NVIDIA Corp (NVDA) — Q1 2018 Earnings Call Transcript

Thank you. Good afternoon, everyone, and welcome to NVIDIA's conference call for the first quarter of fiscal 2018. 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'd like to remind you that our call is being webcast live on NVIDIA's Investor Relations website. It's also being recorded. You can hear a replay via telephone until May 16, 2017. The webcast will be available for replay up until next quarter's conference call to discuss Q2 financial results. 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 risk factors 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, May 9, 2017, 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, Shawn. We had a strong start to the year. Highlighting our record first quarter was a near tripling of data center revenue, reflecting surging interest in artificial intelligence. Overall, quarterly revenue reached $1.94 billion, up 48% from the year earlier, down 11% sequentially and above our outlook of $1.9 billion. Growth remained broad-based, with year-on-year gains in each of our four platforms: gaming, professional visualization, data center, and automotive. From a reporting segment perspective, Q1 GPU revenue grew 45% to $1.56 billion from a year earlier, and Tegra processor revenue more than doubled to $332 million. And we recognized the remaining $43 million in revenue from our Intel agreement. Let's start with our gaming platforms. Gaming revenue in the first quarter was $1.03 billion, up 49% year on year. Gamers continue to show great interest in the Pascal-based GPUs, including gaming notebooks. Our Tegra gaming platform also did extremely well. Demand remained healthy for our enthusiast class GeForce GTX 1080 GPU, which was introduced nearly a year ago. It was complemented this past quarter with the GTX 1080 Ti, which runs 35% faster and was launched at the annual Game Developers Conference in San Francisco. The GTX 1080 Ti is designed to handle the demands of 4K gaming and high-end VR experiences. Typical of many supportive reviews, Ars Technica stated it is undoubtedly a fantastic piece of engineering, cool, quiet, and without rival. Those that demand the absolute very best in cutting-edge graphics need look no further. We also released the next generation of our TITAN-class product, the TITAN Xp, designed for enthusiasts and researchers who demand extreme performance. Gaming continues to be driven by the headlong growth in e-sports. The newest title, Overwatch, added 30 million gamers in its first year. GeForce was the graphics platform of choice at all the top e-sports tournaments, including the finals of the big four international competitions. With apologies to the start of the baseball season, e-sports are now as popular among U.S. male millennials as America's favorite pastime. More people watch gaming than HBO, Netflix, ESPN, and Hulu combined. GeForce sales remained underpinned by the steady stream of AAA titles coming onto the market, which continued to push for more chip GPU performance. In the months ahead, we'll see a series of highly anticipated blockbuster titles. Among them are Destiny 2 coming to the PC for the first time, Star Wars Battlefront II, Shadow of War, and the next installment of the Call of Duty franchise, World War II. We are excited to be working with Nintendo on its acclaimed Switch gaming system. Great reviews and reports of the system selling out in many geographies are a strong part of this platform. Moving to professional visualization, Quadro revenue grew to $205 million, up 8% from a year ago, amid continued demand for high-end real-time rendering and more powerful mobile workstations. We are seeing significant increases in professional VR solutions, driven by Quadro P6000 GPUs. Lockheed Martin is deploying Quadro to create realistic VR walkthroughs of the U.S. Navy's most advanced ships. The Marines utilize VR to train air crew personnel. And IKEA is rolling out VR to many of its stores, helping consumers configure their kitchens from a huge array of options, which they can visualize in sharp detail. Next, data center, record revenue of $409 million was nearly triple that of a year ago. The 38% rise from Q4 marked its seventh consecutive quarter of sequential improvement. Driving growth was demand from cloud service providers and enterprises building training clusters for web services, plus strong gains in high-performance computing, GRID graphics virtualization, and our DGX-1 AI supercomputer. AI has quickly emerged as the single most powerful force in technology, and at the center of AI are NVIDIA GPUs. All of the world's major internet and cloud service providers now use NVIDIA Tesla-based GPU accelerators, including AWS, Facebook, Google, IBM, and Microsoft as well as Alibaba, Baidu, and Tencent. We also announced that Microsoft is bringing NVIDIA Tesla P100 and P40 GPUs to its Azure cloud. Organizations are increasingly building out AI-enabled applications using training clusters, evident in part by growing demand for DGX-1. We are seeing a number of significant deals. Among them are Fujitsu's installment of 24 systems integrated into an AI supercomputer for RIKEN, Japan's largest research center, as well as new supercomputers at Oxford University, GE, and Audi. Working with Facebook, we announced the launch of the Caffe2 deep learning framework as well as Big Basin servers with Tesla P100 GPUs. To help meet huge demand for expertise in the field of AI, we announced earlier today plans to train 100 people this year through the NVIDIA Deep Learning Institute, representing a 10x increase from last year. Through onsite training, public events, and online courses, DLI provides practical training on the tools of AI to developers, data scientists, and researchers. Our HPC business doubled year on year, driven by the adoption of Tesla GPUs into supercomputing centers worldwide. The use of AI and accelerated computing in HPC is driving additional demand in governance intelligence, higher education research, and finance. Our GRID graphics virtualization business more than tripled, driven by growth in business services, education, and automotive. Intuit's latest TurboTax release deploys GRID to connect tax filers seeking real-time advice with CPAs. And Honda is using GRID to bring together engineering and design teams based in different countries. Finally, automotive, revenue grew to a record $140 million, up 24% year over year and 9% sequentially, primarily from infotainment modules. We continue to expand our partnerships with companies using AI to address the complex problems of autonomous driving. Since our DRIVE PX 2 AI car platform began shipping just one year ago, more than 225 car and truck makers, suppliers, research organizations, and startups have begun developing with it. That number has grown by more than 50% in the past quarter alone, the result of the platform's enhanced processing power and the introduction of Tensor RT for its in-vehicle AI inferencing. This quarter, we announced two important partnerships. Bosch, the world's largest auto supplier, which does business all over the world's carmakers, is working to create a new AI self-driving car computer based on our Xavier platform. And PACCAR, one of the largest truck makers, is developing self-driving solutions for Peterbilt, Kenworth, and DAF. We continue to view AI as the only solution for autonomous driving. The nearly infinite range of road conditions, traffic patterns, and unexpected events are impossible to anticipate with hand-coded software or computer vision alone. We expect our DRIVE PX 2 AI platform to be capable of delivering Level 3 autonomy for cars, trucks, and shuttles by the end of the year, with Level 4 autonomy moving into production by the end of 2018. Now turning to the rest of the Q1 income statement, GAAP and non-GAAP gross margins for the first quarter were 59.4% and 59.6% respectively, reflecting the decline in Intel licensing revenue. Q1 GAAP operating expenses were $596 million. Non-GAAP operating expenses were $517 million, up 17% from a year ago, reflecting hiring for our growth initiative. GAAP operating income was $554 million and non-GAAP operating income was $637 million, nearly doubling from a year ago. For the first quarter, GAAP net income was $507 million. Non-GAAP net income was $533 million, more than doubling from a year ago, reflecting revenue strength as well as gross margin and operating margin expansion. For fiscal 2018, we intend to return approximately $1.25 billion to shareholders through share repurchases and quarterly cash dividends. In Q1, we issued $82 million in quarterly cash dividends. Now turning to the outlook for the second quarter of fiscal 2018, we expect revenue to be $1.95 billion plus or minus 2%. Excluding the expiry of the Intel licensing agreement, total revenue is expected to grow 3% sequentially. GAAP and non-GAAP gross margins are expected to be 58.4%, 58.6% respectively plus or minus 50 basis points. These reflect approximately a 100 basis points impact from the expiry of the Intel licensing agreement. GAAP operating expenses are expected to be approximately $605 million. Non-GAAP operating expenses are expected to be approximately $530 million. GAAP OI&E is expected to be an expense of approximately $8 million, inclusive of additional charges from early conversions of convertible notes. Non-GAAP OI&E is expected to be an expense of approximately $3 million. GAAP and non-GAAP tax rates for the second quarter of fiscal 2018 are both expected to be 17% plus or minus 1%, excluding discrete items. Further financial details are included in the CFO commentary and other information available on our IR website. Finally, this week we are sponsoring our annual GPU Technology Conference here in Silicon Valley. Reflecting the surging importance of accelerating computing, GTC has grown to more than 7,000 attendees from 60 countries, up from 1,000 when we started eight years ago. Among its highlights, Jen-Hsun will deliver a news-filled keynote tomorrow morning. We have 550-plus talks, more than half on AI. Developers will have access to 70 labs and workshops to learn about deep learning and GPU computing. And we will award a total of $1.5 million to the six most promising companies among the 1,300 in our Inception program for AI startups. We will be hosting our annual Investor Day tomorrow and hope to see many of you there.

Thanks for taking my questions. On the HPC and data center business, clearly impressive growth, and I'm hoping that you can maybe drill down on the drivers here. I guess on the cloud side, we think of two different areas: GPU as a service versus the cloud companies' own AI effort. And I'm hoping you could help us understand to the extent where the demand is falling into either one of those buckets. And then on the enterprise side, I think there's a view out there that the enterprise is going to the cloud. So to hear you talk about training clusters for web services is very interesting, and I was hoping you could provide some more color on that demand driver.

Yeah, Mark, thanks for your question. So our GPU computing business for data center is growing very fast, and it's growing on multiple dimensions. On the one hand, there's high-performance computing using traditional numerical methods. We call that HPC. That's growing. There's in enterprise the virtualization of graphics. There's a whole lot of desktop PCs running around. However, more and more people want thinner laptops or would like to have a different type of computer and still be able to run Windows. They would like to virtualize basically their entire PC and put it in the data center. It's easier to manage. The total cost of ownership is lower. And mobile employees could enjoy their work wherever they happen to be. And so the second pillar of that is called GRID, and it's basically virtualizing the PC. And as you can tell, virtualization, mobility, and better security are all driving forces there. And then there's the Internet companies. And the Internet companies, as you mentioned, really have two pillars. There's the Internet service provision part, where they're using deep learning for their own applications, whether it's photo tagging or product recommendation or recommending a restaurant or something you should buy or personalizing your webpage, helping you with search, provisioning up the right apps, the right advertisement, language translation, speech recognition, and so on and so forth. There are a whole bunch of amazing applications that are made possible by deep learning. And so Internet service providers are using it for internal application development. And then lastly, what you mentioned is cloud service providers. And because of the adoption of GPUs and because of the success of CUDA and so many applications that can now be accelerated on GPUs, we can extend the capabilities of Moore's Law, continuing to have the benefits of computing acceleration, which in the cloud means reducing cost. That's on the cloud service provider side of the Internet companies. So that would be Amazon Web Services, the Google Compute cloud, Microsoft Azure, the IBM cloud, Alibaba's Aliyun, and many others. We're starting to see almost every single cloud service around the world standardizing on GPU architecture, so we're seeing a lot of growth there as well. So I think the essence of it all is that we're seeing data center growth in GPU computing across the board.

As a follow-up if I may, on the gaming side, what we have observed over time is that when you launch a new platform, it definitely creates demand, and you see 12 months of very good visibility into growth. And I was wondering if you see the data center numbers come in quarter after quarter here. To what extent do you think the data center demand that you're seeing is – I know probably you're only able to answer qualitatively. But to what extent do you think the data center is secular versus you have a new platform and there's just platform-driven demand?

PC gaming is growing. There's no question about that. E-sports is growing the number of players in e-sports. The number of people who are enjoying e-sports is growing. MOBA is growing. I think it's amazing the growth of MOBA and the latest games. And of course, the first-party titles, the AAA titles are doing great. Battlefield is doing great, and I'm looking forward to the new Battlefield. I'm looking forward to the new Star Wars, and I'm looking forward to the first time that Destiny is coming to the PC. As you know, it was a super hit on consoles, but the first-generation Destiny wasn't available on PC. Destiny 2 is coming to the PC, so I think the anticipation is pretty great. So I would say that PC gaming continues to grow, and it's hard to imagine people not enjoying gaming in another amazing world. So I think people are going to be amazed at how long the alternative reality of the video game market is going to continue.

Thanks for taking my question and congratulations on the solid results and execution. Jen-Hsun, for my first one, it's on the competitive landscape in your data center business. There has been more noise around FPGA or CPU or ASIC solutions also chasing the same market. What do you think is NVIDIA's sustainable competitive advantage? And what role is CUDA playing in helping you maintain this lead in this business?

Vivek, thanks for the question. First of all, it's really important to understand that the data centers, the cloud service providers, and the Internet companies all get lumped together in one conversation. But obviously, the way they use computers is very different. There are three major pillars of computing up in the cloud or in large data centers: hyperscale. One pillar is just internal use of computing systems, for developing, for training, for advancing artificial intelligence. That's a high-performance computing problem. It's a very complicated software problem. The algorithms are changing all the time. They're incredibly complicated. The work that the AI researchers are doing is not trivial, and that's why they're in such great demand. And it's also the reason why computing resources have to be provisioned to them so that they can be productive. Having a scarce AI researcher waiting around for a computer to finish simulation or training is quite unacceptable. And so that first pillar is the market that we focus on after a neural network is trained—like for example, your Alexa speaker has a little tiny network inside. And so obviously, you can do inferencing on Alexa. It does voice recognition on a hot keyword. In the long term, your car will be able to do voice recognition and speech recognition. The second pillar is inferencing. And inferencing, as it turns out, is far less complicated than training. It is about a trillion times less complicated. Once the network is trained, it can be deployed. There are thousands of networks running inside these hyperscale data centers, all detecting or inferring various things. In that regard, the current incumbent is CPUs, which currently can run every single network. This creates an opportunity for us, and it's a growth opportunity. One would suggest that FPGA is an opportunity as well. And I would urge you to come to the keynote tomorrow, and maybe I'll say a few words about that. The last pillar is cloud service providers, and that's about provisioning computing power. It's not about provisioning inferencing or GPUs; it's about provisioning computing platforms. One reason why NVIDIA CUDA and all of our software stacks have become the industry standard is that they have been tailored for deep learning and high-performance computing.

That's very helpful. And as my quick follow-up, Jen-Hsun, there is a perception that your gaming business has been driven a lot more by pricing and adoption of more premium products, and hence there could be some kind of feeling on how much gamers are willing to pay for these products. Could you address that? Are you seeing the number of gamers and the number of cards grow, and how long can they continue to reach for more premium products? Thank you.

The average selling price of the NVIDIA GeForce is about a third of a game console. That's the way to think about it. That's the simple math. People are willing to spend $200, $300, $400, $500 for a new game console, and the NVIDIA GeForce GPU gaming card is on average far less. There are people who absolutely demand the best. The reason for that is that they're driving a monitor or multiple monitors at a refresh rate well beyond a TV. If you have a 4K monitor or want 120 hertz, or some people are even driving it to 200 hertz, those kinds of displays need a lot more horsepower to drive than an average television, whether it's 1080p or 4K at 60 frames a second or 30 frames a second. The desire for high-quality graphics drives demand for more powerful hardware. Ultimately, that's the opportunity for us. I think GeForce is akin to a game console. At an equivalent ASP of $200-300, that's an opportunity ahead for GeForce.

Good afternoon, thank you for taking my questions. My first question is around gaming. I was hoping you could walk through how you're thinking about seasonality here in calendar 2017, particularly as Pascal launch calendarizes and you get both to launch in early 2018. I would love to hear your thoughts on how we should think about the trajectory of that business.

First of all, GeForce is sold unit by unit around the world and is a consumer product. It’s sold both into our installed base and is also growing our installed base. When we think about GeForce, we consider multiple factors. How much of our installed base has upgraded to Pascal? How much of our installed base is growing? What are the dynamics around gaming? Are people turning to team sports or new trends like MOBA or using games for artistic expression? It's likely related to the AAA titles that are being released. Some years the games have been fabulous, and in some years they are less incredible. These days, the production quality of games has just improved markedly, leading to several years of blockbuster titles. And then there’s the seasonal effect, where people typically buy graphics cards and game consoles during Christmas and holidays, and there are other international holidays when people receive money as gifts and they save up for a new game console or platform. Our business has similarities with other aspects of the gaming industry.

Very helpful. I guess as my follow-up, on the inventory side, that grew I think 3% sequentially. Can you walk through the moving parts there? What's driving that, and is foundry diversification part of that? Thank you.

The main reason for the growth in inventory is new products, and that's probably all I ought to say for now. I would invite you to come to GTC; the keynote tomorrow will be enlightening.

Hi, congrats on the strong quarter. Jen-Hsun, can you maybe talk a little bit about the breadth of your customer base in data center relative to maybe 12 months ago? Are you seeing the same customer group buy more GPUs, or is the growth in your business more a function of the broadening of your customer base?

Thanks, Toshiya. Let me think here. I think one year ago—maybe it was two years ago—when Jeff Dean spoke about Google using a lot of GPUs for deep learning, that was probably the only public customer we had in hyperscale data centers. Fast-forward a couple of years, we now have basically everybody: every hyperscaler in the world is using NVIDIA for deep learning applications. Most of them have now standardized on NVIDIA’s architecture in the cloud. Over the last one to two years, the hyperscale segment has transformed from being insignificant in our overall business to now one of the fastest-growing divisions.

Okay. And then as my follow-up, I had a question for Colette. Three months ago, I think you went out of your way to guide data center up sequentially. And for the July quarter, ex the Intel business going away, you're guiding revenue up 3% sequentially. Can you maybe provide some additional color for the individual segment? Thank you.

Thanks for the question. We feel good about the guidance that we're providing for Q2. We wanted to make sure that it was understood the impact of Intel has been incorporated in there. It's still too early to provide specific comments about how each of those businesses will end up. But we do believe data center is a great opportunity for us, and I think you’ll hear more about that tomorrow. We don’t have additional details on our guidance, but we feel confident about it.

Hi, thanks for taking my question and congratulations on the strong results and guide. Jen-Hsun, can you talk about the adoption of GPU in the cloud? At CES earlier this year, you announced GeForce NOW. I'm curious how the adoption of GeForce is going?

Yes, Atif, thanks for the question. GeForce NOW is really an exciting platform. It virtualizes GeForce, putting it in the cloud, turning it into a gaming PC as a service that can be streamed. I mentioned at GTC that around this time, we will likely open it up for external beta. We've been running internal beta for some time, and we have many thousands signed up for external beta trials. I'm looking forward to letting people try it. However, it's important to realize that this is still several years away from becoming a major gaming service, as we need to find the right balance between cost and quality of service, as well as the pervasiveness of virtualizing a gaming PC. We've been working on it for several years, and things like this take time. My personal experience is that almost every great thing takes about a decade. If that’s true, then we have a few more years to go.

Great. As a follow-up, with your success in Nintendo Switch, does that open up the console market with other console makers? Is that a business that is of interest to you?

Consoles are not really a business for us. It's a business for them. We're selected to work on these consoles. If it makes sense and there's strategic alignment and we can do it, it's worth noting that the opportunity cost of building a game console is quite high. It took several hundred engineers to work on it, and they could be working on something else like all of the major initiatives we have. We must be mindful of the strategic opportunity cost involved. However, with the case of the Nintendo Switch, it was too enticing an opportunity to pass up; we really wanted to do it. It's a truly innovative product, and if you ever have a chance to experience it firsthand, it’s quite delightful. I'm really glad we did it, and it was the perfect collaboration.

Yes, thanks for taking the question and congratulations on the strong execution. I wanted to follow up on some of the prepared comments on automotive with my first question: I think Colette mentioned that there were 225 car and truck development engagements underway, up 50% in the last quarter. My question is, as you engage with those partners, what's NVIDIA finding in terms of the time from engagement to revenue generation? And what are you finding with your hit rate in terms of converting those individual engagements into revenues?

I know the second part is easier. Currently, the revenue contribution is not significant, but I expect it to rise, which is why we're pursuing it. The 200 developers working on the DRIVE PX platform represent various initiatives. In the future, every aspect of transportation will be autonomous. The Amazon effect is significant—people are ordering with the expectation of quick delivery. When you send those electronic orders, a number of trucks must move goods, transitioning from larger vehicles to smaller delivery methods. Transportation is the physical Internet, the molecular Internet. Autonomous technology is crucial in increasing efficiency, reducing accidents, and supporting the burgeoning demand for instant delivery. The excitement around this technology isn't confined to branded cars; there's enthusiasm for autonomous capabilities across the industry, and that’s why we're seeing active engagement from partners.

That's very helpful color, Jen-Hsun. The follow-up is related to the data center business, and you provided a lot of useful customer information. My question is higher level. Given your unique position in nurturing AI for the last many years, could you help us understand where you believe AI adoption is overall? If we think about AI adoption in reference to a nine-inning game, where are we in that nine-inning game?

It's a great question, and there are a couple of ways to approach it. First of all, AI is going to infuse all of software and will permeate every industry. Where Marc Andreessen noted that software is going to eat the world, I suggest that AI will consume software. AI represents the automation of automation. We are likely to see the first transmission of automation, similar to how we transmit and broadcast information for the first time. Every software developer must learn deep learning and apply machine learning. Nearly every company will use AI. We have engaged in deep learning for about six years, while the rest of the world is just beginning to focus on it. Almost no companies today utilize AI in a significant way. The automotive industry is being redefined by AI, and the impact will be monumental. So we are only at the first inning of this process. The challenge lies in the fact that in tech, timelines don't follow linear patterns; rather, they grow exponentially.

Thank you. Congratulations, guys. Hey, Jen-Hsun, can you give us a state of the union on your process node and technology roadmaps? Intel made a pretty nice exposition of where they are in terms of their transistors and so on. So what's your comfort level as you see process technology and your roadmaps for new GPUs?

Yes. Hi, Hans. The world often speaks of the end of Moore's Law, but it's more about the end of two dynamics: the end of productive innovation in processor architecture and the end of Dennard scaling. These two factors create the appearance that Moore's Law has reached its conclusion. The straightforward takeaway is that we can no longer rely solely on transistor advances if we want to improve computing performance. One reason NVIDIA has not obsessed over merely having the latest transistors is that we aim for quality rather than just progress. We continue to improve performance and efficiency at multiple levels — architecture, the software stack, and the algorithms used. As a result, transistors are only one of the many tools in our toolbox. TSMC provides us with cutting-edge technology, and we push hard along with them. Yet, in the final analysis, transistors form just one part of our strategy.

Great, thank you. I've attended GTC the last couple of days. I'm really quite impressed by the breadth of presentations and the number of industries you guys are affecting. On that note, how do you think about segmenting the sales effort? Do you have a healthcare vertical, an avionics vertical, a financial vertical, or is it about having the best building blocks and letting your customers discover things?

Thank you, Joe. You are right; it's both. We develop platforms that cater to specific industries and have teams dedicated to those verticals. For example, we have a team focused on healthcare, another on internet service providers, one on manufacturing, and so on. Each of these verticals is supported by business development professionals, developer relations, and computational mathematicians who optimize their software for our GPU platforms. This vertically tailored approach allows us to cater to various ecosystems. Furthermore, we also have a horizontally integrated partner management team that works collaboratively with our OEM partners to ensure their success, expanding our reach. Our mixed approach combines vertical business development teams with a partnership model, enabling our business to scale rapidly across industries. Thanks for all the questions today. I really appreciate it. We had another record quarter. We saw growth across our four market platforms. AI is expanding. Data center nearly tripled, large ISP/CSP deployments everywhere. PC gaming is still growing, driven by e-sports and AAA titles. We have great games on the horizon. Autonomous vehicles are becoming key in all areas of transportation, as we discussed earlier. We have a prime position with our DRIVE AI computing platform. As Moore's Law continues to slow, GPU-accelerated computing is more vital than ever, positioning NVIDIA at the forefront. Don't miss tomorrow's GTC keynote; we'll have exciting news around next-generation AI, self-driving vehicles, new partnerships, and more.