Nvidia [NVDA] Conference call transcript for 2021 q1
2021-05-26 23:46:20
Fiscal: 2022 q1
Operator: Good afternoon. My name is Sumitra, and I will be your conference operator today. At this time, I would like to welcome everyone to NVIDIA's Financial Results Conference Call. All lines have been placed on mute to prevent any background noise. After the speakers' remarks, there will be a question-and-answer session. Thank you. Simona Jankowski, you may begin your conference.
Simona Jankowski: Thank you. Good afternoon, everyone, and welcome to NVIDIA's conference call for the first quarter of fiscal 2022. With me on the call today from NVIDIA are Jensen Huang, President and Chief Executive Officer; and Colette Kress, Executive Vice President and Chief Financial Officer.
Colette Kress: Thanks, Simona. Q1 was exceptionally strong with revenue of $5.66 billion and year-on-year growth accelerating to 84%. We set a record in total revenue in gaming, data center and professional visualization driven by our best ever product line ups and structural tailwinds across our businesses. Starting with gaming, revenue of $2.8 billion was up 11% sequentially and up 106% from a year earlier. This is the third consecutive quarter of accelerating year-on-year growth, beginning with the fall launch of our GeForce RTX 30 Series GPUs. Based on the Ampere GPU architecture, the 30 Series has been our most successful launch ever, driving incredible demand and setting records for both desktop and laptop GPU sales. Channel inventories are still leading, and we expect to remain supply constrained into the second half of the year. With our Ampere GPU architecture now ramping across the stack in both desktop and laptops, we expect the RTX upgrade cycle to kick into high gear as the vast majority of our GPU installed base needs to upgrade. Laptops continue to drive strong growth this quarter as we started ramping the Ampere GPU architecture across our lineup. Earlier this month, all major PC OEMs launch GeForce RTX 30 Series laptops based on the 3080, 39 -- 3070 and 3060 as part of their spring refresh.
Operator: And your first question comes from Timothy Arcuri with UBS.
Timothy Arcuri: Thanks a lot. Colette, I was wondering if you can double click a little more on the guidance. I know of the 600 to 650 in growth, you said 250 is coming from CMP and both gaming and data center will be up. Can we assume that they're up about equally, so you're getting about 200 roughly from each of those? And I guess second part of that is within data center, I'm wondering can you speak to the networking piece. It sounds like maybe it was up a bit more modestly than it's about the past few quarters. I'm just wondering what the outlook is there. Thanks.
Colette Kress: Yes. Thanks so much for the question on our guidance. So I first want to start off with we see demand really across all of our markets. All of our different market platforms we do plan to grow sequentially. You are correct, that we are expecting a increase in our CMP. And outside of our CMP growth, we expect the lion share of our growth to come from our data center and gaming. In our data center business, right now our product lineup couldn't be better. We have a strong overall portfolio, both for training and for inferencing. And we're seeing strong demand across our hyperscales and vertical industries. We've made a deliberate effort on the gaming perspective to supply to our gamers the cards that they would like, given the strong demand that we see. So that will also support the sequential growth that we are receiving. So you are correct, that we do see growth sequentially coming from data center and gaming, both contributing quite well to our growth.
Timothy Arcuri: Thanks a lot, Colette.
Colette Kress: I didn't answer your second question, my apologies, on Mellanox. Additionally, Mellanox is an important part of our data center. It is quite integrated with our overall products. We did continue to see growth this last quarter and we are also expecting them to sequentially grow as we move into Q2. They are a smaller part of our overall data center business, but again, we do expect them to grow.
Operator: And your next question comes from C.J. Muse with Evercore ISI.
C.J. Muse: Yes, good afternoon. Thank you for taking the question. In your prepared remarks, I think I heard you talk about a vision for acceleration in data center as we go through the year. And as you think about the purchase obligations that you reported up 45% year-on-year, how much of that is related to long lead time data center and how should we interpret that in terms of what kind of ramp we could see in the second half, particularly as you think about perhaps adding more growth from enterprise on top of what was hyperscale driven growth in the April quarter? Thank you.
Colette Kress: Let me take the first part of your question regarding our purchasing of inventory and what we're seeing in just both our purchase commitments and our inventory. The market has definitely changed to where long lead times are required to build out our data center products. So we're on a steady stream to both commit longer term so that we can make sure that we can serve our customers with the great lineup of products that we have. So yes, a good part of those purchase commitments is really about those long lead times, the components to create the full systems. I will turn the second part of the question over to Jensen.
Jensen Huang: What was the second part of the question, Colette?
Colette Kress: Second part of the question was, what do we see in the second half as it relates to the lineup of enterprise. And we articulated in our pre remarks regarding that we're seeing an acceleration. Thank you.
Jensen Huang: Yes. We're seeing strength across the board in data centers, and we're seeing strengthening demand. CGR, our data center as you know is accelerated with a range of applications. From scientific computing, both physical and life sciences, data analytics, and classical machine learning, cloud computing and cloud graphics, which is becoming more important because of remote work. And very importantly, AI both for training as well as inferencing for classical machine learning models, like XGBoost all the way to deep learning we base models like conversational AI, natural language, understanding recommender systems, and so on. And so we have a large suite of applications in our NVIDIA AI and NVIDIA HPC as the case accelerate these applications and data centers. They run on systems that range from HGX for the hyperscalers to DGX for on-prem to EGX for Enterprise and Edge, all the way out to AGX autonomous systems. And this quarter, at GTC, we announced one of our largest initiatives and it's taken us several years. You've seen working on it in open -- on the open over the course of the last several years, and it's called EGX it's our Enterprise AI platform. We're democratizing AI, we're bringing it out in cloud, we're bringing it to enterprises, and we're bringing it out to the Edge. And the reason for that is because the vast majority of the world at the automation that has to be done has data that has data sovereignty issues, or data rate issues that can't move to the cloud easily. And so we have to move the computing to their premise and oftentimes all the way up to the edge. The platform has to be secure, has to be confidential, it has to be remotely manageable. And of course, it has to be high performance, and it has to be cloud native. And that's the built -- be built like the cloud, the modern way of doing cloud data centers. And so these stacks has to be modern. On the one hand, it has to be integrated into classical enterprise systems on the other hand, which is the reason why we work so closely with VMware and accelerated VMware's operating system, data center operating system, software defined data center, stacks on BlueField. Meanwhile, we ported NVIDIA AI, NVIDIA HPC on to VMware so that they could run distributed large scale accelerated computing for the very first time. And that partnership, that partnership was announced at VMworld. It was announced at GTC and we're in the process of going to market with all of our enterprise partners, their OEMs, their value added resellers, their service -- their solution integrators all over the world. And so, this is a really large endeavor and the early indications of it are really exciting. And the reason for that is because as you know, our data center business is more than 50% vertical industry enterprise already. It's more than 50% vertical industry enterprises already and then by creating this easy to adapt and easy to integrate stack, it's going to allow them to move a lot faster. And so this is the next major wave of AI. This is a very exciting part of our initiative. And it's something that I've been working on for -- we've been working on for quite a long time. And so I'm delighted with the launch this quarter at GTC. The rest of the data centers do agree to. As Colette mentioned, hyperscale demand is strengthening. We're seeing that for computing and networking. You know that the world's cloud data centers are moving to deep learning, because every small percentage that they get out of predictive inference drives billions and billions of dollars of economics for them. And so the movement towards deep learning shifts the data center workload away from CPUs, because accelerators are so important. And so hyperscale, we're seeing great traction and great demand. And then lastly, supercomputing. Supercomputer centers all over the world are building out. And we're really in a great position there to fuse for the very first time simulation based approaches as well as data driven based approaches what is called artificial intelligence. And so across the board, our data center is gaining momentum. And we see -- we just see great strength right now and it's growing strength. And we're really set up for years of growth in data center. This is the largest segment of computing as you know, and this segment of computing is going to continue to grow for some time to come.
Operator: And your next question comes from Aaron Rakers with Wells Fargo.
Aaron Rakers: Yes, thanks for taking the questions. Congratulations on the results. I'm going to first slip in two of them here. First of all, Colette, I think in the past you talked about how much of your gaming install base is kind of on the pre-race ray tracing platforms are really kind of the context behind the upgrade cycle that's still part of us. That's kind of question one. And then, on the heels of the last question, I was just curious things like VMware's project model ray as we think about the BlueField-2 product and BlueField-3, how should we think about those starting to become or when should they become really material incremental revenue growth contributors for the company? Thank you.
Colette Kress: So, yes, we have definitely discussed in terms of the great opportunity that we have in front of us of folks moving to our ray traced GPUs. And we're in the early stages of that. We've had a strong cycle already, but still we probably have approximately 15% moving up a little bit from that at this time. So it's a great opportunity for us to continue to upgrade a good part of that install base, not only just with our desktop GPUs, but the RTX laptops are also a great driver of growth and upgrading folks to RTX.
Jensen Huang: Colette, do you want me to take the second one?
Colette Kress: Yes, please.
Jensen Huang: Aaron, a good -- great question on BlueField. First of all, the modern data center has to be rearchitected for several reasons. There are several fundamental reasons that makes it very, very clear that the architecture has to change. The first insight is cloud-native, which means that a data center is shared for everybody. you don't know who's coming and going and it's exposed to everybody on the internet. Number two, you have to assume that it's a zero trust environment because you don't know who's using it. It used to be that we have perimeter security, but those days are gone because it's cloud-native, it's remote access, it's multi tenant, it's public cloud, the infrastructure is used for internal and external applications. So number two has to be -- it has to be zero trust. The third reason is something that started a long time ago, which is software defined in every way, because you want -- you don't want a whole bunch of bespoke custom gear inside a data center, you want to avoid the data center with software. You want to be software defined. The software defined data center movement enabled this one pane of glass, a few IT managers orchestrating millions and millions of nodes of computers at one place. And the software runs what used to be storage, networking, security, virtualization and all of that -- all of those things have become a lot larger and a lot more intensive. And it's consuming a lot of the data center. In fact, the estimate depending on how you want to think about it, how much security you want to put on it, if you assume that it's a zero trust data center, probably half of the CPU cores inside the data center is running not applications. And that's kind of strange, because you created the data center to run services and applications, which is the only thing that makes money. The other half of the computing is completely soaked up running the software defined data center just to provide for those applications. And that you could imagine even accepting, if you like, as the cost of doing business. However, it commingles the infrastructure, the security plane and the application plane and exposes the data center to attackers. And so you fundamentally want to change the architecture as a result of that. To offload that software defined virtualization and the infrastructure operating system, if you will, and the security services to accelerate it because Moore's law has ended and moving software that was running on one CPU -- one set of CPUs, which is really, really good already to another set of CPUs is going to make it more effective, separating it doesn't make more effective. And so you want to offload that and take the -- take that application software and accelerated using accelerators, a form of accelerated computing. And so that's -- these things are fundamentally what BlueField is all about. And we created the processor that allows us to -- BlueField-2 replaces approximately 30 CPU cores. BlueField-3 replaces approximately 300 CPU cores, which just -- put it give you a sense of it. And BlueField-4, we're in the process of building already. And so, we've got a really aggressive pipeline to do this. Now, how big of this market, the way to think about that is every single networking chip in the world will be a smart network -- it will check. It will be a programmable accelerated infrastructure processor. And that's what the DPU is, it's a data center on a chip. And I believe every single server node will have it. It will replace today's mix with something like BlueField, and it will offload about half of the software processing that's consuming data centers today. But most importantly, it will enable this future world where every single packet, every single application is being monitored in real time all the time for intrusion. And so, how big is that application? How big is that market? Just, 25 million servers a year. That's the size of the market. And we know that servers are growing, and so those give you a feeling for that. And then in the future servers are going to move out to the Edge. And all of those Edge devices will have something like BlueField. And then how are we doing? We're doing PLCs now with just about every internet company. We're doing really exciting work there. We've included it in high performance computing, so that it's possible for supercomputers in the future to be cloud-native, to be zero trust, to be secured and still be a supercomputer. And then we expect next year to have meaningful, if not significant revenues contribution from BlueField, and this is going to be a really large growth market for us. You can tell, I'm excited about this. And I put a lot of my energy into it. The company is working really hard on it. And this is a form of accelerated computing that's going to really make a difference.
Operator: And your next question comes from Vivek Arya with Bank of America Securities.
Vivek Arya: Thanks for taking my question. Jensen, is NVIDIA able to ring fence this crypto impact in your CMP product? So even if, let's say crypto goes away, for whatever reason, the decline is a lot more predictable and manageable than what we saw in the 2018, '19 cycle. And then kind of part B of that is, how do you think about your core PC gamer demand? Because when we see these kind of 106% year-on-year growth rate, it brings questions of sustainability. So give us your perspectives on these two topics, just how does one ring fence kind of the crypto effect? And what do you think about the sustainability of your core PC gamer demand? Thank you.
Jensen Huang: Sure. Thanks a lot. First of all, it's hard to estimate exactly how much and where crypto mining is being done. However, we can only assume that the vast majority of it is contributed by professional miners, especially when the amount of mining increases tremendously like in-house . And so we created the CMP. And CMP and GeForce are not fungible. You could use GeForce for mining, but you can't use CMP for gaming. CMP is yields better and producing those doesn't take away from the supply of GeForce. And so it protects our GeForce supply for the gamers. And the question that you have is what happens when on the tail end of this? There's several things that we hope. And we learned a lot from the last time, but you never learn enough about this dynamic. What we hope is that that the CMPs will satisfy the miners at work will stay in mines, in the professional mines. And we're trying to produce a fair amount of them and we have secured a lot of demand for the CMPs and we will fulfill it. And what makes it different this time is several things. One, we're in the beginning of our RTX cycle, whereas Pascal was the last GTX. And now exactly was at the tail end of the GTX cycle, because the last GTX and it was the tail end of GTX cycle. We're at the very beginning of the RTX 30 cycle. And because we reinvented computer graphics, we reset the computer industry. And after 3 years, the entire graphics industry has followed. Every game developers need to do ray tracing, every content developer and every content tool has moved to ray tracing. And so if you lose ray tracing, these applications are so much better. And they simply run too slow on GTX's and so we're seeing a reset of the install base, if you will. And at a time when the gaming market is the largest ever, we've got this incredible install base of GeForce users. We've reinvented computer graphics and reset the install base and create an upgrade opportunity that's really exciting at a time when the market is the gaming market, the gaming industry is really large. And what's really exciting on top of that is that gaming is no longer just gaming. And it's infused into sports, e-sports. It's infused into art. It's infused into social. And so gaming is -- it has such a large cultural impact now, it's the largest form of entertainment. And I think that the experience we're going through is going to last a while. And so, one I hope that crypto will -- the CMP will steer our GeForce supply to gamers. We see strong demand and I expect to see strong demand for quite some time because of the dynamics that I described. And hopefully in the combination of those two, we'll see strong growth and through strong growth in our core gaming business through the year.
Operator: And your next question comes from John Pitzer with Credit Suisse.
John Pitzer: Yes. Good afternoon, guys. Thanks for let me ask the question. Jensen, I had two hopefully quick questions. First, I hearken back to the monitor you guys put out at couple of analyst days ago, the more you spend, the more you save. And you've always been very successful as you brought down the cost of doing something to really drive penetration growth. And so I'm curious with the NVIDIA Enterprise AI software stack, is there a sense that you can give us is how much that brings down the cost of deployment and AI inside the Enterprise? And do you think whether COVID lockdown related or cost related, there's pent up demand that this unlocks? And then my second question is just around government subsidies. A lot of talks out of Washington about subsidizing the chip industry, a lot of that goes towards building fabs domestically. But when I look at AI, I can't think of anything more important to maintain sort of leadership in relative to national security. How do we think about NVIDIA and kind of the impact that these government subsidies might have on either you or your customers or your business trends?
Jensen Huang: The more you buy, the more you , there's no question about that. And the reason for that is because we're in the business of accelerated computing, we don't accelerate every application. However, for the applications we do accelerate, the acceleration is so dramatic. And because we sell a component, the entire system, the TCO, the TCO of the entire system, and all the services and all the people and the infrastructure and the energy cost has been reduced by X factors, sometimes 10x, sometimes 15x, sometimes 5x. And so the -- so when we set our mind on accelerating a certain class of applications and recently we worked on true quantum so that we could help the quantum industry, quantum computing industry it's already there simulators so that they could discover new algorithms and invent future computers, even though it won't happen until 2030. For the next 20 years, that we're going to have 15 years, we're going to have some really, really great work that we can do, using NVIDIA GPUs to do quantum simulations. We recently did a lot of work in natural language understanding in computational biology so that we could decode biology and understand how biology is to infer to understand it and to predictively improve upon it and design new proteins. Those words are so vital. And that's what accelerated computing all about. Our Enterprise software, and I really appreciate the question. Our Enterprise software used to be just about the BGP, which is virtualizing GPU inside the VMware environment, or inside the Red Hat environment and makes it possible for multiple users to use one GPU, which is the nature of Enterprise virtualization, but now with NVIDIA AI, NVIDIA Omniverse, NVIDIA Fleet Command, whether you're doing collaboration or virtual simulations for robotics and digital twins, design your factory or you're doing data analytics, learning what the predictive features are that could create an AI model, predictive model that you can deploy out at the Edge using Fleet Command. We now have an intense suite of software that is consistent with today's enterprise service agreements. It's consistent with today's enterprise business models, and allows us to support customers directly, and provide them with the necessary service promises that they expect, because they're delivering -- they're trying to build a mission critical application on top. And, more importantly, by creating this -- prioritizing our software, we provide the ability for our large network of partners, OEM partners, value added resellers, system integrators, solution providers for this large network of hundreds of thousands of IT sales professionals that we are connected to through our network, we give them a product that they can take to market. And so the distribution channel, the sales channel, VMware, the sales channel of Cloudera, the sales channel of all of our partners and design, Autodesk, so on so forth, all of these sales channels and all of these partners are now partners and taking our stacks to market. And we have a fully integrated system that are open to the OEM, so that they could create systems of run the stack. And it's all certified, all tested, all benchmark and, of course, very importantly, all supported. And so this new way of taking our products to market, whereas our cloud business is going to continue to grow, and that part of AI is going to continue to grow that business is direct. We sell components directly to them, we support them directly. But there are 10 of those customers in the world. For Enterprises, there are thousands industries far and wide. And so I think this -- we now have a great stack and a great software stack that allows us to take it to the world's market so that everybody could buy more and save more.
Operator: And your final question comes from Stacy Rasgon with Bernstein.
Stacy Rasgon: Hi, guys. Thanks for taking my questions. Colette. So Colette, last quarter you had kind of suggested that Q1 would be the trough for, I guess, for gaming as well as the rest of the company beginning in particular, and it would grow sequentially through the year. I guess given the strength we're seeing in the first half, do you still believe that that is the case? And I kind of heard you guys, I think kind of dance around that point a little bit in response to one of the other questions. But could you clarify that? Is that still your belief that that core gaming business can grow sequentially through the rest of the year? And I guess same question is for data center, especially since sounds like hyperscale is now coming back, like after a few quarters of digestion and then all of the other tailwinds you've talked about. I mean, is there any reason to think that data center itself shouldn't also grow sequentially, like through the rest of the year?
Colette Kress: Yes, Stacy, thanks for the question. So I first of all start with when we talked about our Q1 results. And when we're looking at Q1, we were really discussing a lot about what we expected between Q4 and Q1. Given what we knew was still high demand for gaming. We believed we would continue to grow between Q4 and Q1, which often we don't. And we absolutely have the strength and overall demand to grow. What that then lead was, again, continued growth from Q1 to Q2 as we are working hard to provide more supply for the strong demand that we say. We have talked about that we have additional supply coming. We expect to continue to grow as we move into the second half of the year as well for gaming. Now, we only guide one quarter at a time, but our plan is to take the supply, serve the overall gamers, work on building out the channel, as we know the channel is quite lean. And so yes, we do and still expect growth in the second half of the year, particularly when we see the lineup of games, the holiday overall coming, the back-to-school, all very important cycles for us. And there's a great opportunity to upgrade this, RTX install base. Now, in terms of data center, will work in terms of our guidance here. We have growth from Q1 to Q2 planned in our overall guidance. And we do see as things continue to open up a time to accelerate in the second half of the year for data center. We have, again a great lineup of products here. It couldn't be a better lineup now that we've also added the inferencing products and the host of overall applications that are using our software that we have. So this could be an opportunity as well to see that continued growth. We will work in terms of serving the supply that we need for both of these markets. But yes, we can see definitely growth in the second half of the year.
Operator: There are no further questions at this time. The CEO, Jensen Huang, I'll turn the call back over to you.
Jensen Huang: Well, thank you. Thank you for joining us today. NVIDIA computing platform is accelerating. Launched at GTC, we are now ramping new platforms and initiatives. There are several that I mentioned. First, enabled by the fusion of NVIDIA RTX, NVIDIA AI and NVIDIA . We built Omniverse, a platform for virtual collaboration and virtual worlds to enable tens of millions of artists and designers to create together in their own metaverse. Second, we lay the foundation to be a three check data center scale computing company with GPUs, DPUs and CPUs. Third, AI is the most powerful technology force of our time. We partner with cloud and consumer internet companies to scale out and commercialize AI powered services. And we're democratizing AI for every enterprise and every industry. With NVIDIA AGX certified systems, the NVIDIA Enterprise AI Suite pre-train models for conversational AI, language understanding, recommender systems and our broad partnerships across the IT industry, we are removing the barriers for every enterprise to access state-of-the-art AI. Four, the work of NVIDIA Clara in using AI to revolutionize genomics and biology is deeply impactful for the health care industry, and I look forward to telling you a lot more about this in the future. And fifth, the electric self driving and software defined car is coming. With NVIDIA DRIVE, we are partnering with the global transportation industry to reinvent the car architecture, reinvent mobility, reinvent driving and reinvent the business model of the industry. Transportation is going to be one of the world's largest technology industries. From gaming, metaverses cloud computing, AI, robotics, self driving cars, genomics, computational biology, NVIDIA is doing important work and innovating in the fastest growing markets today. As you can see, on top of our computing platforms that span PC, HPC, Cloud, Enterprise to Autonomous Edge, we've also transformed our business model beyond chips. NVIDIA vGPU, NVIDIA AI Enterprise, NVIDIA Fleet Command and NVIDIA Omniverse adds enterprise software license and subscription to our business model. And NVIDIA GeForce Now and NVIDIA DRIVE with Mercedes Benz as the lead partner, our end-to-end services on top of that. I want to thank all of the NVIDIA employees and partners for the amazing work you're doing. We look forward to updating you on our progress next quarter. Thank you.
Operator: This concludes today's conference call. You may now disconnect.
