NVIDIA Corp (NVDA) — Q4 2017 Earnings Call Transcript

Thank you. Good afternoon, everyone, and welcome to NVIDIA's conference call for the fourth quarter and fiscal 2017. 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 February 16, 2017. The webcast will be available for replay up until next quarter's conference call to discuss Q1 financial results. The content of today's call is NVIDIA's property. It cannot be replaced, 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 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, Arnab. We had a stellar Q4 and fiscal 2017 with records in all of our financial metrics; revenue, gross margin, operating margins and EPS. Growth was driven primarily by the Datacenter tripling with a rapid adoption of AI worldwide. Quarterly revenue reached $2.17 billion, up 55% from a year earlier, and up 8% sequentially, and above our outlook of $2.1 billion. Fiscal 2017 revenue was just over $6.9 billion, up 38% and nearly $2 billion more than fiscal 2016. Growth for the quarter and fiscal year was broad-based with record revenue in each of our four platforms, Gaming, Professional Visualization, Datacenter, and Automotive. Our full-year performance demonstrates the success of our GPU platform-based business model. From a reporting segment perspective, Q4 GPU revenue grew 57% to $1.85 billion from a year earlier. Tegra Processor revenue was up 64% to $257 million. Let's start with our Gaming platform. Q4 Gaming revenue was a record $1.35 billion, rising 66% year-on-year and up 8% from Q3. Gamers continued to upgrade to our new Pascal-based GPUs. Adding to our gaming lineup, we launched GTX 1050 class GPUs for notebooks, bringing eSports and VR capabilities to mobile at great value. The GTX 1050 and 1050 Ti were featured in more than 30 new models launched at last month's Consumer Electronics Show. To enhance the gaming experience, we announced G-SYNC HDR, a technology that enables displays which are brighter and more vibrant than any other gaming monitor. Our partners have launched more than 60 G-SYNC-capable monitors and laptops, enabling smooth play without screen tear artifact. eSports too continues to attract new gamers. Major tournaments with multi-million dollar purses are drawing enormous audiences. This last quarter, Dota 2 held its first major tournament of the season in Boston. Tickets sold out in minutes. The prize pool reached $3 million, and millions of gamers watched online. Moving to Professional Visualization, Quadro revenue grew 11% from a year ago to a record $225 million, driven by demand for high-end, real-time rendering and mobile workstations. We recently launched a family of Pascal-based GPUs designed for mobile workstations that leading OEMs are embracing. Earlier this week, we introduced Quadro GP100, which creates a new supercomputing workstation. This new type of workstation enables engineers, designers, and artists to take advantage of new technologies of photorealism, fluid simulation, and deep learning. Next, Datacenter; revenue more than tripled from a year ago, and was up 23% sequentially to $296 million. Growth was driven by AI, cloud service providers deploying GPU instances, High Performance Computing, GRID graphics virtualization, and our DGX AI supercomputing appliance. AI is transforming industries worldwide. The first adopters were hyperscale companies, like Microsoft, Facebook, and Google, which use deep learning to provide billions to customers with AI services that utilize image recognition and voice processing. The next area of growth will occur as enterprises in such fields as healthcare, retail, transportation, and finance embrace deep learning on GPUs. At November's SC 2016 Supercomputing Conference, Microsoft announced that its GPU-accelerated Microsoft Cognitive Toolkit is available both in Azure cloud and on-premises with our DGX-1 AI supercomputer. In a series of related announcements at SG 2016, we described our plans to join the Cancer Moonshot project in conjunction with the National Cancer Institute, the U.S. Department of Energy, and several national labs, to help build predictive models and guide treatment under this project. We are collaborating on a new AI framework called CANDLE, the Cancer Distributed Learning Environment. And to support this work, we unveiled our own supercomputer, the NVIDIA DGX SATURNV, which joins together 124 DGX-1 systems. It's currently the world's 28th fastest supercomputer and the number one system in energy efficiency. Our GRID, graphics, virtualization business doubled year-on-year, driven by strong growth in the education, automotive, and energy sectors. We are excited to be hosting our eighth annual GPU Technology Conference here in Silicon Valley from May 8 to May 11. This will be the year's most important event for AI and accelerated computing. And we expect it to be our largest GTC yet, attended by thousands of application developers, scientists, and academics, as well as entrepreneurs and corporate executives. Finally, in Automotive, revenue grew to a record $128 million, up 38% year-over-year. At Jen-Hsun's CES opening keynote, we demonstrated our leadership position in self-driving vehicles. With a growing list of industry players adopting our AI car platform, we also showcased AI co-pilot, a technology that will recognize a driver and their preferences, monitor their alertness, understand natural spoken language, and provide alerts in dangerous situations. One of the highlights at CES was the demonstration of our own autonomous car, dubbed BB8. More than 500 passengers took rides in the back seat without a driver behind the wheel. We announced a number of new partnerships at the show. Among them were collaborations with Bosch, the world's largest automotive supplier, and ZF, Europe's leading supplier for the truck industry, both centered on developing AI car computers with DRIVE PX 2 technology. We also announced that we're working on a cloud-to-car mapping collaboration with HERE, focused on the U.S. and Europe, and ZENRIN, focused on Japan. These complement partnerships announced in Q3 with Europe's TomTom and China's Baidu. Our mapping partnerships spanned all geographies. Jen-Hsun was joined on the CES stage by Audi of America's President, Scott Keogh. They announced the extension of our decade-long partnership to deliver cars with Level 4 autonomy starting in 2020, powered by DRIVE PX technology. Audi will deliver Level 3 autonomy in its A8 luxury sedan later this year through its zFAS system powered by NVIDIA. We also shared news at CES of our partnership with Mercedes-Benz to collaborate on a car that will be available by year's end. During the quarter, Tesla began delivering a new autopilot system powered by the NVIDIA DRIVE PX 2 platform in every new Model S and Model X, to be followed by the Model 3. Tesla's cars will be capable of fully autonomous operation via future software updates. In addition, Volvo started turning over the keys to initial customers of its Drive Me program. Its XC90 SUVs equipped with DRIVE PX 2 are capable of fully autonomous operation on designated roads in Volvo's hometown of Gothenburg, Sweden. With NVIDIA powering the market's only self-driving cars and partnerships with leading automakers, Tier 1 suppliers, and mapping companies, we feel very confident in our position as the transportation industry moves to autonomous vehicles. Now, turning to the rest of the income statement for Q4. Gross margins were at record levels with GAAP gross margins at 60%, and non-GAAP at 60.2%. These reflect the success of our platform approach, as well as strong demand for GeForce gaming GPUs and deep learning. GAAP operating expenses were $570 million. Non-GAAP operating expenses were $498 million, up 12% from a year earlier, reflecting head count-related costs for our AI growth initiatives, as well as investments in sales and marketing. We are investing into huge market opportunities, AI, self-driving cars, cloud computing, and gaming. Thus, we expect our operating expense growth rate to be in the high teens over the next several quarters. GAAP operating income was $733 million, and non-GAAP operating income was $809 million, both more than doubled from a year earlier. Our GAAP tax rate was 10%, and our non-GAAP was 13%. These rates were lower than expected, primarily due to a decrease in the amount of earnings subject to U.S. tax. GAAP EPS was $0.99. Non-GAAP EPS was $1.13. In fiscal year 2017, we returned $1 billion to shareholders through dividends and share repurchases, in line with our intention. For fiscal year 2018, we intend to return $1.25 billion to shareholders through dividends and share repurchases. Now, turning to the outlook for the first quarter of fiscal 2018. We expect revenue to be $1.9 billion, plus or minus 2%. At the mid-point, this represents 46% growth over the prior year. We expect Datacenter to grow sequentially. Our GAAP and non-GAAP gross margins are expected to be 59.5% and 59.7%, respectively, plus or minus 50 basis points. This guidance assumes that our licensing agreement with Intel ends in March and does not renew. GAAP operating expenses are expected to be approximately $603 million. Non-GAAP operating expenses are expected to be approximately $520 million. GAAP OI&E is expected to be an expense of approximately $20 million, including additional charges from the early conversions of convertible notes. Non-GAAP OI&E is expected to be an expense of approximately $4 million. GAAP and non-GAAP tax rates for the first quarter of fiscal 2018 are both expected to be 17%, plus or minus 1%, excluding any discrete items. With that, I'm going to turn it back for the operator so we can open up for questions. Please limit your questions to just one.

Can you hear me? Yeah, my apologies. Stuck on a plane here. Great, great, great results. I guess, was hoping to get a little more color on the Datacenter side. Now that we've completed a full fiscal year 2017, I would love to get some clarity on the different moving parts and contributions there. And then looking into 2018, how do you see the growth unfolding thereafter? Thank you.

Yeah, C.J., first of all, thanks a lot. Well, the single biggest mover would have to be Datacenter. I mean, when you look back on last year and we look forward, there's a lot of reasons why Datacenter business overall grew threefold. And so I would expect that to continue. There are several elements of our Datacenter business. There's the high-performance computing part. There's the AI part. There's GRID, which is graphics virtualization. There's cloud computing, which is providing our GPU platform up in the cloud for startups and enterprises and all kinds of external customers to be able to access in the cloud, as well as a brand new AI supercomputing appliance that we created last year for anybody who would like to engage deep learning and AI, but doesn't have the skills or the resources or the desire to build their own high-performance computing cluster. And so we integrated all of that with all of the complicated software stacks into an appliance that we maintain over the cloud. We call that DGX-1. And so these pieces, AI, high-performance computing, cloud computing, GRID, and DGX all contributed to our growth in Datacenter quite substantially. My sense is that, as we look forward to next year, we're going to continue to see that major trend. Of course, gaming was a very large and important factor. I expect that gaming is going to continue to be significant. And then longer-term, our position in self-driving cars is becoming more and more clear to people over time. I expect that self-driving cars will be available on the road starting this year with early movers, no later than 2020 for Level 4 by the majors, and you might even see some of them pull into 2019. Those are some of the things that we're looking forward to.

Thanks. I actually had one question for Jen-Hsun and one sort of clarification for Colette. So Jen-Hsun, where are we in the gaming cycle? It's been very strong the last few years. What proportion of your base do you think has upgraded to Pascal, and where does that usually peak before you launch your next-generation products? And then for Colette, just inventory dollars in days picked up. If you could give us some comment on that. And on OpEx productivity, you did a very good job last year, but this time you're saying OpEx will go up mid-teens. Do you still think there is operating leverage in the model? Thank you.

Well, we typically assume that we have an installed base of a couple of hundred million GeForce gamers, and we've upgraded about two quarters of them over the course of our four-year cycle. It takes about three to four years to upgrade the entire installed base. We started ramping Pascal a few quarters ago, and our data suggests that the upgrade cycle is going well, and we have plenty to go.

Thanks, Vivek. On your question on inventory, as you know, in many of our businesses, we are still carrying a significant architecture and a broad list of different products for those architectures across. We feel comfortable with our level of inventory as we look forward into fiscal year 2018 and our sales going forward. Your second question was regarding OpEx, and comparing it to where we finished in 2017 and moving into fiscal year 2018. We do have some great opportunities, large businesses for us to capture the overall TAMs, and we are going to be continuing to invest in Datacenter, specifically in AI, self-driving cars, as well as gaming. Rather than a focus on what the specific operating margin is, we're going to focus primarily just on growing the overall TAM and capturing that TAM on the top line.

Thanks for taking my question. Question back on the Datacenter, the growth was impressive. And I'm wondering, you mentioned that the hyperscale players really have embraced the products first. Are they embracing it for their own use, or deploying it for services such as machine learning as a service and enterprises tapping into this through the hyperscale guys? I'm wondering if you could provide some visibility on where you're getting that from.

On hyperscale, you're absolutely right, that there's internal use for deep learning, and then there's hosting GPU in the cloud for external high-performance computing use, which includes deep learning. Inside the hyperscalers, the early adopters are moving very fast, and everybody else has to follow. Deep learning has proven to be too effective, and everybody now understands that every hyperscaler in the world is investing heavily in deep learning. I would expect that, over the coming years, deep learning and AI will become the essential way they do their computing. When they host it in the cloud, people use it for a variety of applications, and one of the reasons the NVIDIA GPU is such a great platform is because of its broad utility. We've been working on GPU computing for almost 12 years now, and industry-after-industry, our GPU computing architecture has been embraced for countless applications. The hyperscalers will continue to adopt GPU both for internal consumption and cloud hosting for some time to come, and we're just at the beginning of that cycle.

Thank you. Any additional thoughts on the enterprise side?

Enterprise has awakened to the power of AI, and they all understand that they have a treasure trove of data they wish to analyze. In transportation, car companies create self-driving cars, requiring deep-learning models trained on massive amounts of data. We use our DGX or Tesla GPUs to train these networks, which are then deployed into self-driving applications on DRIVE PX. In healthcare, medical imaging companies recognize the importance of deep learning for cancer detection and other applications, and the list goes on and on.

Hi. Thanks for taking my question, and congratulations to the team on great results and guidance. My first question is for Jen-Hsun. Regarding the adoption of VR for gaming, I see that the price points of the headset and PC are a little high for wider adoption. Could GPU in the cloud, like GeForce NOW, be a way for the price points on VR to come down? And then I have a follow-up for Colette.

The first year of VR has seen several hundred thousand units sold. Our VRWorks SDK processes graphics in very low latency, ensuring an excellent experience. Early VR is targeted at early adopters, and we must ensure headsets are easier to use, lighter, and cheaper. The creative and professional applications of VR are also significant, with many applications transformed from expensive installations to affordable desktop solutions. We recently announced a new Quadro P5000 with VR, the world's first VR notebook. Expect rapid growth in both professional and consumer applications of VR as we proceed.

Yes. Thank you, and first of all, congratulations on a strong fiscal 2017. If I may, Jen-Hsun, the revenue beat this quarter wasn't as big as we've seen in previous periods, and most of it came from Datacenter. I understand that when the Gaming business grows as much as it has, it's harder to beat expectations by the same margin. I was wondering if you could talk about gaming demand during the holiday season.

The global PC gaming market is vibrant and growing. The number of eSports gamers is increasing. Activision Blizzard's Overwatch is very popular in Asia and globally. Our recent launch of the 1050 and 1050 Ti worldwide was successful, and I expect games like Overwatch and League of Legends to continue driving PC gaming growth. Q4 was strong, and we had a record quarter and year, something rarely seen in a large business like ours. Datacenter, in particular, grew threefold, which is impressive. We are well-positioned for the future.

Yeah, thanks. Jen-Hsun, can you talk about the evolution of artificial intelligence and the distinction between artificial intelligence, machine learning, and deep learning? How does NVIDIA's end-to-end computing platform dominate machine learning relative to the competition? Then I have a question on gross margins, if I could.

Deep learning is a breakthrough technique that falls under the broader category of machine learning, which is essential for achieving true AI. Learning is foundational to intelligence, and deep learning allows software to construct algorithms by learning from large data sets. Before deep learning, traditional machine learning methods were less effective due to their reliance on crafted features. The rise of GPUs facilitated the necessary computational power for deep learning, making modern AI algorithms viable. Deep learning is not only robust but also exceedingly useful across various industries, from healthcare to finance to self-driving cars. Our GPUs are perfectly tailored for this purpose, and we are seeing growth across many sectors.

Thank you very much. Jen-Hsun, you guys obviously have gained some business with your automotive supercomputer at Tesla recently. I was curious if you could comment on some of the application porting issues you've faced when moving features from the previous architecture onto your new architecture, and what you've learned through that process, which might be applied to future partnerships.

We are a full-stack platform. Our approach involves ensuring our architecture is optimized for neural networks, sensor fusion, and high-speed processing. This includes our semiconductor design, system software, and algorithms for perception, localization, and action planning. We've developed a deep learning SDK, integrated with mapping services globally. The self-driving car challenge is more than just computer vision; it's a complex end-to-end problem, and we have a lot of experience managing this entire stack of software, which gives us an advantage in this market.

Great. Thank you for taking the question. I wondered if you could talk a little bit about the inference market. Where are you in terms of hyperscale adoption for specialized inference type solutions, and how big do you think that market can ultimately become? Thank you.

The inference market is going to be very large. Almost every computing device in the future will have inference capabilities, from thermostats to cameras to self-driving cars and robots. I believe that in the long term, there will be a trillion devices capable of inference, connected to both edge computing devices and cloud computing servers. The largest inference platforms will likely be ARM devices, depending on the level of precision and speed desired. We will focus on markets where inference precision is mission-critical, such as self-driving cars and manufacturing robots. The opportunities in this segment are vast.

Hello. Can you hear me?

Sure.

Hi. This is Toshiya from Goldman. Thanks for taking the question, and congrats on the results. I had a question on gross margins. I think you're guiding Q1 gross margins only mildly below levels you saw in fiscal Q4 despite the royalty stream from Intel rolling over. I guess improvement of mix in Datacenter and parts of Gaming are driving this. Is that the right way to think about the puts and takes going into Q1? Should we expect gross margins to edge higher in future quarters as Datacenter becomes a bigger percentage of your business?

Yes, this is Colette. You're correct regarding Q1. The delta from Q4 to Q1 is due to only having partial recognition from Intel, which will stop in the middle of March. As we move to Q2, we will also have the absence of what we had in Q1. Meanwhile, our overall business model is shifting to higher-value platforms, which gives us opportunities for gross margin improvement. We'll see how that mix looks in Q2, but bear in mind the Intel influence.

Hi, thanks for taking my questions. First one is on the Datacenter tightening. Given the expected sequential growth in that business during the April quarter, can you talk about what products are helping to drive that? Is it possibly the DGX-1 computer box or is it more GPUs for training purposes at the hyperscale cloud datacenter?

It would have to be Tesla processors used in the cloud. There are several SKUs of Tesla processors. Some are designed for high-performance computing, while others have been optimized for molecular dynamics, astrophysics, fluid dynamics, and more. The majority of high-performance supercomputing applications have been ported to our GPUs over the last decade. Moreover, our deep learning stack, which includes cuDNN and many algorithms, is integrated with various frameworks. We also introduced our DGX-1 recently, an AI supercomputer appliance for companies that don't want to build their own infrastructure yet. All these segments of our Datacenter business are growing.

Great. Thanks for the time. Just a quick question in the auto market. At CES, you had some solutions demonstrated that showed significant declines in terms of size and offerings. How small can you ultimately make the Level 4 solution you discussed for 2020?

Currently, DRIVE PX is a one-chip solution for Level 3. With two chips, you can achieve Level 4. Our next generation, Xavier, consolidates four processors into one, allowing us to achieve Level 4 with just one chip. For Level 5, multiple processors might be needed due to their functional safety and failover functionality requirements. The true challenge lies in the end-to-end software problem, not just hardware. We are well-positioned to manage this software stack, which is where our competition really struggles.

Thanks for sneaking me in, and congratulations on the very good execution. Jen-Hsun, I wanted to come back to the Gaming platform. You've now got the business running at a $5 billion annualized run rate. Investors view that as a business built on the strength of a vibrant enthusiast market. At CES, you announced the GeForce NOW offering, which allows you to tap into the casual gamer market. What will GeForce NOW do incrementally for the opportunity that you have with your Gaming platform?

The PC gaming market is flourishing due to a dynamic shift towards eSports, which has become a popular social sport. This trend ensures that even casual gamers need decent equipment, an area where GeForce shines. GeForce NOW lets gamers with less powerful machines access high-quality gaming services through the cloud. We're excited to explore how this can capture casual gamers and bring new audiences to gaming. I'm eager to learn more from this initiative and will share insights as we progress. I want to thank all of you for following us. We had a record year, a record quarter. Most importantly, we are at the beginning of the AI computing revolution. This new form of computing, where parallel data processing is vital, makes GPU computing the ideal approach. We are seeing tremendous growth in the data center market, which has now tripled year-over-year and is becoming a significant business for us. Gaming remains vital, and self-driving cars represent an exciting growth opportunity. Our platform approach has redefined how we go to market. We find ourselves well-positioned in gaming, AI, and self-driving cars. Thank you all for following NVIDIA, and have a great year.