Good afternoon. My name is Victoria, and I'm your conference operator for today. Welcome to NVIDIA's financial results conference call. [Operator Instructions] I'll now turn the call over to Simona Jankowski, Vice President of Investor Relations, to begin your conference.

Thank you. Good afternoon, everyone and welcome to NVIDIA's Conference Call for the Third 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 is also being recorded. You can hear a replay by telephone until November 16, 2017. The webcast will be available for replay up until next quarter's conference call to discuss Q4 and full year fiscal 2018 financial results. The contents 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, November 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, Simona. We had an excellent quarter with record revenue in each of our four market platforms. And every measure of profit hit record levels, reflecting the leverage of our model. Data center revenue of $501 million more than doubled from a year ago and the strong adoption of our Volta platform and early traction with our inferencing portfolio. Q3 revenue reached $2.64 billion, up 32% from a year earlier, up 18% sequentially and well above our outlook of $2.35 billion. From a reporting segment perspective, GPU revenue grew 31% from last year to $2.22 billion. Tegra processor revenue rose 74% to $419 million. Let's start with our Gaming business. Gaming revenue was $1.56 billion, up 25% year-on-year and up 32% sequentially. We saw robust demand across all regions and form factors. Our Pascal-based GPUs remained the platform of choice for gamers as evidenced by our strong demand for GeForce GTX 10-Series products. We introduced the GeForce GTX 1070 Ti which became available last week. It complements our strong holiday lineup, ranging from the entry-level GTX 1050 to flagship GTX 1080 Ti. A wave of great titles is arriving for the holidays, driving enthusiasm in the market. We collaborated with Activision to bring Destiny 2 to the PC early in the month. PlayerUnknown's Battlegrounds popularly known as [indiscernible], continues to be one of the year's most successful titles. We are closely aligned with PUB G to ensure that GeForce is the best way to play the game, including bringing shadow play highlights to its 20 million players. Last weekend, Call of Duty: World War II had a strong debut. And Star Wars Battlefront 2 will be [indiscernible]. E-sports remains one of the most important secular growth drivers in the Gaming market with a fan base that now exceeds 350 million.…

[Operator Instructions] Your first question comes from the line of Toshiya Hari with Goldman Sachs.

Jen-Hsun, three months ago, you described the July quarter as a transition quarter for your data center business. And clearly, you guys have ramped very well into October. But if you can talk a little bit about the outlook for the next couple of quarters in data center? And particularly on the inferencing side. I know you guys are really excited about that opportunity. So if you can share customer feedback and what your expectations are into the next year in inferencing, that will be great.

Yes. As you know, we started ramping very strongly Volta this last quarter. And we started the ramp the quarter before. And since then, every major cloud provider from Amazon, Microsoft, Google to Baidu, Alibaba and Tencent and even recently, Oracle, has announced support for Volta and we'll be providing Volta for their internal use of deep learning as well as external public cloud services. We also announced that every major server computer maker in the world has now supported Volta and in the process of taking Volta out to market. HP and Dell and IBM and Cisco and Huawei in China, Insper in China, Lenovo, have all announced that they will be building service -- families of servers around the Volta GPU. So I think we -- this ramp is just the first part of supporting the build out of GPU-accelerated service from our company for data centers all over the world as well as cloud service providers all over the world. The applications for these GPU servers has now grown to many markets. I've spoken about the primary segments of our Tesla GPUs. There are five of them that I talk about regularly. The first one is high-performance computing where the market is $11 billion or so. It is one of the faster growing parts of the IT industry because more and more people are using high-performance computing for doing their product development or looking for insights or predicting the market or whatever it is. And today, we represent about 15% of the world's top 500 supercomputers. And I've repeatedly said, and I believe this completely, and I think it's becoming increasingly true, that every single supercomputer in the future will be accelerated somehow. So this is a fairly significant growth opportunity for us. The second is deep…

Your next question comes from the line of Stacy Rasgon with Bernstein Research.

I had a question on your Gaming seasonality into Q4. It's usually up a bit. I was wondering, do you see, I guess, drivers that would drive the lack of normal seasonal trends given how strong it's been sequentially and year-over-year? And I guess as a related question, do you see your Volta volumes in Q4 exceeding Q3?

Let's see. There's -- I'll answer the last one first and then work towards the first one. I think the guidance that we provided, we feel comfortable with. But if you think about Volta, it is just in the beginning of the ramp and it's going to ramp into the market opportunities I talked about. And so my hope is that we continue to grow. And there's every evidence that the markets that we serve, that we're addressing with Volta is -- are very large markets. And so there's a lot of reasons to be hopeful about the future growth opportunities for Volta. We've primed the pump. So cloud service providers are either announce the availability of Volta or they announce the soon availability of Volta. They're all racing to get Volta through cloud because customers are clamoring for it. The OEMs are -- we've primed the pump with OEMs and some of them are sampling now and some of them are racing to get Volta into production in the marketplace. And so I think the foundation, the demand is there. The urgent need for accelerated computing is there because Moore's Law is not scaling anymore. And then we've primed the pump. So the demand is there, there is a need, the need is there; and the foundations for getting Volta to market is primed. With respect to Gaming, what drives our Gaming business? Remember, our Gaming business is sold one at a time to millions and millions of people. And what drives our Gaming business is several things. As you know, e-sports is incredibly, incredibly vibrant and what drives -- the reason why e-sports is so unique is because people want to win and having better gear helps. The latency that they expect is incredibly low and performance drives…

Your next question comes from the line of C.J. Muse from Evercore.

I was hoping to speak in a near-term and a longer-term question. On the near term, you talked about the health on demand side for Volta. Curious if you're seeing any sort of restrictions on the supply side, whether it's wafers or access to high-bandwidth memory, et cetera. And then the longer-term question really revolves around CUDA. You've talked about that as being a sustainable competitive advantage for you guys entering the year. And now that we've moved beyond HPC and hyperscale training to more into inference and GPU as a service and you've posted GTC around the world, curious if you could extrapolate on how you're seeing that advantage and how you've seen it evolve over the year and how you're thinking about CUDA as the AI standard?

Yes, thanks a lot, C.J. Well, everything that we build is complicated. Volta is the single largest processor that humanity has ever made, at 21 billion transistors, 3D packaging, the fastest memories on the planet and all of that in a couple of hundred watts which basically says it's the most energy-efficient form of computing that the world has ever known. And one single Volta replaces hundreds of CPUs. And so it's energy-efficient, it saves an enormous amount of money and it gets this job done really, really fast which is just one of the reasons why GPU-accelerated computing is so popular now. With respect to the outlook for our architecture. As you know, we are a one architecture company. And it's so vitally important. And the reason for that is because there are so much software and so much tools created on top of this one architecture. On the inference side -- on the training side, we have a whole stack of software and optimizing compilers and numeric libraries that are completely optimized for one architecture called CUDA. On the inference side, the optimizing compilers that takes these large, huge computational graphs that come out of all of these frameworks, and these computational graphs are getting larger and larger and their numerical precision differs from one type of network to another -- from one type of application to another. Your numerical precision requirements for a self-driving car where lives are at stake to detecting where counting the number of people crossing the street, counting something versus trying to track -- detect and track something very subtle in all the weather conditions, is a very, very different problem. And so the numeric -- the types of networks are changing all the time, they're getting larger all the time. The numerical…

Your next question comes from the line of Vivek Arya with Bank of America.

Congratulations on the strong results and the consistent execution. Jen-Hsun, in the last few months, we have seen a lot of announcements from Intel, from Xylinx and others describing other approaches to the AI market. My question is how does the customer make that decision, whether to use a GPUs or an SPGA or an ASIC, right? What is -- what can remain a competitive differentiator over the longer term? And does your position in the trail market also then maybe give you a leg up when they consider solution for the inference part of the problem?

Yes, thank you, Vivek. So first of all, we have one architecture and people know that our commitment to our GPUs, our commitment to CUDA, our commitment to all of the software stacks that run on top of our GPUs, every single one of the 500 applications, every numerical solver, every CUDA compiler, every tool chain across every single operating system in every single computing platform, we are completely dedicated to it. We support the software first long as we shall live. And as a result of that the benefits to their investment in CUDA just continues to accrue. I -- you have no idea how many people send me notes about how they literally take out their old GPU, put in a new GPU. And without lifting a finger, things got 2x, 3x, 4x faster than what they were doing before, incredible value to customers. The fact that we are singularly focused and completely dedicated to this one architecture in an unwavering way allows everybody to trust us and know that we will support it for as long as we shall live, and that is the benefit of an architectural strategy. When you have four or five different architectures to support that you offer to your customers and you ask them to pick the one that they like the best, you're essentially saying that you're not sure which one is the best. And we all know that nobody's going to be able to support five architectures forever. And as a result, something has to give and it would be really unfortunate for a customer to have chosen the wrong one. And if there's five architectures, surely, over time, 80% of them will be wrong. And so I think that our advantage is that we are singularly focused. With respect to FPGAs. I think FPGAs have their place. And we use FPGAs here at NVIDIA to prototype things and -- but FPGAs is a chip design. It's able to be a chip for -- it's incredibly good at being a flexible substrate to be any chip, and so that's it's advantage. Our advantage is that we have a programming environment. And writing software is a lot easier than designing chips. And if it's within the domain that we focus on, like for example, we're not focused on network packet processing but we are very focused on deep learning. We are very focused on high performance and parallel numeric analysis. If we're focused on those domains, our platform is really quite unbeatable. And so that's how you think through that. I hope that was helpful.

Your next question comes from Atif Malik with Citi.

Colette, on the last call, you mentioned that crypto was $150 million in the OEM line in the July quarter. Can you quantify how much crypto was in the October quarter? And expectations in the January quarter directionally? And just longer-term, why should we think that crypto won't impact the gaming demand in the future? If you can just talk about the steps anybody has taken with respect to having the different mode and all that?

So in our results, in the OEM results, our specific crypto boards equated to about $70 million of revenue, which is the comparable to the $150 million that we saw last quarter.

Yes. Our longer term, Atif -- well, first of all, thank you for that. The longer-term way to think about that is crypto is small for us but not 0. And I believe that crypto will be around for some time, kind of like today. There will be new currencies emerging, existing currencies would grow in value. The interest in mining these new emerging currency crypto algorithms that emerge are going to continue to happen. And so I think for some time, we're going to see that crypto will be a small but not 0, small but not 0 part of our business. The -- when you think about crypto in the context of our company overall, the thing to remember is that we're the largest GPU computing company in the world. And our overall GPU business is really sizable and we have multiple segments. And there's data center and I've already talked about the five different segments within data center. There's [indiscernible] and even that has multiple segments within it, whether it's rendering or computed design or broadcast, in a workstation, in a laptop or in a data center, the architecture is rather different. And of course, you know that we have high performance computing, you know that we have autonomous machine business, self-driving cars and robotics. And you know of course that we have gaming; and so these different segments are all quite large and growing. And so my sense is that as although crypto will be here to stay, it will remain small not zero.

Your next question comes from the line of Joe Moore with Morgan Stanley.

Just following up on that last question. You mentioned that some of the crypto market had moved to traditional gaming. What drives that? Is there a lack of availability of the specialized crypto product? Or is it just that there's a preference driven for the gaming oriented crypto solutions?

Yes, Joe, I appreciate you asking that. Here's the reason why. So what happens is when a crypto -- when a currency -- digital currency market becomes very large, it entices somebody to build a custom ASIC for it. And of course, Bitcoin is the perfect example of that. Bitcoin is incredibly easy to design in its specialized chip form. But then what happens is a couple of different players starts to monopolize the marketplace. As a result, it chases everybody out of the mining market and it encourages a new currency to evolve, to emerge. And the new currency, the only way to get people to mine is if it's hard to mine, okay? You got to put some effort into it. However, you want a lot of people to try to mine it. And so therefore, the platform that is perfect for it, the ideal platform for digital, new emerging digital currencies turns out to be a CUDA GPU. And the reason for that is because there are several hundred million NVIDIA GPUs in the marketplace. If you want to create a new cryptocurrency algorithm, optimizing for our GPUs is really quite ideal. It's hard to do. It's hard to do, therefore, you need a lot of computation to do it. And yet there is enough GPUs in the marketplace, it's such an open platform that the ability for somebody to get in and start mining is very low barriers to entry. And so that's the cycles of these digital currencies, and that's the reason why I say that digital currency crypto usage of GPUs, crypto usage of GPUs will be small but not 0 for some time. And it's small because when it gets big, somebody will be able to build custom ASIC. But if somebody builds a custom ASIC, there will be a new emerging cryptocurrency. So ebbs and flows.

Your next question comes from the line of Craig Ellis with B. Riley.

Jen-Hsun, congratulations on data center annualizing $2 billion, it's a huge milestone. I wanted to follow-up with a question on some of your comments regarding data center partners. Because as I look back over the last five years, I just don't see any precedent for the momentum that you have in the market place right now between your server partners, white box partners, hyperscale partners that are deploying it, hosted, et cetera. And so my question is, relative to the doubling that we've seen year-on-year in each of the last two years, what does that partner expansion mean for datacenters growth? And then if I could sneak one more in, two new products just announced in the Gaming platform, the 1070 Ti and a Collector's Edition on Titan Xp. What does that mean for the gaming platform?

Yes, Craig, thanks a lot. Let's see. We have never created a product that is as broadly supported by the industries and has grown nine consecutive quarters. It has doubled year-over-year and with partnerships of the scale that we're looking at. We have just never created a product like that before. And I think the reason for that is several folds. The first is that it is true that CPU scaling has come to an end. That's just laws of physics. The end of Moore's Law is just laws of physics. And yet, the world for software development and the world -- the problems that computing can help solve is growing faster than any time before. Nobody's ever seen a large-scale planning problem like Amazon before. Nobody's ever seen a large planning problem like DD before, the number of millions of taxi rides per week is just staggering. And so nobody's ever seen large problems like these before, large-scale problems like these before; and so high performance computing and accelerated computing using GPUs has become recognized as the path forward. And so I think that that's at the highest level of the most important parameter. Second is artificial intelligence and its emergence and applications to solving problems that we historically thought were unsolvable. Solving the unsolvable problems is a real realization. I mean, this is happening across just about every industry we know, whether it's Internet service providers to healthcare, to manufacturing, to transportation, logistics. You just name it, financial services. And so I think artificial intelligence is a real tool. Deep learning is a real tool that can help solve some of the world's unsolvable problems. And I think that our dedication to high performance computing and this one singular architecture, our seven year headstart, if you will, in deep learning and our early recognition of the importance of this new computing approach, both the timing of it, the fact that it was naturally a perfect fit for the skills that we have and then the incredibly -- the incredible effectiveness of this approach, I think has really created the perfect conditions for our architecture. And so I think I really appreciate you noticing that. But this is definitely the most successful product line in the history of our company.

Your next question comes from the line of Chris Caso with Raymond James.

I have a question on the automotive market and the outlook there. And interestingly, with the other segments growing as quickly as they are, auto is becoming a smaller percentage of revenue now. And certainly, the design traction seems very positive. Can you talk about the ramp in terms of when the auto revenue, when we could see that as getting back to a similar percentage of revenue? Is that growing more quickly? Do you think that is likely to happen over the next year with some of these design wins coming out? Or is that something we should be waiting for over several years?

I appreciate that, Chris. So the way to think about that is, as you know, we've really, really reduced our emphasis on infotainment even though that's the primary part of our revenues so that we could take, literally, hundreds of engineers and including the processors that we're building now, a couple of 2,000, 3,000 engineers, working on our autonomous machine and artificial intelligence platform for this marketplace to take advantage of the position we have and to go after this amazing revolution that's about to happen. I happen to believe that everything that moves will be autonomous someday. And it could be a bus, a truck, a shuttle, a car. Everything that moves will be autonomous someday; it could be a delivery vehicle, it could be little robots that are moving around warehouses, it could be delivering a pizza to you. And we felt that those -- this was such an incredibly great challenge and such a great compute problem that we decided to dedicate ourselves to it. Over the next several years, and if you look at our DRIVE PX platform today, there's over 200 companies that are working on it. 125 startups are working on it. And these companies are mapping companies, they're Tier 1s, they're OEMs, they're shuttle companies, car companies, trucking companies, taxi companies. And this last quarter, we announced an extension of our DRIVE PX platform to include DRIVE PX Pegasus which is now the world's first auto grade full [indiscernible] platform for robotaxis. And so I think our position is really excellent and the investment has proven to be one of the best ever. And so I think in terms of revenues, my expectation is that this coming year, we'll enjoy revenues as a result of the supercomputers that customers will have to buy for training their networks, for simulating the -- all these autonomous vehicles driving and developing their self-driving cars. And we'll see fairly large quantities of development systems being sold this coming year. The year after that, I think is the year when you're going to see the robotaxis ramping and our economics in every robotaxi is several thousand dollars. And then starting, I would say, late 2022, 2021, you're going to start to see the first fully automatic autonomous cars, what people call level 4 cars, starting to hit the road. And so that's kind of how I see it. Just next year is simulation environments, development systems, supercomputers. And then the year after that is robotaxis. And then a year or two after that will be all the self-driving cars.

Your next question comes from the line of Matt Ramsey with Canaccord Genuity.

I have, I guess, a two-part question on gross margin. Colette, I remember, I don't know if maybe 3 years ago, 3.5 years ago at Analyst Day, you guys were talking about gross margins in the mid-50s and that was inclusive of the Intel payment. And now you're hitting numbers at 60% excluding that. I want to -- if you could talk a little bit about how mix of the data center business and some others drives gross margin going forward? And maybe Jen-Hsun you could talk a little bit about, you mentioned both are being such a huge chip in terms of transistor count. How you're thinking about taking costs out of that product as you ramp in into gaming next year and the effects on gross margins.

Thanks, Matt, for the question. Yes, we've been on a steady stream of increasing the gross margins over the years. But this is the evolution of the entire model. The model of the value-added platforms that we sell and inclusive of the entire ecosystem of work that we do, the software that we enable in so many of these platforms that we bring to market. Datacenter is one of them; our ProVis, another one. And if you think about all of our work that we have in terms of gaming, that overall expansion of the ecosystem. So this has been continuing to increase our gross margin. Mix is more of a statement in terms of each quarter, we have a different mix in terms of our products and some of them have a little bit of seasonality. And depending on when some of those platforms come to market, we can have a mix change within some of those subsets. It's still going to be our focus as we go forward in terms of growing gross margins as best as we can. You can see in terms of our guidance into Q4 which we feel comfortable with that guidance that we will increase it as well.

Yes. With respect to yield enhancement, the way to think about that is we do it in several ways. The first thing is I'm just incredibly proud of the technology group that we have in VLSI and they get us ready for these brand new nodes, whether it's in the process readiness, through all the circuit readiness, the packaging, the memory readiness. The readiness is so incredible -- incredibly important for us because these processors that we're creating are really, really hard. They're the largest things in the world. And so we get one shot at it. And so the team does everything they can to essentially prepare us. And by the time that we take off a product for real, we know for certain that we can build it. And so the technology team in our company is just world-class. Absolutely world-class, there's nothing like it. Then once we go into production, we get the benefit of ramping up the products. And as yields improve, we'll surely benefit from the cost. But that's not really where the focus is. I mean, in the final analysis, the real focus for us is continue to improve the software stack on top of our processors. And the reason for that is each one of our processors carry with it an enormous amount of memory and systems and networking and the whole data center. Most of our data center products, if we can improve the throughput of a data center by another 50%, or in our case, often times, we'll improve something from 2x to 4x, the way to think about that is that billion-dollar data center just improved this productivity by a factor of two. And all of the software work that we do on top of CUDA and the incredible work that we do with optimizing compilers and graph analytics, all of that stuff then all of a sudden translates to a value to our customers, not measured by dollars but measured by hundreds of millions of dollars. And that's really the leverage of accelerated computing.

Your next question comes from the line of Hans Mosesmann with Rosenblatt.

Jen-Hsun, can you comment on some of the issues this week regarding Intel and their renewed interest in getting into the graphic space and their relationship at the chip level with AMD?

Yes, thanks, Hans. There's a lot of news out there. I guess some of the things I take away, first of all, Raj leaving AMD is a great loss for AMD. And it's a recognition by Intel probably that the GPU is just incredibly, incredibly important now. And the modern GPU is not a graphics accelerator. The modern GPU, we just left the word G -- the letter G in there. But these processors are domain specific parallel accelerators. And they're enormously complex. They're the most complex processes built by anybody on the planet today. And that's the reason why IBM uses our processors for the world's largest supercomputers, that's the reason why every single cloud, every single -- every major cloud, every major server maker in the world has adopted NVIDIA GPUs. It's just incredibly hard to do. The amount of software engineer that goes on top of it is significant as well. So if you look at the way we do things, we plan a roadmap about five years out. It takes about three years to build a new generation and we build multiple GPUs at the same time. And on top of that, there's some 5,000 engineers working on system software and numeric libraries and solvers and compilers and graph analytics and cloud platforms and virtualization stacks in order to make this computing architecture useful to all of the people that we serve. And so when you think about it from that perspective, it's just an enormous undertaking. Arguably, the most significant undertaking of any processor in the world today. And that's the reason why we are able to speed up applications by a factor of 100. You don't walk in and have a new widget and a few transistors and all of a sudden, speed up applications by a factor of 100 or 50 or 20. That's just something that's inconceivable unless you do the type of innovation that we do. And then lastly, with respect to the chip that they built together, I think it goes without saying now that the energy efficiency of Pascal GeForce and the Max-Q design technology and all of the software that we created has really set a new design point for the industry. It is now possible to build a state-of-the-art gaming notebook with the most leading-edge GeForce processors and be able to deliver gaming experiences that are many times greater than a console in 4K and had that be in a laptop that's 18 millimeters thin. The combination of Pascal at Max-Q has really raised the bar. And I think that that's really the essence of it.

Unfortunately, we have run out of time. I'll now turn the call over to you for closing remarks.

We have another great quarter. Gaming is one of the fastest-growing entertainment industries and we're well-positioned for the holidays. AI is becoming increasingly widespread in many industries throughout the world and we're hoping to lead the way with all major cloud providers and computer makers moving to deploy Volta and we're building the future of autonomous driving. We expect robotaxis using our technology to hit the road in just a couple of years. We look forward to seeing many of you at the SE 17 this weekend. Thank you for joining us.

This concludes today's conference call. You may now disconnect.