is going super well, and I'm excited about that. And then lastly, partnerships with large publishers, for example like Tencent. And we offer them our platform of course and a great deal of software and just a lot of engineering that has to be done in collaboration to refine the service.

Yeah. Good afternoon. Thank you for taking my question. I guess a question on the gaming side. If I look at your overall revenue guide, it would seem to suggest that you're looking for typically, I guess, better seasonal trends into April. And I guess can you speak to that? And then how are you seeing desktop gaming demand with recreation content becoming more available? How should we think about the growth trajectory through 2020? And then just really as a modeling question as part of gaming. With notebook now is third of the revenues, how should we think about kind of the seasonality going into April and July for that part of your business? Thank you.

Yes, C.J., I'll go first and then Colette will follow. To start, our gaming business is experiencing strong end market demand, which has remained robust throughout the year. It's evident that RTX is performing exceptionally well, and it's clear that ray tracing has become the most significant new feature in next-generation graphics. We have over 30 games that have been announced, around 11 games that have been shipped. The pipeline of ray tracing games that will be released is very exciting. Additionally, we have finally reduced the price of RTX to $299, making it very appealing for gaming. RTX is performing exceptionally well, with strong sales worldwide. The second part of our business that is changing in gaming is the amount of notebook sales and the success of Nintendo Switch has really changed the profile of our overall gaming business. Our notebook business as Colette mentioned earlier, has seen double-digit growth for eight consecutive quarters and this is unquestionably a new gaming category. Like it's a new game console. This is going to be the largest game console in the world I believe. And the reason for that is because there are more people with laptops than there are of any other device. And so the fact that we've been able to get RTX into a thin and light notebook, a thin and light notebook is really a breakthrough. And it's one of the reasons why we're seeing such great success in notebook. Between the notebook business and our Nintendo Switch business the profile of gaming overall has changed and has become more seasonal. It's more seasonal, because devices systems like notebooks and Switch are built largely in two quarters Q2 and Q3. And they build largely in Q2 and Q3, because it takes a while to build them and ship them and put them into the hubs around the world. And they tend to build it ahead of the holiday season. And so that's one of the reasons why Q3 will tend to be larger and Q4 will tend to be more seasonal and Q1 will tend to be more seasonal than the past. But the end demand is fantastic. RTX is doing great. And part of it is just a result of the success of our notebooks. I'm going to hand it over to Colette.

Yeah. So with that from a background and you think about all those different components that are within gaming, the notebook, the overall Switch and of course all of the ray tracing that we have in terms of desktop, our normal seasonality as we look at Q1 for gaming with all those three pieces is usually sequentially down from Q4 sequentially down Q4 to Q1. This year the outlook assumes it will probably be a little bit more pronounced due to the coronavirus. So, in total, we're probably looking at Q1 to be in the low double-digit sequential decline in gaming.

Hi. Thank you for taking my question and great job on the results and guidance. Regarding the coronavirus, Colette, I’m a bit surprised that the guidance range isn’t wider compared to historical data. Can you explain why the range isn’t wider and what factors contributed to the $100 million impact from the coronavirus?

So Atif, thanks for the question. Again, it's still very early regarding the coronavirus. Our thoughts are out with both the employees the families and others that are in China. So our discussions both with our supply chain that is very prominent in the overall Asia region, as well as our overall AIC makers, as well as our customers is as about as timely as we can be. And that went into our discussion and our thoughts on the overall guidance that we gave into our $100 million. We'll just have to see how the quarter comes through and we'll discuss more when we get to it. But at this time that was our best estimate at this time.

Great. Thanks for taking my question. Jensen, I'd love to hear your thoughts as to how you anticipate the inference market playing out. Historically NVIDIA's had essentially all of the training market and little of the inference market in the last 1.5 years or so. I think that's changed where you've done much better in inference. Now you have the T4 in the cloud, you have EGX at the edge. And you have Jetson, I think is what it's called at the endpoint device. How do you anticipate that market for inference developing across those various positions? And how are you aligning your portfolio for that growth?

Thank you, Will. Historically, inference has been a small part of our business because AI was still in development. Traditional AI and classical machine learning were not particularly suited for GPUs or acceleration. It was only with the advent of deep learning that the computational demands became extraordinary. And the second factor is the type of AI models that were developed. Eventually the type of models related to natural language understanding and conversational AI and recommendation systems these require instantaneous response. The faster the answer, the more likely someone is going to click on the answer. Latency is very important and can be measured. The impact on the business can be quantified directly. For instance, in conversational AI, we have successfully decreased the latency across the entire process, from speech recognition to language processing, including error fixing, generating recommendations, and transitioning to text-to-speech and voice synthesis. That entire pipeline could take several seconds. We run it so fast that it's possible now for us to process the entire pipeline within a couple of 100 to 300 milliseconds. That is in the realm of interactive conversation. Beyond that it's just simply too slow. And so the combination of AI models that are large and complex that are moving to inference moving to production. And then secondarily, conversational AI and latency-sensitive models and applications where our GPUs are essential now moving forward, I think you're going to see a lot more opportunities for us in inference. The way to think about that long-term is acceleration is essential because of end of Dennard scaling. Process technology is going to demand that we compute in a different way. And the way that AI has evolved and deep learning, it suggests that acceleration on GPUs is just a really phenomenal approach. Data centers are going to have to be software-defined. And I think as I mentioned, I think I mentioned earlier to another question, I believe that in the future the data center will all be accelerated. It will be all running AI models and it will be software defined and will be programmable and having an accelerated computing platform is essential. As you move out to the edge, it really depends on whether your platform is software-defined whether it has to be programmable or whether it's fixed functioned. There are many, many devices where the inference work is very specific. It could be something as simple as detecting changes in temperature or changes in sound or detecting motion. Those type of inference models are – could still be based on deep learning. It's function-specific. You don't have to change it very often, and you're running one or two models at any given point in time. And so those devices are going to be incredibly cost-effective. I believe those AI chips, you're going to have AI chips that are $0.50, $1 and you're just going to put it into something and it's going to be doing magical detections. The type of platforms that we're in, such as self-driving cars and robotics, the software is so complicated and there's so much evolution to come yet and it's going to constantly get better. Those software-defined platforms are really the ideal targets for us. And so we call it AI at the edge, edge computing devices. One of the edge computing devices I'm very excited about is, what people call mobile edge or basically 5G telco edge. That data center will be programmable. We recently announced that we partnered with Ericsson and we're going to be accelerating the 5G stack. And so that needs to be a software-defined data center. It runs all kinds of applications, including 5G. And those applications are going to be – those opportunities are fantastic for us.

Thanks for taking my question. Jensen, I guess, I had a question about your – how you think about the sustainability of your market position in the data center. And I guess in my simplistic view about 12 years ago you made out a consensus call to invest in CUDA software distribute it to universities. Neural networking took off and you were the de facto standard and here we are right now. And for me what's interesting to hear is that the demand that you're seeing today for your products is from markets that's just developed within the last year. And my question is like, how do you think about your investment your R&D investment strategy to make sure that you are staying way ahead of the market of the competition and even your customers who are investing in these markets too? Thank you.

Yeah. Thanks, Mark. Our company has to live 10 years ahead of the market. And so we have to imagine where the world is going to be in 10 years time, in five years time and work our way backwards. Now, our company is focused on one singular thing. The simplicity of it is incredible. And that one singular thing is accelerated computing. Accelerated computing and accelerated computing is all about the architecture of course. It's about the complicated systems that we're in, because throughput is high. When our acceleration we can – when we can compute 10, 20, 50, 100 times faster than the CPU, all of a sudden everything becomes a bottleneck. Memory's a bottleneck, networking's a bottleneck, storage is a bottleneck, everything is a bottleneck. And so we have to be – NVIDIA has to be a supremely good system designer. But the complexity of our stack which is the software stack above it is really where the investments over the course of the last – some 29 years now has really paid off. NVIDIA frankly has been an accelerated computing company since the day it was born. And so we – our company is constantly trying to expand the number of applications that we can accelerate. Of course, computer graphics was an original one and we're reinventing it with real-time ray tracing. We have rendering, which is a brand-new application that we're making great progress in. We just talked – I just mentioned 5G acceleration. Recently, we announced genomics computing. And so those are new applications that are really important to the future of computing. In the area of artificial intelligence, from image recognition to natural language understanding, to conversation to recommendation systems, to robotics and animation, the number of applications that we're going to accelerate in the field of AI is really, really broad. And each one of them are making tremendous progress and getting more and more complex. And so the question about the sustainability of our company really comes down to two dimensions. Let's assume for the fact – let's assume for now that accelerated computing is the path forward and we surely believe so. And there's a lot of evidence from the laws of physics to the laws of computer science that would suggest that accelerated computing is the right path forward. But this really basically comes down to two dimensions. One dimension is, are we continuing to expand? Are we continuing to expand the number of applications that we can accelerate? Whether it's AI, computer graphics, genomics, or 5G, we need to consider if these applications are becoming more impactful and widely adopted within the industry. Additionally, we should assess if they are increasing in complexity. If the number of applications and their impact continue to grow, along with the complexity of these applications, I believe we will perform well and maintain our position. When I explained it this way, it essentially outlines our company's growth equation. It's quite evident that there are exciting opportunities ahead.

Thanks for squeezing me in. Jensen, I just wanted to ask you on the auto side. I think at least one of your customers might have slowed out their program. Just kind of curious as you look out the next couple of years, the challenges if the OEM is moving slower? And then just any perspective on the regulatory side has anything changed there would be helpful? Thanks.

I believe the automotive industry is facing challenges, but for reasons that are well-known. Nevertheless, the drive to innovate and transform their business models has never been stronger. Each company now recognizes that developing autonomous capabilities is essential for achieving that transformation. They need to be tech companies. Every car company wants to be a tech company. They need to be a tech company. Every car company needs to be software-defined. And the platform by which to do so is an electric vehicle with autonomous capability. That car has to be software-defined. And this is their future and they're racing to get there. And so although the automotive industry is struggling in the near term, their opportunity has never been better in my opinion. The future of AV is more important than ever. The opportunity is very real. The benefits of autonomous is for whether it's safety, whether it's utility, whether it's cost reduction and productivity has never been more clear. And so, I think that I'm as enthusiastic as ever about the autonomous vehicles and the projects that we're working on are moving ahead. And so the near-term challenges of the automotive industry or whatever sales slowdown in China that they're experiencing, I feel badly about that. But the industry is as clearheaded about the importance of AV as ever.

We had an excellent quarter with strong demand for NVIDIA RTX graphics and NVIDIA AI platforms and record data center revenue. NVIDIA RTX is reinventing computer graphics and the market's response is excellent, driving a powerful upgrade cycle in both gaming and professional graphics, while opening whole new opportunities for us to serve the huge community of independent creative workers and social content creators and new markets in rendering and cloud gaming. Our data center business is enjoying a new wave of growth powered by three key trends in AI natural language understanding, conversational AI and deep recommenders are changing the way people interact with the Internet. The public cloud demand for AI is growing rapidly. And as AI shifts from development to production, our inference business is gaining momentum. We'll be talking a lot more about these key trends and much more at next month's GTC Conference in San Jose. Come join me. You won't be disappointed. Thanks everyone.