NVIDIA Corporation (NVDA) Earnings Call Transcript & Summary

Thanks, everybody, for joining us for a very exciting session that's coming up now. We are very fortunate to join Manuvir Das, who is the Vice President of Enterprise Computing at NVIDIA. NVIDIA is, of course, the largest -- single largest market cap company, doing some extremely exciting things, of course, through all of its businesses, but I think the most exciting thing, no one will argue with this, are happening with what they're doing within the data center where Manuvir is deep into it. So with that, I'm going to turn it over to Manuvir. He's got a short slide deck that he wants to talk about. And Manuvir, the floor is yours.

Thank you so much, Harsh, for having me and for giving NVIDIA this opportunity to talk to the audience. It's a real privilege. I thought what I'd do at the outset is just share with you the big-picture view of what NVIDIA is doing and where we're headed in the data center and with artificial intelligence before we do some Q&A here. So I'll start with a statement about what we're sharing in the slides, as we always do. So the first picture I have here is something we've shared before, when we announced a new software product from NVIDIA called NVIDIA AI Enterprise. And I thought I would start with this to just level set. This is the news we've shared prior and why we did this work, right? So if you think about the state of the union for artificial intelligence in the enterprise, for enterprise customers at large, we are at a stage today where we've had a lot of success with early adopters. There's a few thousand companies across the world that have had great success improving their business, improving the experience of their customers with automation intelligence, but the broad base of enterprise customers is yet to adopt the app, right? And what is the fundamental reason for this? The fundamental reason is that there are 2 very different sets of people within every enterprise company. On the one hand, you have the data scientists. These are the people who understand AI, who understand the tools, Jupiter notebooks, all these kinds of things. They do the development of new AI capabilities. And they move fast and they're pretty agile and they're state-of-the-art cutting edge, doing new things every night. On the other hand, you've got IT administrators who are accountable and responsible for making sure that the actual applications running in the enterprise data center are safe, secure, stable because the business of the company depends on it, right, and the experience of the customer depends on it. And these 2 personas, these 2 worlds, are pretty much apart because the one world of the data scientist wants to use the tools and frameworks that they are comfortable with, whereas the IT administrator is used to a different model for how they deploy applications. And there's a disconnect because IT does not know how to pick up what the data scientists produce. And the data scientists don't know how to operate in a world where IT lives. And so we created NVIDIA AI Enterprise to address this gap. And what we did is we took NVIDIA's AI software for training, for inference, for data science, and we made it work on top of VMware vSphere, which is sort of the de facto platform in the data center. If you look at any enterprise data center today, you will find virtualized servers running VMware vSphere. And so that's what this picture shows, right? And it achieves 2 things at the same time. On the one hand, for the data scientists, they see all the tools and frameworks that they are comfortable and experienced with to do their work. That's the layer in green provided by NVIDIA. On the other hand, for the IT administrator, it's the same VMware vSphere environment they're used to the same tooling, how do I provision, how do I give access to people, but now with these new workloads for AR. And so this is really a way of bringing these 2 worlds together. So this is what we've announced earlier this year in conjunction with VMware, which is NVIDIA AI Enterprise, really NVIDIA's way of becoming mainstream for enterprise customers, for making AI a mainstream workload for enterprise customers. Now this is just actually the beginning. And so what I really want to share with you today is that this is something that NVIDIA has been thinking about and working on for many years, right? And what we realized is this is mainstream artificial intelligence in enterprise data centers is a full stack problem. Of course, you need the right hardware. That is the layer I've shown you in green. But then you also need all of these pieces of software, sort of the operating system of AI, all the essential tools so that you can run your different AI workloads. And then finally, if you think about it, there's just different use cases, whether it's visual AI, detecting interesting things that are going on in video feeds or cybersecurity, finding attacks that are happening in your data center. And so you would love to have pieces of software that are customized frameworks for each of these use cases that are easy to adopt. And so I've drawn this abstract picture for you that is representative. You can think of it as a brickwall, right, that if you really want to solve the artificial intelligence problem end to end, you need to fully construct this brickwall of all these different boxes to get a complete solution. And then in NVIDIA, that is exactly what we've done. This is the same picture, but I've replaced every one of those abstract concepts with an NVIDIA product at the bottom, hardware products, but in the middle and the top, all software products that NVIDIA has produced over the last few years and especially over the last year to really complete this reform. This is not a vision slide. This is an execution slide. All of these things I'm showing you on this slide today already exists, are already usable by customers. The fact of the matter is that today, NVIDIA is much more a software company than a hardware company. We have thousands of software engineers within NVIDIA who work on this -- on all of these things every day. And so we built this entire stack, a set of frameworks for these different use cases, the essential software that allows all of this to run on mainstream servers, as I said, in conjunction with folks like VMware, Cloudera, et cetera, all of the hardware. And then what we announced recently was we have a partnership with Equinix to put all of this technology, the hardware and the software, into Equinix's data centers around the world so that for customers as they get going, it's very easy for them to start their journey where NVIDIA has predeployed all of these things for them. And then as they proceed in their journey, of course, they can procure and deploy these things for themselves in their own data centers or in a colocation facility. Before I come back to you, Harsh, the final point I wanted to make was that NVIDIA is a pretty fast-moving company, right? This is our general philosophy. And so I did this exercise for myself of if I were to show you this picture, the same picture from last year, but only show you the things that were in execution mode that were actually produced, what would this slide actually look like? And this is what it would look like. Whereas today, it looks like this, right? And so I just want to end by making this point that NVIDIA is an R&D-first, innovation-first company. The business results we have today are based on the work we've done in the last few years. And what our teams are working on every day today, all of these software stacks that we have been producing and are putting out, are to unlock the opportunity in the years ahead. And that's what we're really focused on as a company. So Harsh, that's what I had as a bit of an opening context-setting statement, if you will. Artificial intelligence in the enterprise data center is a full stack problem. It's an end-to-end problem. It requires a broad ecosystem. This is where NVIDIA is focused. We built the hardware. We built the software. We've created an ecosystem. We have more than 2.5 million developers who use different parts of our stack to develop their own applications and solutions. And that's our contribution to make AI feasible for enterprise customers. And with that, we have a go-to-market motion that is in conjunction with established partners, the OEMs who produce servers, folks like VMware who produce software stacks for the data center. And we're really looking forward to this journey of democratizing AI for enterprise customers over the next few years. And so with that, I'll stop sharing my slide deck and hand back to you, Harsh.

Manuvir, that is simply incredible to see the number of products you guys have introduced in just the last 12 months to be able to fill up the gap of where you were and where you're trying to go. And that brings us to an interesting topic. There's been a lot of changes here, not just with COVID but just generally. Data center is always morphing, always changing. Can you talk about how it's changing and what are the large changes that are happening in the industry that sort of you wake up and think about and say, this is the kind of direction that NVIDIA maybe needs to think about going into?

Yes. That's a great question, Harsh. And it's amazing how much the landscape of data centers has changed in the last decade. You'll hear some of these buzzwords these days like cloud, Kubernetes, containers, all these things. What's the common thread to all of that, right? The common thread to all of it is that for quite some time, computing in the data center was done in a scale-up manner. You take one server. You run your application on it. And as the applications get more demanding, you make your server bigger and bigger and more capable, right? And then you buy a few of these servers, and they're super expensive. And then what happened with the advent of the public cloud was the proliferation of a different model, which is scale-out rather than scale-up. Instead of having one giant server, let me have many small servers that cooperate to run a workload, right? This is what in computer science for decades has been referred to as distributed computing that the public cloud already did. And Kubernetes and containers are just a mechanism for building your application as a distributed computing workload, right? And this is how data centers have really evolved in the last decade. So what does this mean? This means now that when you run an application, instead of running on one server, you're running on a set of servers that are working in conjunction to run your workload. So when you think about computing now, you're not just thinking about building the best server. You have to think about the network because the data is flowing across all these servers. You have to think about security because if you -- if a malicious thing intercepts one server, they have access to all the other servers. You have to think about how you store your data so it's accessible to other servers, right? So computing is really evolving to data center scale. Every workload runs within a complete data center rather than a single server. And so because of that, you have to solve this as a full stack problem. You have to think about what's the right servers, what's the right networking gear, what's the right networking software so that it goes fast, what is the software stack for orchestrating the workload and running the workload? You have to put it all together, right? And this is how NVIDIA has really evolved, that we've become a full stack company for that reason. Now the other thing I would say, Harsh, as you know, our genesis at NVIDIA was as a hardware company, right, with the GPU. So the other insight that we had in NVIDIA was that in order to make this full stack go, you're going to need 3 essential components in every server. Of course, you need a CPU, which is where applications have traditionally run. You need a GPU, which is the way of accelerating the workload so you can do more in every server. And then you need this new form factor that we call a DPU, a data processing unit, which sits on the network interface and really runs not the workload but the infrastructure of the data center itself, okay? So every server needs a CPU, a GPU and a DPU in conjunction. This is our vision of the data center. And this is why we, of course, have GPUs. We have the BlueField DPU from NVIDIA. We also recently announced that we are working on a CPU optimized for artificial intelligence called the Grace CPU based on ARM technology. And we really see this as the future direction of the data center, where every server will have a CPU, a GPU, and a DPU inside, right? So just to summarize all that, I would say, because I know I said a lot there, Harsh, we really think that computing going forward in the data center becomes a data center scale problem, a full stack problem. We believe every server needs to have a CPU, GPU and a DPU inside of it as the essential hardware components, and then you need the right layers of software that I showed on my slides to bring it all together within the data center.

Amazing, Manuvir. It seems like the opportunity is getting bigger and bigger as the data center compute sort of gets distributed and flattens out, if you will. So you guys, I'm sure, talk to a lot of customers. And I'm sure the highest-end customers actually come to you with their problem and say, "This is kind of what we need solved." What are you seeing in terms of what's actually strategically important to the customers? And what areas are these customers emphasizing versus deemphasizing, particularly as a result of, for example, COVID-19 that we're caught up in right now?

Right. I think -- and you mentioned the pandemic, and that's had 2 profound impacts, Harsh, that we've typically seen from talking to customers, right? And they are 2 sides of the same coin, which is mainly that the amount of in-person connection has gone down dramatically, right? One side of the coin is for the companies doing their own work and their own business across the employees, et cetera, the employees are not able to sit in a room together anymore, right? So the question is, how can a company remain as productive as before even though their employees are all in different places and working for home, right? That's one consideration. The second consideration is the company's engagement with their customer base has also not changed because of the pandemic, right? It's become much more online and digital even more so than before. And so with that change in how they're interacting with customers, what should they do, right? So let me just take a minute to break down each of these, right? So if I take the first one, which is that employees are not all sitting in the same room together. Instead, our approach at NVIDIA with our customers has been, instead of looking at this as a loss, this is actually a forcing function for a new opportunity for companies that there are actually -- technology can allow companies to be far more productive by leveraging people all over the world rather than just leveraging people in a room. And this is why we created a platform called NVIDIA Omniverse, which we now make available to enterprise customers. And the way to think about NVIDIA Omniverse is it is a digital, real-time remote collaboration environment for people working on the same project. It could be engineers designing a building together. It could be designers creating the facade of a display somewhere together. And with Omniverse, all of these people can essentially log into the same place. They can collaborate in real time. One person makes a change, another person can see the change, right? So it creates a whole new model for collaboration and working together, right? And this is why we put so much emphasis on Omniverse. It's a big, big initiative for NVIDIA. And of course, there's a bit of a bias here because for such a model to work well, one of the core technologies you need is really good graphics. And that's something NVIDIA knows a thing or 2 about, right? So it was a very natural pathway. But it's also a distributed computing problem. It's also scale. It's a data center scale problem because you're running this giant sort of thing that different people can connect to and work on, right? So that's one change, Harsh. And so that is within the company's world. That's why we did Omniverse. And of course, there have been technologies for remote work like VDI that NVIDIA has been working on for quite some time with RGB technology, and we continue to do that, right? And we see a lot of adoption, for example, of workstations now because if you think about it, if you're an employee working from home, right, you need a proper workstation in your home if you're going to do all your work from home, right? And it changes the dynamic there a little bit, right? So that's the one side. The other side is the company's engagement with their customer base, which is now much more digital and online than it was even 2 years ago, right? And so you see -- look at how we are doing this conference right now, right? We're on a video conferencing technology. These have proliferated, right? But you see things like the need to converse with your customers, what is called conversational AI. So many more customer conversations, you don't have enough humans in your company to do all these conversations. So you need some automation, you need AI, you need a chatbot that can interact with your customers on your website, right, so you can handle more requests and more queries. So on that side of the coin, we've seen a lot more companies now interested in adopting AI because they see it as a way to greatly enhance their communication with their customer base in this new era where their customer is relatively disconnected from them physically, right? So those are the 2 things I would point out.

And what about any -- I mentioned that maybe something that's sort of important versus something that's become less important to the customers. Can you talk about, if you have an example, I would appreciate it, of something that the customers are not as focused on today as they were maybe before?

Yes. I know you'd love an answer to that one, Harsh, but I'm going to pass on that because for better or worse, I happen to be in a position where when I talk to customers, it's mostly about the things they want to do now, right? And so that's been a focus, yes.

That's fair. That's fair. Let's talk about -- off of that first question as well, big companies that want complex things done come to NVIDIA. And I suspect that you're probably more of a partner today and increasingly in the future than you were before because they're actually coming to you saying, we need this XYZ and help us with that. And you're sort of involved earlier on. A, is that -- am I correct in thinking about it correctly? And then -- and is it really happening? And are you seeing enhanced interaction with your customers on a daily basis with the requirements that they want fulfilled?

I think it's a great observation, Harsh, that you have. It is true. I will put it to you this way, right? AI is actually -- AI is hard for any customer to implement. That's the truth of it, right? And we sort of went through this phase where we were just proving it out. The technology was complex, and we were working with a small number of customers who really need it. Yes? Like, for example, I'm an online shopping site, and they need to recommend to a customer what they should buy next. And I know that AI will help me. And as painful or difficult it might be, I'll just jump in into it. And so those are the people we worked with earlier, right? But that has now evolved. And as we've sort of broadened our reach across the enterprise customer, they are not looking for point pieces of technology that deploy the software or put in this piece of hardware. They want a solution, right? They want to solve a business problem. And so more and more, we find our conversations to be of that nature that, "Hey, this is my use case. This is the problem I'm trying to solve. Tell me, what is the recipe? What hardware do I need? What software do I need? What ISV application builder do I need to work with? What data sets do I need to acquire in order to do my training?" It's a complete discussion. We believe in this so much, Harsh, that if you look inside NVIDIA, we have a very large organization, a dedicated organization of what we call solution architects. And these are people -- they're not sellers. They're not sales reps. They're not product engineers. They sit in the middle. And what they do is every customer conversation begins with, what's the problem we're trying to solve. Here's our SLAs. They will sit down with you as almost consultants, right, and as partners, and we'll design the solution with you. And as we design a solution with you, maybe you use our technology. Maybe you won't use our technology. That's fine. Either way, if you adopt AI, as NVIDIA, we're super excited about that, right? And we do think we have good pieces of technology. Even for folks like myself, Harsh, like when we go and have conversations at the executive level with the customer, right, I never have a conversation as a vendor. I don't have a conversation about here's a product I want to sell you, right? My conversation is, "What's the problem you're trying to solve? How have you architected things so far? How do we think you might want to architect your infrastructure or data center to go solve this problem? And if we align on that, maybe we can be of help to you with some parts of that architecture," right? That's kind of how we do it.

It's an amazing way to think about customer interaction because the customer in this situation is more than likely to feel you're there to help them with their issues than just when you're trying to sell a product like -- you put it best.

Yes. Harsh, if you don't mind, I might get in trouble for saying this, but my boss, Jensen, who's the CEO of NVIDIA, I would say the one word he uses the most in meetings with folks like myself and NVIDIA is empathy. That's sort of the most important word in this dictionary, and that is it starts with that, have some empathy for the customer, right, understand the situation they're in, what problem they're trying to solve, what opportunity they're trying to take advantage of, and then make it happen.

And you know what, I bet it allows NVIDIA to connect at a completely different level versus the rest of the vendors. Let's move on to software. You mentioned software earlier on. So NVIDIA, I've noticed more over the last 3 years, is bringing increasingly more and more amount of software to the marketplace, specifically when it relates to AI, which is obviously a core competency of NVIDIA. Can you talk about NVIDIA's AI software? And what is the differentiating factor here? Where are we in the adoption curve? And like if I dream the dream -- it's a long question, but if I dream the dream, what is the opportunity for NVIDIA here?

Yes. So I'll do this in reverse order with a punchline, Harsh. I think we all believe that if we execute well on our plans, there is at least a multibillion-dollar incremental software opportunity here on top of what we are already doing, right? Because today, our revenue in enterprise AI is primarily based on the hardware that we provide, the GPUs and the networking gear, et cetera. But if you think about it, a simple way to think about it is if you look inside an enterprise data center, there are certain layers of software, for example, VMware or SAP, et cetera, that are deployed across servers, and there's a commercial model for that software. And the reason is because that software solves a very important problem for the customer, which is how do I run my workloads. And the software is almost more important than the hardware because the software is what the customer is experiencing. And the customer has an expectation that the software is supported. It has a certain level of quality and performance. It is updated regularly, those sorts of things, right? And that's why there's a commercial model for the software. And we are now entering that world for the first time with NVIDIA, right? To date, we have produced software that has been made available to the community, to other people to make their lives easier. But now for the first time with NVIDIA AI Enterprise, we really have a similar kind of product that can be sold because that -- the environment a customer can rely on, right? And there's a simple math you can do about how many servers there are in the world, how many servers we expect would be used for AI, what's our licensing you could do for the software in every server that will be fair to the customer and you multiply those things out. And it's at least multiple billions of dollars of incremental revenue for that layer of the software, right? So that -- so I'd start with that. Now to your first question was sort of where are we in the business, right? The truth of the matter is we are in early days, right? We have -- this year is when we have rolled out the software. In fact, NVIDIA AI Enterprise went to general availability just last month, right? And it just beginning to roll out now. There's a new version of VMware that supports that, which has been rolling out. So we're in the beginning of this journey, but we certainly expect that there will be broad adoption. And you can think of that adoption on 2 fronts, Harsh. One is the software itself being adopted. But the other thing is what the software is really doing is it's making it possible that you can take your regular mainstream servers you have in your data center today that you would normally not think of using for AI, but now you can use them for AI. So it also -- so it expands the balloon in 2 different ways. One way is there's this new thing called the software. That is a commercial proposition. But the second is that the software brings a lot more servers into the picture to be used for AI. And so it expands the balloon and it expands the reach, right? So that's kind of what I would say. We see it as a big opportunity. We're early in the adoption curve. We're in the steep part of the S curve, if you will. And then just one thing I might say about the piece parts themselves. The simplest way I would describe this is, when you adopt AI, you need to do 2 things. Number one, with your data scientists and other people, you need to develop the AI. and then once you've developed it, you've got these great models, then you need to deploy NVIDIA within the application so that you can actually use the AI, for example, to see what's going on in your retail store, okay? And so essentially, we've produced 2 platforms. We have something called NVIDIA Base Command, which is what an enterprise uses to develop their AI. And we've got a platform called NVIDIA Fleet Command, which you use to then deploy your AI out to all the places where you need to deploy it out, right? So that's the highest level, simplest way of thinking about our platform. There's Base Command and there's Fleet Command. And we are very excited about these. But as I said, we are very much in the early stages.

Why don't we -- just one more thing on that. Do you think there's anybody in the space that's even close to the level of work you guys are doing? I know historically, NVIDIA has been just a pioneer in AI on the hardware side, and now I see this focus on the software side. Is there anyone even in the ZIP Code of where NVIDIA operates in bringing the complete package together?

Yes. We -- of course, I am biased here, but I would say, we do not face them, right? And -- but I'll elaborate on that, right? If you think about the picture I showed in my slide, the point we made was this is really a full stack problem, from the piece parts of the hardware to the systems, to the lower level of software, to the frameworks on top. We're the only company on the planet that has been working on all of these layers. And as I said, my slide was not a vision slide. My slide was a reality slide of the things we've built. Now we operate at all these different levels. And we are a big believer in the ecosystem, whether it's cloud service providers or server manufacturers or whatever, right? So our model is we are happy to partner with anybody at any level. For example, you might be a company that focuses on building frameworks, the top level. But then we have APIs so you can use the middleware of our software as the basis for developing your frameworks. You might be a system manufacturer like a Dell or an HPE. You can incorporate our GPUs and our DPUs into your servers, right? So there are certainly companies at every layer. In fact, we foster that ecosystem very intentionally. But we believe we're really the only company in the planet, Harsh, that has focused on the entire stack, right? And that's why we need to really optimize it and take the rein for these use cases.

Absolutely. No question about it. You guys have been there at the forefront with compute and with AI for a very long time already. You brought up something, Manuvir, earlier on that was fascinating, Omniverse. How does Omniverse fit into your software strategy? You talked in terms of collaboration. But obviously, there's got to be a long-term game plan, I would think, if NVIDIA is putting so much effort into it. What is the opportunity for adoption for this in the next couple of years? And so then maybe I'll hit you with that first and then go from there.

Yes. And I'll do this one backwards toward the punchline. First things first, Harsh. Our math, basically, when we look at the target audience for Omniverse and the work that we've done, we think there is about 20 million designers and engineers out there for whom Omniverse will be a great platform for them to do their day-to-day work. And if you just do some simple math of a subscription-based model that we've already put out and that fixed the norms and standards of the industry, if you will, this is, again, definitely a multibillion-dollar net incremental market opportunity from the use of Omniverse, right? Now -- so that's one way to answer the question. The other way of answering is, as you pointed out, I talked about collaboration, and that's certainly a use case of remote collaboration. But do we see a bigger opportunity, right? The bigger opportunity we see, Harsh, is that one way actually of tying together everything that NVIDIA has done from its inception as a company, whether it's graphics or AI or robotics or self-driving cars or any of these, is that fundamentally, we are a simulation company, okay? We build technologies in different domains that allow you to simulate something without actually having to do it. That's the core of our technology. Like, for example, think about our platform for self-driving cars. Yes, you can drive cars around and you can capture what's happening on the roads and make your cars better. Of course, we do that. But we also have a complete simulation platform that you can use to do miles and miles of driving "without actually driving," right? So you can learn a lot more. So we really believe that going forward, no matter what industry you're in, as the world evolves, simulation will become more and more routine as the basis for how you are productive. And really what Omniverse is, is that has dramatically changed the state of the art in terms of being a platform for simulation -- for real-time simulation so you can actually model things and see what's happening, right? And we think that is a massive opportunity that goes beyond just the real-time collaboration.

Thank you for that. In the most recent earnings call, I think Jensen focused a lot on software for one reason or the other. And then in your presentation, you're talking a lot about software. So we see the change happening. My question is about when do you think in the future -- how far out are we before you start generating a meaningful amount of opportunity in revenues from the software stack that NVIDIA is bringing to the table?

Yes. I think I'll answer that for you. I'll apologize and answer that for you in a relatively generic way, Harsh, instead of putting specific numbers, right? This is definitely a journey that we are at the beginning of. We are on the steep part of the curve. We are seeing massive interest, so we know we're heading in the right direction. But certainly, right now, our revenue is primarily driven by the things we have been working on over many years, right? And these things will begin to pay off as we go forward. But as I said, what I quoted to you for both NVIDIA enterprise as well as for Omniverse enterprise as being multibillion-dollar opportunities, we see these as very real opportunities, right? I would also -- yes, I would also say, Harsh, that I also want to paint the picture accurately for the audience, right? There is, in fact, a next level of software opportunity for NVIDIA that is, in some ways, more powerful than what I described, right? So what I'm talking about here is sort of the essential software for artificial intelligence or for collaboration and simulation in Omniverse. But if you think of the real AI journey, what is the real AI journey about? It's about saying that in every walk of life, no matter what industry your company is in, there are certain functions that humans are performing, right? And those -- each of those functions one by one can -- if we can figure out a way to automate that function with AI, then you can do it much more cost effectively and you can free up your humans to focus on other things. A great example is that you can use NVIDIA's software frameworks to look at X-rays and detect whether some -- whether there's a fracture in a person's bone, right? That's something that today a radiologist has to do. But you can take that function and you can automate it, right? In the space of retail, you can look at the camera feeds from across the store and determine who's shopping for what and what are they walking out of the store with, right? Instead of having humans in the back room, having to sit there and look at the videos with weary eyes all the time, right? So one by one, you can take each of these human functions and replace them with some NVIDIA software. So now the question you ask is, what is the potential business value of this software? The business value of the software is not a function of how much did it cost NVIDIA in terms of engineers to develop the software. The business value is in terms of how valuable is it to that enterprise customer to replace that human function or augment that human function with this automated software, right? And so we see a rich landscape of business opportunity from the software there that we are yet to unlock, right? And that's a whole other domain of opportunities.

So I wanted to shift gears a little bit, Manuvir. We're at the last kind of 7, 8 minutes, and I wanted to hit upon this. So of late, since maybe the acquisition of Mellanox, we hear NVIDIA talk a lot about SmartNICs and DPUs. And I guess connectivity is a core theme now. You touched upon it with the distributed compute methodology. Can you update us on how you feel, A, about the importance of things like SmartNICs and DPUs? Maybe what's the difference between the 2? And then where you are in the road map as a company on these 2 particular connectivity products?

Yes. Yes, let me do that, Harsh. So firstly, starting with -- let's just disambiguate these things, SmartNICs and DPUs, because there's a number of people, a number of companies out there that work on SmartNICs, right? So I think the best way to think about it is SmartNICs is sort of step one, which is to say, I've got a network interface part. The data is flowing through there. If I put a little bit of computing power, maybe some ARM CPU cores over there, there's some more processing I can do on the data as it's flowing through the network. Now we took this to the next level and created this concept of the DPU. Our DPU product family is called BlueField. And the idea of the DPU is it has so much horsepower in that processor that what it actually does is it takes over the functions of the data center itself. So we've heard a lot in the last decade about software-defined data centers. What does that really mean? What that means is that all these things you will bring in your data center firewalls and things for which you have this dedicated hardware, we're now turning to software that was running on the server itself. But as this happen, more and more of this load run with the server, which means that there was less and less places for the applications themselves to actually run. So whereas you would have needed 5 servers to run an application, you now need 10 because of the servers being consumed on this stuff. And what our DPU really does is it says, offload all of that work on to this other processor. Move it there. You free up the CPU and the main server to run your workload. And the way we built the DPU, it actually accelerates. It's like the GPU. If you take the firewall software and you move it from the CPU to the DPU, it's not just shifting the problem. It runs 100x faster. And so you need much less silicon and the deeper you do the job than you would have on the CPU, right? So it actually saves money in the data center, right? So this is why we're so high on the DPU because it can dramatically change the way data centers are architected. So our view is every server needs a DPU. Now 2 specific things we have done here, Harsh, that we think distinguish NVIDIA. The first thing is we learned a great lesson from when we did GPUs. We created a software SDK interface called CUDA, which was a simple way for developers to interact with GPU. We said, no matter what GPU you use, CUDA is CUDA, right? So it makes your work portable. We've done the same thing here with DPUs. We've created an SDK called DOCA, and it's a consistent SDK across our DPU family. And so again, what we say to the ecosystem is program to this API, this SDK, and your work will translate as we make better and better DPUs, and your software will just become better. And the proof point of this -- the second point I want to make is we have a road map. We are already working on BlueField-3, the third generation. We've already announced the architecture of BlueField-4, right? And it's not just making the processor better, but we now are working on versions of that processor where we've actually got GPU capabilities inside the DPU as well, right? So you can do AI now inside the network, right? So think about what that enables, right? So that's how I would summarize it, Harsh. On the one hand, we have a rich hardware road map for how much more powerful DPUs are becoming. What we've created is an interface called DOCA that rides along. So for the ecosystem, you just develop once. And as the processor gets better, your software will just get better along the way.

It's -- Manuvir, it's amazing. You described it so well. I think maybe 15, 17 years ago, maybe even 20 years ago, when the first NICs were coming out, I was trying to understand what they did. And the point was it takes away some of the complex functionality off of the CPU and does it for the CPU. And it seems like the same thing is happening, except the functions are getting more complex. Now they're software-richer, but the basic functionality is the same, but we're moving up the stack, which is great for companies like you and actually makes a data center simpler in some ways because like I said, it's more cost-effective. I mean -- and so anyways, fantastic stuff. So a lot to think about there, a lot to unpack. Manuvir, as always, pleasure to have you. Thank you so much for your time. Thank you, anybody that joined in and listened to this presentation, and we really appreciate your time. Thank you, Manuvir.

Thank you, Harsh. It was my pleasure. And on behalf of Jensen and the entire team at NVIDIA, really appreciate the opportunity to be with you today.

Thank you so much. Take care.