Executives: Arnab K. Chanda - Senior Director, Head of Investor Relations Colette M. Kress - Chief Financial Officer & Executive Vice President Jen-Hsun Huang - President, Chief Executive Officer &

Director
Analysts: Vivek Arya - Bank of America Merrill Lynch Mark Lipacis - Jefferies LLC Hans C. Mosesmann - Raymond James & Associates, Inc. Ting Pong Gabriel Ho - BMO Capital Markets (United States) C.J.

Muse - Evercore ISI Stephen Chin - UBS Securities LLC Sanjay Chaurasia - Nomura Securities International, Inc. Ross C. Seymore - Deutsche Bank Securities, Inc. Harlan Sur - JPMorgan Securities LLC Deepon Nag - Macquarie Capital (USA), Inc. Joe L.

Moore - Morgan Stanley & Co. LLC Matthew D. Ramsay - Canaccord Genuity, Inc. Christopher Hemmelgarn - Barclays Capital, Inc. Christopher Adam Jackson Rolland - FBR Capital Markets & Co.

Rajvindra S. Gill - Needham & Co. LLC Brian Alger - ROTH Capital Partners

LLC
Operator: Good afternoon, my name is Ash, and I'll be your conference operator today. I would like to welcome you to the NVIDIA Financial Results Conference Call. All lines have been placed on mute to prevent background noise.

After the speakers' remarks, there will be a question-and-answer period. I will now turn the call over to Arnab Chanda, Vice President of Investor Relations at NVIDIA. You may begin your conference. Arnab K. Chanda - Senior Director, Head of

Investor Relations: Thank you.

Good afternoon, everyone, and welcome to NVIDIA's conference call for the fourth quarter of fiscal 2016. 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 today's call is being webcast live on NVIDIA's Investor Relations website. It is also being recorded. You can hear a replay by telephone until February 24, 2016.

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 reproduced or transcribed without our prior written consent. During the course of this call, we may make forward-looking statements based on current expectations. These forward-looking statements 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 17, 2016, 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. Colette M. Kress - Chief Financial Officer & Executive

Vice President: Thanks, Arnab. Revenue reached a record in the fourth quarter totaling $1.4 billion, up 12% from a year earlier, up 7% sequentially, and above our outlook of $1.3 billion. Our full year revenue crossed above $5 billion to a record $5.01 billion, which was up 7% from the previous year.

Quarterly growth was broad-based with expansion across each of our four

market platforms: gaming, professional visualization, datacenter and automotive. Pacing games were our GTX gaming platform, our datacenter platform powered by deep learning, growing adoption and automotive. Viewed from a reporting segment perspective, Q4 GPU revenue grew 10% to $1.18 billion from an year earlier. Tegra processor revenue was up 40% to $157 million. NVIDIA's strategy remains sharply focused on creating platforms for our key markets.

Our progress stemmed from our success in creating strong products that are targeted at growth markets. In Q4, our four market platforms contributed more than 85% of revenue, up from 78% a year earlier. Our growth platforms collectively increased 23% year-over-year. First, let's start with our gaming platform. Gaming revenue rose 25% year-on-year to $810 million, with good momentum carrying forward from the previous quarter.

Maxwell-based GeForce GTX processers continue to lead our gaming growth, combined with growing anticipation for VR and the launch of holiday blockbuster games. The GeForce GTX 970 GPU stands as the world's most popular graphics card on the Steam gaming platform, and we continue to get strong traction with our GeForce GTX GPUs that power gaming notebooks. That includes the recently launched GTX 980 for notebooks, which has enabled a new category, enthusiast-class, VR-capable gaming notebooks. Excitement is growing around VR gaming, a key theme of last month's Consumer Electronics Show. We unveiled there our GeForce GTX VR-ready program to help gamers choose the best hardware for an immersive VR experience; and Oculus, which is now opened pre-orders for the Rift headsets, has exclusively certified GeForce GTX systems as being ready for VR.

GeForce sales are driven by the launch of great gaming titles, and that again proved true this past holiday season. Fallout 4 was among the standout, recording more than $750 million in sales in its first 24 hours. Other major hits were

Star Wars: Battlefront, Call

of Duty: Black Ops 3, and Rainbow Six. We remain pleased with the continued success of GeForce experience, our gaming platform that automatically optimizes your PC settings for each game and downloads the greatest game-ready drivers. At the end of January, just two-and-a-half years after its introduction, GeForce Experience subscribers stood at 76 million, up 37% on the year.

Moving to professional visualization, Quadro revenue increased 7% both sequentially and year-over-year to $203 million. The refresh cycle of workstations continued to improve during the quarter, driven in part by new workstation configurations in the market. While VR is often portrayed as a consumer play, we're also excited by its potential in enterprise, particularly in areas such as medicine, architecture, education and product design. Audi now has 20 virtual showrooms, with several hundred expected later this year, that let customers experience new models, customizing them real-time and take them for a virtual spin. In a very different application, a start-up called Surgical Theater uses flight simulator technology and multiple GPUs to allow surgeons to use VR to fly through a patient's anatomy and rehearse complicated procedures before making the first cut.

In datacenter, inclusive of Tesla and GRID, revenue rose 18% sequentially to a record $97 million, up 10% year-on-year. This reflects the extraordinary rise of deep learning, a field in which we're now engaged with nearly 3,500 companies and organizations, as well as growth in the number of high-performance computing applications that are GPU-accelerated. During the quarter, we launched key products for this market; and a number of the partners provided updates to their own work in this area that underscores the central role of the accelerated GPU platform. A key development came during November's Supercomputing 2015 Conference with the release of the latest list of the world's top 500 fastest supercomputers. It showed that more than 100 of these systems are now using accelerators.

Two-thirds of these use NVIDIA accelerators, up 50% on the year. For hyperscale datacenters, we announced a platform that lets web services companies accelerate machine learning. It consists of both the NVIDIA Tesla M40, the most powerful accelerator designed for training deep neural networks; and the NVIDIA Tesla M4, a low-power, small form-factor accelerator for machine learning inference. Web services companies have enthusiastically embraced this trend. Shortly after our hyperscale announcement, Facebook disclosed that it will use the Tesla M40 to power its next-generation computing system for machine learning applications.

And earlier this month, AliCloud, Alibaba's cloud computing business, announced it will work with us to promote China's first GPU-accelerated, cloud-based, high-performance computing platform. They joined other web services giants embracing GPUs for machine learning. During the quarter, Google outsourced its TensorFlow deep learning framework, which can be accelerated on GPUs. Microsoft Computational Network Toolkit was integrated with Azure's GPU Lab, enabling neural nets for speech recognition that are up to 10x faster than their predecessors. And IBM revealed that its Watson systems are now using GPUs.

Progress continues to be made in our GRID virtualization platform, which enables companies to deliver graphics-rich applications to employees on any device anywhere. More than 100 companies are participating in an accelerated deployment program. Turning to automotive. Automotive revenue was a record $93 million, up 18% sequentially and up 68% year-over-year. One of the biggest stories at CES was the introduction of our NVIDIA DRIVE PX 2 self-driving car platform, which utilizes artificial intelligence to address the profoundly complex challenge of autonomous driving.

As many of you saw, DRIVE PX 2 is a supercomputing platform the size of a lunch box that processes 24 trillion deep learning operations a second and delivers 8 teraflops of processing power, equivalent to that of 150 MacBook Pros. It is a flexible platform that automotive developers can scale from one to four processors, and it can utilize passive cooling or integrate seamlessly with the water cooling systems of self-driving EVs. Capable of fusing data from cameras, lidar, radar and ultrasonic sensors, it creates a full 360-degree understanding of what is happening around the vehicle. It localizes the vehicle on an HD map and it determines a safe path forward for using deep learning techniques. Volvo, well-known for its safety and reliability, will be the first to develop DRIVE PX 2, using it as the brain for its fleet of 100 self-driving cars to be publicly available next year in its hometown of Gothenburg, Sweden.

Just a couple of weeks ago, the first autonomous shuttle, the WEpod, incorporating our deep learning platform, took its inaugural trip on public roads in the Netherlands, where it can be summoned with a smartphone app. The DRIVE PX 2 launch generated enormous interest around the world from car makers, Tier 1 suppliers and others. We're now collaborating with more than 70 companies that are developing self-driving car technologies. Finally, our OEM and IP business was $198 million, up 3% sequentially, driven by the seasonal demand for notebooks. Now turning to the rest of the income statement.

GAAP quarterly gross margin was 56.5%. Non-GAAP gross margin was a record 57.2%, slightly above our outlook. GAAP and non-GAAP gross margins increased from a year ago. GAAP operating expenses for the fourth quarter was $539 million, inclusive of $34 million of restructuring and other charges. Non-GAAP operating expenses, including litigation charges, were $445 million, in line with our outlook.

For full fiscal 2016, our non-GAAP operating expenses were $1.72 billion, including litigation costs. Our focus on rigorous execution and enhancing efficiencies enabled our core operating expenses to remain flat from fiscal 2015 as we focused on expanding operating margins. GAAP operating income for the fourth quarter was $252 million. Non-GAAP operating income was $356 million, up 26% from $283 million a year earlier. GAAP net income was $207 million.

GAAP earnings per diluted share were $0.35, including $0.04 of restructuring and other charges. Non-GAAP net income was $297 million. Non-GAAP earnings per diluted share were $0.52, an increase of 21% year-over-year. Now turning to some key balance sheet items. At the end of Q4, our cash and marketable securities balance was just over $5 billion.

During the quarter, we paid $62 million in cash dividends and we closed our accelerated repurchasing agreement with an additional 4.3 million shares returned. As a result, we've returned to shareholders an aggregate of $800 million in fiscal 2016, meeting our intention we communicated at the start of the fiscal year. Over the past four years, we've returned more than $3 billion to shareholders, representing 98% of our free cash flow. As part of our ongoing commitment to deliver shareholder value through capital return, our intention is to return $1 billion in fiscal year of 2017 through quarterly cash dividends and share repurchases. For fiscal year 2016, revenue reached a record $5 billion, up 7%.

Our growth platforms increased 26% year-on-year. Non-GAAP gross margin was a record 56.8%, up 100 basis points on the year. Non-GAAP operating income grew 18% to $1.13 billion, with operating margin expansion of more than 200 basis points to 22.5%. Non-GAAP EPS grew 18%. Now turning to the outlook for the first quarter of fiscal 2017.

We remain excited about our business prospects. Gaming remains strong; and eSports, VR and new exciting gamings were lifted further. GPU-accelerated datacenters are expanding in both HPC and cloud, driven by the growth of deep learning. And autonomous driving continues to move forward. We have excellent positions in each of these growth markets.

We expect revenue for the first quarter of 2017 to be $1.26 billion, plus or minus 2%. Our GAAP and non-GAAP gross margins are expected to be 57.2% and 57.5%, respectively, plus or minus 50 basis points. GAAP operating expenses are expected to be approximately $500 million. Non-GAAP operating expenses are expected to be approximately $445 million. GAAP and non-GAAP tax rates for the first quarter of fiscal 2017 are both expected to be 19%, plus or minus 1%.

Further financial details, including the CFO Commentary and revenue by market platforms, are available on our IR website. We will now open the call for questions. We ask you that you limit your questions to just two. Ash, would you please poll for questions at this time?

Operator: Certainly. Our first question comes from the line of Vivek Arya with Bank of America Merrill Lynch.

Please proceed with your question. Vivek Arya - Bank of America

Merrill Lynch: Thanks for taking my question, and congrats on the very good growth and execution. My first question, Jen-Hsun, is on the gaming segment. For the last two years it's grown at over 30% a year. I don't know if many other multi-billion-dollar businesses in semis that are growing at this pace.

The question really is how sustainable is this growth? I understand the drivers, but could you help us ballpark that going forward over the next two years, three years, do you think of this as a 10%, 15%, 5% growth opportunity? Any guidance there would be extremely helpful. Jen-Hsun Huang - President, Chief Executive Officer & Director: Yeah. Thanks a lot, Vivek. First of all, I think you captured the essence of it in your question. GeForce is really not a chip business anymore; it's really a gaming platform business.

And when you think about it from a gaming platform business, it has to be thought of in the context of the whole gaming ecosystem and the gaming industry. It's $100 billion large. And when you think about it that way and you drive the business that way and you create value that way, I think the prospects for our growth there is still quite significant. There's several different ways that we can grow with the market. First of all, when we introduced new game platforms and this year – the last couple of years, we've introduced Maxwell; it's the most successful gaming platform we've ever introduced.

The install base of about 100 million GeForce gamers in the world has an opportunity to upgrade to a new platform. Another reason why we can grow is because the production value continues to increase; the graphics richness continues to increase. And we do that by inspiring the industry, providing a technology that helps it include our technology in a much easier way; and the way that we do that is called GameWorks. All of the physics simulations, all of the visual simulations, all the lighting simulations and all of the things that make games beautiful today are easy to include by just supporting GameWorks; and it's been an enormous success for us. And, of course, the developing countries are still doing incredibly well.

There are many countries around the world that are just starting to get into PC gaming. Southeast Asia is growing incredibly. And then not to mention that gaming is no longer just gaming. Gaming is all about sports now. But we're starting to see a new culture, a new dynamic in gaming even beyond that.

It's really becoming a platform by which people could share and a platform by which they could artistically express themselves. And if you look at some of these games today, it's something that you enjoy well beyond just playing the game. You use the game as an editor to tell stories. And so these games, like GTA 5, it's just fantastic for telling stories. And so you can see now that the gaming platform's going beyond games, it's going beyond sports; and now it's a creative platform.

And so there's just all these wonderful ways that the game industry has continued to be vibrant; and my sense is that we're going to continue to grow with it. Vivek Arya - Bank of America

Merrill Lynch: Got it. And as a follow-up, Jen-Hsun, on your automotive business. As you move from infotainment systems that are graphics-intensive to more advanced computing systems, do you think it changes the competitive environment? And what I'm referring to is we have seen Qualcomm and others enter the segment and they are making the case that they can integrate a lot of different piece parts and tie that to their processor. And I'm trying to draw a parallel with what you had on the smartphone side where you were specializing in one part, but others could integrate other parts and become more successful.

Is there going to be a similar situation in auto? Or do you think that we should read it in a different way as to how your competitive situation can evolve in autos as computing becomes a bigger part of the application?
Jen-Hsun Huang - President, Chief Executive Officer & Director: Well, we have to be mindful of competition. And there's a lot of different ways to compete, there's a lot of different ways to bring value; and surely what you described is one way to compete. Those aren't really the segments in the market that we'll address. The way that we think about infotainment is there are segments of the infotainment market, surely the parts of the market that we serve incredibly well. The richness of the displays, the number of displays and how the displays are going be used in the coming years are going to continue to expand.

You know the display costs are coming down and OLED displays are become cheaper. There's so many different ways to bring visualization into the car to enhance the driving experience. You can also imagine how artificial intelligence technology could change the way infotainment systems are even used. And one of our strategies, of course, is to bring artificial intelligence technology to enhance how the driver communicates with the car. And so there's all kinds of new technologies that we're going deploy into the infotainment system, leveraging our expertise in deep learning.

The part of the market that you're starting to talk about is the segment of the market that we really introduced into the marketplace, which is the autonomous driving computer. We started talking about it several years ago, I think it was like three years at CES, when I introduced the DRIVE PX, where we imagined that in the future a car would also have a supercomputer inside that is powered by deep learning, that's powered by artificial intelligence. And that takes in the sensor input continuously from the car, what's surrounding the car and infer from it the appropriate thing to do. That vision three years ago seemed a little bit, if you will, outer space. But I think that it is very clear now that the technology that we're bringing to bear, deep learning, is really the best approach for helping car companies go beyond ADAS, which is going be a commodity in the coming years as you can imagine, go beyond ADAS and move towards assisted driving to full autonomous driving.

And so I think that we can add a lot of value there. PX 2 was really invented to allow OEMs to scale that entire range from assisted driving, all the way to fully-assisted driving. And that's one of the reasons why we can support one chip all the way up to four chips, from passive cooling all the way up to integrating directly into the self-driving EV water cooling system that's quite available for most EV cars with liquid cooling. And so I think that our strategy there is going to work out quite well. We add a lot of value.

It's very algorithm-rich, it's very software-rich; and I think our DRIVE PX platform is really quite state-of-the-art.

Operator: Our next question comes from the line of Mark Lipacis with Jefferies. You may proceed with your question. Mark Lipacis -

Jefferies LLC: Hi. Thank you for taking my questions.

First question, on the TDS business – I'm sorry, the Tegra Development Services – you noted that that was an important driver of growth. And can you give us a little bit more color on that business? How big is that? What end markets are you working with? What are you helping customers do? Hi. Can you hear me?
Jen-Hsun Huang - President, Chief Executive Officer & Director: Actually, I'm just trying to figure out what your question was. Let me see. I guess I'd be reluctant to announce anything today, but there are semi-custom businesses that people need our help on.

And we're open for business to help select partners develop proprietary systems that leverages the wealth of technologies that we have, whether it's in visual computing or deep learning or supercomputing, so that we can create systems and products and services that the world has never had before. And so that's an area that I think is of interest to us; and it's an area that we'll likely see a lot more success in the future. But there's not much to really announce today. Mark Lipacis -

Jefferies LLC: Okay. Fair enough.

And the gross margins – second question on the gross margins – they've gone over the last three years from the low-50%s and pushing through 57% now. At what point do these asymptote out? How should we be thinking about that? Thank you. Jen-Hsun Huang - President, Chief Executive Officer & Director: So I appreciate that question. There are lot of moving parts in our gross margins business – gross margins. Of course, our enterprise businesses is richer in gross margins; our consumer business tends to be lower in gross margins.

At the highest level, the way I would think of our company's gross margins is really the nature of our business model is changing. And if you think about our business model a long time ago, it used to be a chip business, but today we're really a differentiated specialty platform business. And what I mean by platform business is it's, of course, the chips, the systems, but it's largely about the differentiated software that's on top of it. And so, increasingly, you're going to find that our businesses is software-rich, it's services rich. And if that's the case, one would think that our business model would become increasingly of that nature; and I think you're just seeing the reflection of that.

As our company continues to move towards our differentiated platforms, which was, call it, 50% just a few years ago and it's now reaching some 80% now; as we move into these specialty differentiated platforms, the software content is just much, much higher. And our customers who work with us are not buying chips for their systems, for their commodity systems, but they're looking for a platform to solve a particular problem. And the problems that we help to solve, the solution that we bring to bear, is so high-valued that I think that increasingly you should expect that – well, you should hope and I hope myself – that our gross margins continue to move along with the change in our business model.

Operator: Our next question comes from the line of Hans Mosesmann with Raymond James. You may proceed with your question.

Hans C. Mosesmann - Raymond James & Associates, Inc.: Great. Thanks, and congratulations guys. Hey, Jen-Hsun, can you give us an update on the high-performance compute side of the business? How will Pascal compare to the upcoming other solutions in the market and specifically Intel's Knights Landing? And I have a follow-up. Thanks.

Jen-Hsun Huang - President, Chief Executive Officer & Director: Yeah. Hans, thank you. Our high-performance computing business uses an architecture we call accelerated computing. Accelerated computing is a model of computing that we invented almost 10 years ago, and it's a very unique way of doing computing and it takes advantage of the strength of the CPU, as well as the advantage of the world's most parallel processor called the GPU. It is very software-intensive, it's very mathematics-intensive, it's very algorithm-intensive.

And it's a problem that when applied to some verticals can accelerate computing dramatically. We see accelerations of 5x, 10x, 20x quite normally, quite regularly. And the way that you translate this benefit to a customer is that it reduces their costs. Instead of building a supercomputer that may cost as much as $500 million, the supercomputer would be the world's best at $100 million. That's a pretty substantial reduction in expenses.

The power bill that they would spend on a regular basis would be dramatically reduced. And so datacenters and supercomputing centers save an enormous amount of money. On the other hand, the researchers see a substantial boost in their application throughput. And so that's one of the reasons why accelerated computing is doing really well. We're seeing a couple of different drivers for accelerated computing and high-performance computing, our datacenter business.

Accelerated computing itself for supercomputing applications, whether it's weather simulation or molecular dynamics simulation, continues to grow. But the big killer app that we're starting to see – and we've been cultivating this for several years now and it's now really turning the corner and going into turbocharge in growth – is deep learning. Almost in every field of science, as well as for web services companies, artificial intelligence helps them wade through, comb through just massive mounds and oceans of data to discover insight. And so deep learning and using artificial intelligence technology across all fields of science – I'm super excited about the work that's going be done in medicine – it's really going to see some great adoption. I think we mentioned that in just a couple years ago, we had 100 companies working with us in the area of deep learning and now it's ballooned to 3,500.

That's quite a large scale growth. And it's in industries, all the way from life sciences to supercomputing of course, to web services of course, to even industrial. And the application for industrial would be Internet of Things. All of these sensors all around the world collecting data needs artificial intelligence software, deep learning software to reveal insight. In terms of our positioning relative to the competition, this is an area that we have a real advantage; and we have a real advantage for several reasons.

The incremental cost to our company – the incremental cost of engineering, accelerated computing – into our normal course of running our GPU business, GeForce business is incremental. And so the system, the entire system, the 10,000 people in our company can quite easily, if you will, continue this rhythm – and quite a high velocity rhythm – of bringing on new GPUs that are great for accelerated computing and great for gaming, of course, and great for workstations in its natural course of doing work. This is not adjunct business to us; this is our core business. And at the core that is our fundamental advantage. It's a singular motion, singular execution, singular investment, singular architecture, incredibly leveraged.

And the execution, as a result, is just absolutely flawless. And so I think in the end that's our situation. Hans C. Mosesmann - Raymond James & Associates, Inc.: Very helpful. Thanks, Jen-Hsun.

Jen-Hsun Huang - President, Chief Executive Officer & Director: Yeah. Thank you very much.

Operator: Our next question comes from the line of Ambrish Srivastava of BMO Capital Markets. You may proceed with your question. Ting Pong Gabriel Ho - BMO Capital Markets (United States): Hi.

This is Gabriel Ho calling in for Ambrish. Thanks for taking my question. Just want to follow-up on your Tesla business. I think in the recent earnings call Cray actually iterated its expectation of over 50% of its $825 million revenue in the fourth quarter of this calendar year. And I think they cite its drivers one of the three supercomputers actually has used Pascal.

So how should we think about the benefit to your Tesla business?
Jen-Hsun Huang - President, Chief Executive Officer & Director: Well, Cray is a very important partner of ours. And the thing that's really exciting for me is to see them transition their business – not transition, but transform their business from one that is really focused on supercomputing centers to one that is also working on big data. This this an area where we can add a lot of value to them. We have a lot of expertise in this area. And as they continue to evolve their market footprint beyond supercomputing centers and now into large enterprises, I think they could find a lot of success.

And so they're seeing a lot of success in this area. Big data analytics is – we're square in their bulls eye and I'm quite excited for them in the work that they're doing there. And so they're a good customer for us, a great partner of ours; and I'm excited to see their ongoing success. The thing that we're all seeing is that big data analytics – the most powerful weapon for big data analytics has recently been discovered. Deep learning is just a fantastic new computing model.

It is able to discover insight that is provably now superhuman. Its dimensionality in thinking through data is unrivaled than any approach that we've learned in the past. And now that's one of the reasons why industries all over the world, from life sciences, to industry, to manufacturing, to supercomputing, are jumping onto deep learning bandwagon. And so I think their adoption of Tesla, the NVIDIA GPU, is going to be quite a successful one. Ting Pong Gabriel Ho - BMO Capital Markets (United States): Okay.

Thanks. As a follow-up, I think you seem to spend about $90 million in legal expense. And how should we think about in fiscal 2017? And also, can you give us an update where you are in the cases of Qualcomm and Samsung?
Jen-Hsun Huang - President, Chief Executive Officer & Director: Well, just as a backdrop, we litigated against Samsung last year and the expenses was what you were referring to. At the core of it, fundamentally philosophically we believe that it is inappropriate and it's wrong for Samsung to use NVIDIA's technology, technology that has cost us billions of dollars to invent, and to use it without compensating us. At the core, I just think that's just wrong.

And we think it's wrong; and that's the reason why we decided to litigate – to sue Samsung. The ITC has passed its early decisions, and we disagree with them. We're disappointed by them. It is unfortunate that the business courts couldn't see through the obviously complex data associated with the technology. But we're disappointed by it.

We've appealed for a review. And hopefully in the near term, we'll discover what the ITC will do. Bu t I still believe that it was the wrong thing to for Samsung to use technology that companies who are specialized in these fields invent and to use it without compensation. And I'm disappointed with the decision from the ITC, but so be it. Next year, we have plenty of things to go invest in and we have plenty of growth drivers.

You know that we have four powerful growth drivers in

our company: gaming is one, VR is another, artificial intelligence and self-driving cars. And we have plenty of growth drivers to go focus our company on.

Operator: Our next question comes from the line of C.J. Muse with Evercore ISI. You may proceed with your question.

C.J. Muse -

Evercore ISI: Yeah. Good afternoon. Thank you for taking my question. I guess first question on the auto side.

Trying to get my arms around how we should think about growth here in calendar 2016 off the 75% growth in 2015. I guess, if you could kind of parse between your backlog for infotainment, your outlook there, as well as what kind of ramp you see with the product development contracts on the ADAS side?
Jen-Hsun Huang - President, Chief Executive Officer & Director: So two questions there. First of all, our pipeline. We've talked about our pipeline several times. We've shipped probably 5 million, 6 million cars.

We have another 20 million, 25 million cars to ship in our pipeline. And so these are design wins that took quite a few years to have won and quite a few years of engineering to ramp into production. So we have a pretty good visibility of the pipeline and the opportunities that are ahead of us. Probably there's some market dynamics that's helpful to some of the design wins, the segments that we serve. Of course, at the time, a long time ago, it's hard to tell, but it's very clear now that the computerization of cars is a highly desirable end user feature.

And the customers, the partners that we worked with, the car companies we worked with, to computerize their cars, whether it's Audi or Tesla whose cars are heavily computerized, their growth prospects in the coming years are quite good. And so I think that that's one that we have a clear view of the pipeline, and I think the mega trends of the computerization of cars is in our favor. Now you mentioned – secondarily, we introduced this platform called DRIVE PX. It's our autonomous driving car computer platform. And the recent success of ADAS has really inspired just about every car company in the world to look beyond ADAS.

And what's beyond ADAS is self-driving cars. And it could be partially assisted, it could be mostly assisted and it could be completely assisted. And in each one of those levels of autonomy, a different amount of computation would have to be deployed. And we've created a scalable architecture that allows car companies to develop cars that are partially assisted, all the way to completely assisted. We're working with quite a large number of customers now, car companies, start-up companies, companies that are largely cloud-based and have an enormous amount of data that they could transform into an automotive service, transportation as a service.

And so we're working with a whole lot of different types of companies, and I think this is going to be an area of quite a significant industrial revolution; and arguably quite a gigantic society good in the long-term. So anyways we're working on a lot of projects there.

Operator: Our next question comes from the line of Stephen Chin with UBS. You may proceed with your question. Stephen Chin - UBS

Securities LLC: Hi, there.

Thanks for taking the questions. Jen-Hsun, the first one for you, if I could. In terms of the deep learning, machine learning opportunity, I was wondering if you could help quantify sort of the longer-term silicon TAM for the opportunity both in datacenter and automotive? And I guess more of near-term, any thoughts on what kind of development revenues could be generated for these machine learning type platforms in the near-term?
Jen-Hsun Huang - President, Chief Executive Officer & Director: Sure. Thanks. I think part of the answer is I'm not sure.

Part of the answer is I'm not sure. So with that as a disclaimer, let me tell you why I'm so enthusiastic about it. There are many problems that computer science has been trying to solve, which algorithmically is just impossible to solve. There's no known way of a human-described algorithm that completely captures the noisy and long tail of society. And it could be almost any problem.

It could be weather-related product type problems, it could be market-related type problems, it could be all kinds of purchasing-related challenges and all kinds of data. It could be life sciences, as we know that the human body is not in a perfect condition all the time. There's that randomness that plays a role in understanding molecular science. And so there are so many different types of areas where there is no simple Newtonian physics equation that can describe the nature. And so in that particular case, using an enormous amount of data to train a neural net, to train software, to rewrite the software, if you will, using an enormous amount of computation is an pretty exciting computation model.

I think this is a brand new computing model, one that is going to augment the traditional model of symbolics and computer programming. This is going to be a data-driven type of computing model. And in this particular case, GPU-accelerated computing is really quite ideal. And the computing model that we've invented some 10 years ago is really quite ideal. How big is it? I think that it could be quite significant.

And we're starting to see, of course, the type of companies that are jumping on top of the deep learning bandwagon. They're great companies, from Google, to Facebook, to Baidu, to IBM, to Alibaba, to just about every hyperscale web services company in the world is jumping on this because they have enormous amounts of data and it has very, very long tails. And traditional segmentation is too contrived of a approach to find great insight. Now the companies with a great deal of web-based data, cloud-based data has already starting to engage in this area. They're starting to implement artificial intelligence into one application after another.

And I think we've already heard them announce that it's very likely they'll put artificial intelligence into every single application they have. We're starting to see this sweep across industries. The automotive industry, of course, has the longest tail as the world is a very noisy place. And in order to create a car that can navigate through it, the long tail of a very complicated world has to be handled somehow. Writing software programs is just not going do it.

And so using an ongoing, learning artificial intelligence network could be exactly the solution for it; life sciences, industries, manufacturing, supercomputing, financial services. I mean the list goes on and on and on, and we're seeing a lot of enthusiasm there. Before everybody can use deep learning, they have to train a network. And this is an area where we have a great deal of expertise. This year, as you know, we also announced our first hyperscale inferencing engine.

It's our first end-to-end training-to-inferencing – inferencing is predictions – the application of the network. And so from training all the way to inferencing, we now have a complete architecture that is architecturally compatible. The Tesla M40 is for training and the Tesla M4 is for inferencing. The M4 is a little, tiny credit card-sized GPU, and very low powered, incredibly energy efficient; and you can connect it into just about any hyperscale data center in the world. And we're sampling customers now.

The results are quite exciting. Customers are very enthusiastic about it. And I think we could dramatically reduce the cost of datacenters all over the world as they start to ramp up artificial intelligence in their everyday workload. Stephen Chin - UBS

Securities LLC: Okay. Appreciate that color, Jen-Hsun.

And as a quick follow-up, I had one for Colette on the inventory levels. Colette, just given where inventories ended for the quarter – on a days and dollars basis it's roughly comparable to a number of quarters back when revenues were about 20% to 30% lower than where it is today. Is this a new level that the company can continue operating at just based on supply chain efficiencies? Or is this just sort of the seasonal volatility in this number or any other kind of average ASP of the products that you're carrying? Thanks. Colette M. Kress - Chief Financial Officer & Executive

Vice President: Yeah.

Stephen, thanks so much for the question. Our inventory levels that we are holding here, they're definitely going to swing a bit in terms of the mix, in terms of our platform. But what we have right now, we do have a very healthy level of inventory. And we have a great team of people managing all of those different pieces, both for the channel, for our partners and definitely for what we need to ship going forward. So I don't think we look at a number to exactly optimize in any single one quarter, as we do make sure that we are prepared for the platforms coming down the pipeline, as well as what customers need.

But you're correct, it's probably at a fairly healthy low level at this time.

Operator: Our next question comes from the line of Sanjay Chaurasia with Nomura Securities. You may proceed with your question. Sanjay Chaurasia - Nomura Securities International, Inc.: Hi, Jen-Hsun. One question on deep learning.

I was wondering if you could talk about relative opportunity sizes in training and inference part of the deep learning? Clearly, you have a strong position on the training side. I would love to hear your thoughts on the inference side. My understanding is there's a lot of custom chips are being built in the industry on the inferencing side. So I would love to get your color on what competition you will see on that front?
Jen-Hsun Huang - President, Chief Executive Officer & Director: Sure. Here's my guess.

I think long-term, training will be half of the overall market. And the reason for that is because training is so heavyweight. And in the long-term – well, not long-term, now, you're training your network constantly. You create a network, you want to improve this network as fast as you can because you have so much valuable data and so much insight that you can go after. And you deploy the network for inferencing which collects brand new data, and the world looks completely different to you.

You now collect that data and you use that data to train your network. I think that network training is going to be a continuous basis and we're seeing that absolutely. Also, there are more types of networks. The types of networks that are being created, the rate of revolution, the rate of innovation of networks, network styles, network types, network configurations, network depths; it's happening every single week. I'm actually not exactly sure how you would design a custom chip for it, which explains why there are only two chips today that are successful in inferencing.

One of them is the Intel Xeon and the other one is the Tesla M40 and M4. And so I think that the ability to adapt to new algorithms quickly is really quite vital as we go through the next several years of this artificial intelligence revolution. And there's just so much algorithms being developed; and I think you guys are reading about it constantly, new breakthroughs in AI, new breakthroughs in network design. At the moment, I just really don't know how someone would settle down and design a custom chip for it. And so I happen to believe that long-term artificial intelligence is not a chip.

Artificial intelligence is a computing model; and computing model needs processors; and processors are programmable; and these programmable processors need to have rich software development environments around it, and these platforms needs to be available all over the world. And, today, the NVIDIA accelerated computing platform is available in a PC, in a workstation, in a laptop, in the cloud, in a car, in robots, in embedded environments. And it's all exactly the same architecture. I think that that's really one of our advantages that we have the ability to be adaptable, programmable and yet we're available in literally every single computing platform form-factor you can imagine. And the accessibility of NVIDIA's architecture is literally global, worldwide and within reach for anybody.

Operator: Our next question comes from the line of Ross Seymore with Deutsche Bank. You may proceed with your question. Ross C. Seymore - Deutsche Bank Securities, Inc.: Thanks for letting me ask a question. One for either Jen-Hsun or Colette.

In your first quarter guidance it looks like the down 10% has some seasonality to it, but you also have a lot of businesses that have secular trends behind them. So I was hoping that you could provide a little bit of color on kind of seasonality versus secular, or which of the drivers would be better or worse than that 10%, acknowledging also that you lose a week of business guiding into that April quarter. Jen-Hsun Huang - President, Chief Executive Officer & Director: Yeah. First of all, I appreciate the question. I think, first of all, it's just the guidance; and it's our best view today, it's our most prudent view today.

And as we know, although there are many things we know, the worlds' a very uncertain place. There's a few things that we do know. The gaming market is quite vibrant and it continues to be quite vibrant. We monitor it literally every day, every week; and we monitor it all over the world, and it remains quite vibrant. In the coming months there are some really, really wonderful games that are coming out that we think are going be spectacularly successful, whether it's The Division or Tomb Raider or – the list goes on.

And so I think the gaming market appears to be quite vibrant. Our automotive business is vibrant. And the work that we're doing in self-driving is really gaining traction and captured the imagination of just about every car company around the world. Our deep learning work and supercomputing work, high-performance computing work is accelerating. And so in a lot of ways I understand where you're coming from, but we don't want to ignore seasonality.

Q1 is Q1. And we recognize that the market is uncertain, and we'll see how it plays out. At the end of the quarter, we'll come and report it again.

Operator: And our next question comes from the line of Harlan Sur with JPMorgan. You may proceed with your question.

Harlan Sur - JPMorgan

Securities LLC: Hi. Good afternoon and congratulations on another solid quarter. Jen-Hsun, you talked about the install base and the upgrade opportunity in gaming. I think last call you quantified it as around three-quarters of the install base that really needs a more updated GTX processor. Given the 37% growth in the gaming business last year and the success of Maxwell, it actually does appear that you did drive some meaningful upgrade in the install base.

So the question for you is, is there any updates to your views on where the install base sits at from an upgrade opportunity? And then the second question is do you get a sense that the cadence of these upgrades will be accelerating, given the advancements you and your gaming engine partners are bringing to the market every year?
Jen-Hsun Huang - President, Chief Executive Officer & Director: Yeah. Those are really good questions, and we monitor our install base pretty carefully. And currently in the install base, we basically have three architectures still in operation. We have the Maxwell architecture, and we have the Kepler architecture and we have the Tesla architecture. All of those – excuse me, the Fermi architecture.

And those architectures are all running in the install base at the moment. We've managed to upgrade about a third of the install base. Meanwhile, it is the case that ASPs of our GPUs are going up because the graphics richness and the graphics production value is going up. The quality of games – because the market for games are so high, game developers can really create much, much more beautiful games and take the risk to do that. The developing markets are growing.

The number of genres, like eSports, of games are growing. And so there's a lot of different growing vectors. And meanwhile, all of that is on top of our desire to upgrade our install base so that they can enjoy games the way that it ought to be enjoyed. And so I think there's still a fair amount of growth opportunity ahead of us; and we'll monitor it carefully and report it once a quarter.

Operator: Our next question comes from the line of Deepon Nag with Macquarie Capital.

You may proceed with your question. Deepon Nag - Macquarie Capital (USA), Inc.: Yeah. Thanks a lot, guys. So Quadro grew for the first time I think in several quarters. Can you kind of describe what drove that growth and how we should think about the growth profile for the rest of this year? Is it possible that it could grow in the mid-single digits for all of 2017?
Jen-Hsun Huang - President, Chief Executive Officer & Director: Yeah.

Deepon, thanks for the question. I was delighted myself. I'll just put that out there. We worked really hard to improve our Quadro business. The team works incredibly hard.

We've invented a new technology for rendering called Iray. It's the world's first physically modeled photorealistic renderer that is accelerated by GPU. The result of it is really quite remarkable, and they continue to add new capabilities to it. We, this last quarter, also benefited from the enthusiasm and excitement around VR, and we have VR SDKs and collaborations with just about every ISV in the world that is working on VR. And so I think there's a lot of good reasons to be enthusiastic about Quadro.

We don't believe for a moment that the design quality and design production value of movies, or games, or architecture or manufacturing will continue. We believe absolutely that's going to continue to improve in visual realism and the productivity of the engineers that are involved, the artists that are involved needs to continue to increase. And so we think that this is going to be a vibrant growth area ahead. I think that what drove recently the uptick are the OEMs refreshing workstation cycle, and I think we should enjoy some of that for the coming quarters. But I still think long-term the real opportunity, surely the market is there.

We know that more and more of design and creativity is done digitally. So at the core, the market is there. The opportunity for us is to bring new forms of rendering, new forms of design. And as you could see, Iray is for rendering and VR is for interacting with the design. These types of capabilities require just an enormous amount of GPU capability.

And I think at the core that's going to be our long-term growth drivers.

Operator: Our next question comes from the line of Joe Moore with Morgan Stanley. You may proceed with your question. Joe L. Moore - Morgan Stanley & Co.

LLC: Great. Thank you. Can you give us some color on what you're seeing in emerging markets, with a lot of macro concern about some of your end markets, notably China, and you guys keep putting up very good numbers. Can you sort of talk about that demand by geography and how you weigh the economic and currency risks over the course of this year?
Jen-Hsun Huang - President, Chief Executive Officer & Director: Sure. For some reason I still kind of tend to believe that what drives the gaming market is great games.

And I think that there's some evidence that the continued release of great games and great production value games – the vibrancy of eSports, the fact that eSports is really not just for competition, but drives the dynamic of sharing and social. Those kind of factors continue to drive our gaming business. I'm quite enthusiastic about the developing markets. Southeast Asia, for one, is really starting to adopt PC gaming quite rapidly, and it is a market that is extremely underserved. India is a market that is extremely underserved.

They are underserved because broadband Internet hasn't been available to those marketplaces until just recently. And there are surely demographics in these markets that would love to jump onto gaming. And who doesn't need a PC? And so almost every market develops around PCs quite rapidly. And so I think the way of enjoying games is so affordable by adding a GeForce GTX to a PC that you already own. It is the most affordable form of entertainment if you think about it that way.

And most of eSports are free to play, anyhow, and so much of it is, anyhow. And so it's a wonderfully affordable way to enjoy entertainment. And so I think the South – Southeast Asia, India are really quite exciting developing markets.

Operator: Our next question comes from the line of Matt Ramsay with Canaccord Genuity. You may proceed with your question.

Matthew D. Ramsay - Canaccord Genuity, Inc.: Yes. Thank you very much. Jen-Hsun, there's been a lot of, I guess, speculation about how the emergence of VR, headsets in particular, for gaming would drive your GPU business. I guess I'd just like to hear your perspective on a couple of things.

One, obviously, it requires high-end and high-ASP GPUs to support some of these applications, so of the early adopters, maybe a sense of which of those folks might already own those type of GPUs and which ones might have to upgrade in the near term. And second, how VR might penetrate the eSports phenomenon over time and drive more of those upgrades into the higher-volume mainstream parts of the gaming market. Thanks. Jen-Hsun Huang - President, Chief Executive Officer & Director: Yeah. Well, there are really two questions in your question.

They're both good. And one question is how do I feel about VR and its impact on gaming. The second part is how will VR impact our business. Let me just tell you the second part first. We don't – we don't – we're not forecasting and not assuming any upside in VR.

However, there is no way, but good, that VR will bring to our business and we'll take it a day at a time and we believe that it's going be an exciting growth driver. We believe that it's going to be helpful to our high-end GPU business. But when the time comes, it'll be a nice bonus. And so we're going to run our business as if we don't count on it. However, obviously, we care very deeply about it because we think that the experience of VR is quite amazing.

Anybody who's tried it is surprised how immersive it is, how it takes you away from where you are and into another world. You're really suspended in disbelief and it's as close to a holodeck as we've ever experienced. And so we believe strongly that VR is going to be fantastic for entertainment. It's going to be fantastic for games. We also believe that it's going to be fantastic for all of our Pro business.

I wouldn't be surprised if the segment of our business that it helps the earliest may very well be our Professional business. And the reason for that is because there are many applications that are mission-critical. And even though the headsets are not free, it's quite affordable. And for people who have powerwalls and who use large displays, VR is actually an incredible cost reduction. And so almost anybody can now have a virtual reality cave which cost tens of thousands of dollars.

Anyone can now have a powerwall, which costs tens of thousands of dollars, and now for just a few hundred dollars, have all the benefits of that. And so I think that you could tell that I'm very enthusiastic about it. We're developing a lot of fresh technology and new enabling technology to make it possible. We're working with all of the market leaders to develop the market and cultivate the market. And then from a financial perspective, we'll just see how it plays out.

And my sense is that it's going to be a really nice bonus.

Operator: Our next question comes from the line of Chris Hemmelgarn with Barclays. You may proceed with your question. Christopher Hemmelgarn - Barclays Capital, Inc.: Thanks very much for taking my question, and congrats on the good quarter. Jen-Hsun Huang - President, Chief Executive Officer & Director: Thanks a lot, Chris.

Christopher Hemmelgarn - Barclays Capital, Inc.: Yeah, I guess, first of all, I wanted to – we've talked a lot about some of the really exciting businesses that have a lot of great growth prospects for you. I thought we'd look at, as well, some that have been a little more stagnant. And I was curious to your take of, for example, the OEM business. It looks like it may be finally stabilizing after declining. How do you look at that business in the coming fiscal year? Do you think it can even get back to growth or do you expect continued declines?
Jen-Hsun Huang - President, Chief Executive Officer & Director: Well, my sense is that it will stabilize, and let me tell you why.

Our OEM business is not about gaming because it's our Gaming business. And the OEMs are not about design because that's our Design business. But the thing that some OEMs do is they include our technology to differentiate a mainstream platform from a premium platform. And so by adding NVIDIA's technology, you turn a commodity PC into a premium PC. And the experience is better, the performance is better and everything just works.

And so there's a real benefit in using it as a premium multimedia PC, if you will. And so we'll probably see continued interest in doing that. And we're delighted by that. We don't count on it, but we're delighted by it. And so my sense is that it will likely stabilize.

Operator: Our next question comes from the line of Chris Roland with FBR & Company. You may proceed with your question. Christopher Adam Jackson Rolland - FBR Capital Markets & Co.: Hey, guys, thanks for the question and great quarter. On... Jen-Hsun Huang - President, Chief Executive Officer & Director: How are you, Chris.

Christopher Adam Jackson Rolland - FBR Capital Markets & Co.: Great. Thanks. Speaking about your installed base briefly, can you guys share with us your view on GPU replacement rates, kind of how fast are they or how long are they now, and whether they're kind of shrinking or speeding up here? And also perhaps how they might differ by geography?
Jen-Hsun Huang - President, Chief Executive Officer & Director: I don't know that I have the precise – precise granularity by geography except for just a few countries, but let me address it overall. The installed base takes a couple of two years, three years to upgrade. And on the lower end, three years to four years.

On the higher end, one year to two years. And so overall, I guess – because the lower end products are higher in volume, it would probably weigh the overall average to call it three-plus years. But the ASP, of course – if you look at it from an ASP perspective, it's a little bit different, it would probably drift up. The rate of upgrade appears to be increasing. And that might also explain that the ASP of the products can – the ASP of our overall portfolio increasing.

I think the reason why it's increasing is because the size of the gaming market has now grown to a level that developers can take a fair amount of risk to add a rich content, rich production value in graphics, which they didn't used to be able to do. I mean, they now have the benefit of a large installed base of PlayStation and Xbox, Nintendo and PCs, that they could actually create content that is really, really quite beautiful and technologically rich, which drives up the adoption of higher-end GPUs because you need higher-end GPUs to process it. I think that's quite a significant factor. I think the other factor is that the game consoles, although nowhere near the performance level of our high-end GPUs, on average is higher in terms of capability than our average install base. That's actually really terrific news for us because a PC gamer who wants to enjoy games that are adapted from game consoles, which all of them are, now would have to upgrade their GPUs to enjoy at least a game console experience.

And so I think that there's a – that bottom half of our install base has a real opportunity to enjoy at least the game console experience for just $150. I mean, you could – for a $150 graphics card, you could get an experience that is superior to a game console. I mean, that's quite an amazing value. And so that I think is also another reason to spur adoption.

Operator: Our next question comes from the line of Rajvindra Gill with Needham & Company.

You may proceed with your question. Rajvindra S. Gill - Needham & Co. LLC: Yeah, thanks, and congrats as well. Just a follow-up question on the virtual reality market and how you're looking at that.

Can you talk a little bit about some of the PC requirements that are going to be necessary to use in a Facebook Oculus Rift headset when it comes out this quarter and the cost that's going to be needed for individual user? And kind of contrast that from the business model that Sony is employing, which is basically – based on my knowledge – a bundling strategy with their PlayStation 4, which wouldn't require an upgrade to the graphics card or buying a new desktop. Just wondering how you're thinking about those two different business models. Jen-Hsun Huang - President, Chief Executive Officer & Director: Yeah, there are two different questions in there. One question is what is the minimum requirement for VR today. Using today's graphics card as an example, the GTX 970, which is the most popular graphics card in the world, is the min spec for the Oculus Rift.

And the reason why, of course, is because Oculus and their PC focus wants to have the best possible experience for the early adopters of VR. And I think that's a really prudent strategy. You want to delight all of your early adopters with the best possible experience. But the way to think about it long term is that as the market continues to grow and more content comes and VR moves into the mainstream, there's no question in my mind that our $100 and $150 GeForce GTX cards will in the future be able to play VR just perfectly. And so – so this is not a question about the availability of technology or the cost of technology.

As we know, technology continues to advance and whatever experience today will continue to get more affordable long term. Whatever the Sony PlayStation does, I think, is just fantastic either way. What we would like to do is get people excited about VR. And in the final analysis, there are TV gamers and then there are – there are console gamers and there are PC gamers. And there are different genres and there are different applications and different styles, and very largely different customers.

And so I think we're just enthusiastic about VR, period. And over time, the technology will get more and more affordable.

Operator: And our final question comes from the line of Brian Alger with ROTH Capital Partners. You may proceed with your question. Brian Alger - ROTH Capital

Partners LLC: Thanks for squeezing me in here at the end.

I'll be very brief. The opportunity in automotive obviously continues to grow and your unveiling at CES was impressive by anyone's standards. However, there seems to be two approaches to the automotive market. As we move forward, there's obviously the closed system approach that Mobileye seems to be pursuing, and what appears to be more of an open architecture approach that you seem to have. Can you maybe describe how things shape up for you as you look out over the horizon with sensor fusion and the various OEMs and getting things right from a safety standards basis, et cetera? Do we need the control of a closed system or can we get it done with collaboration with others?
Jen-Hsun Huang - President, Chief Executive Officer & Director: Sure.

I appreciate the question. It's really, really a good question. This is really a matter of philosophy. And philosophically, this is how we see the world. We believe that self-driving car is not a solved problem.

And I say that as a statement of fact. I don't think anybody would dispute it. I also believe that self-driving cars is a field that's going to require the technological muscle of a very, very large industry and that no one company with a few hundred employees is going to solve it all by themselves. So the idea that a unsolved problem of such incredible, daunting levels that an entire computer industry is in the process of trying to solve could possibly be a closed system tied around a chip seems illogical to me. That's number one.

Number two, I believe that long term, our car company – the soul of the car company is the driving experience of that car. The soul of the car company is the driving experience of the car. The soul of the company is the safety record of that car. The soul of the company is the functionality of that car. And in the future, the functionality, the safety, the driving experience of the car is going to be largely software defined.

It's going to be artificial intelligence network defined. I just can't imagine great companies like BMW and Mercedes and Audi and all of the world, Toyota and the list goes on, and many great companies that are emerging into this marketplace, I just can't imagine that these companies would somehow outsource the soul of their car to a chip company. That's a second philosophical belief. And so what we've decided to do is to create an automotive autonomous car computing platform and all of the rich software that's necessary to enable this incredibly high-throughput computer to behave in a really energy-efficient way and cost-effective way, and to be able to apply our deep learning expertise so that these cars can benefit from artificial intelligence to solve these really complicated world problems. And that by partnering with every single car company in the world, that together we might be able to solve this incredibly daunting challenge and hopefully bring some society good.

And so that's our approach is the open platform, and it starts really from a philosophical approach. Now, that philosophical approach results in a very substantial technological difference. Notice that our platform is completely programmable. We have rich tools. We know that developers all over the world can very easily buy themselves a GeForce TITAN and write CUDA applications and those CUDA applications will tomorrow run on a DRIVE PX just seamlessly.

And so I think that's a really wonderful way for designers all over the world to be able to develop software which is really hard to do right now and then quickly deploy it into the card. And so our strategy is just very, very different and that's our approach. And my sense is that at the moment, it appears to be quite a good approach. Okay. I really appreciate that question.

Jen-Hsun Huang - President, Chief Executive Officer & Director: NVIDIA is the world leader in visual and accelerated computing, which is helping to create exciting growth markets like VR, AI and self-driving cars, which will transform many industries and positively impact the future of society. Our strategy is to leverage one core investment into four growth markets; gaming, professional visualization, datacenter and auto, and it's delivering results and gaining momentum. Our goal is to balance investments to capture the enormous opportunity ahead while maintaining a keen focus on improving near-term financial performance. I also want to remind everyone that our annual GPU Technology Conference will take place April 4 through 7 in San Jose. We'll be focusing on VR, artificial intelligence and autonomous driving.

We'll also be holding Analyst Day there and I look forward to seeing all of you. Thank you.

Operator: Ladies and gentlemen, that does conclude the call for today. We thank you for your participation and ask that you please disconnect your lines.