NVIDIA Corporation (NVDA) Earnings Call Transcript & Summary

We're the largest networking company in the world today. I'm expecting us to be the largest storage processor company in the world. That I think is likely, very likely to happen. And it's very likely that we will ship more high-end CPUs than just by anybody else either. And the reason for that is because if you look at Vera and Grace and Vera, they go into the SmartNIC of every single node, okay? We are now the SmartNIC of AI factories. There's other -- a lot of CSPs have their own SmartNICs like Nitro, and they'll continue to have their. But outside, BlueField is incredibly successful. And BlueField-4 is going to knock it out of the park. And so the early -- not the early adoption, but the adoption of BlueField-4. And the software layer on top is called DOCA, rhyming with CUDA. And so DOCA is adopted all over the place now. And so for networking, east-west traffic, high-performance networking, we're the largest. For network isolation, north-south networking, I'm fairly certain we're going to be one of the largest. For storage, that is a completely unserved market today. The way that storage works is SQL. SQL is structured data. Structured database is lightweight. AI database KV caches insanely heavy weight. You're not going to hang that off of your north-south network. I mean that's just a horrible waste of network traffic. You want to put it right into the computing fabric, which is the reason why we introduced this new tier. This is a market that never existed. And this market will likely be the largest storage market in the world, basically holding the working memory of the world's AIs. And that storage is going to be gigantic, and it needs to be super high performance. And so I'm so happy that the amount of inference that people do has now eclipsed the computing capability that the world's infrastructure has. And so we now -- the amount of context memory, the amount of token memory that we process, KV cache we process is now just way too high. You're not going to keep it -- keep up with the old storage system. And so when an inflection point in the market happens and you had the vision to see it happen, and then this is a brand-new market because of this inflection. This is the best way to go into a market. And BlueField-4, there's nothing like it, absolutely nothing like it.

Will Stein from Truist Securities.

Someday, nobody is even going to ask me a question about GPUs. I'm going to -- but don't worry, I'm going to have to throw it out there.

My question is about the ramping velocity of Vera Rubin relative to the 2 prior generations. And in particular, when you -- as you said, you're in full production today with Vera Rubin. As that starts to get rev rec in the second half, we certainly expect Blackwell will still be going as well. Will Hopper also be still present? Will you have 3 architectures at once? And regardless of that question, maybe you can talk about the margin impact because it seems like your ramp for Vera Rubin should be much faster given the discussion of the time to manufacture you addressed today in the Q&A or in the presentation.

Yes, I appreciate that. Vera Rubin's ramp should be fast. The challenge for Vera Rubin, it is the only computer in history where literally every single chip is new. I don't think you could buy a phone where every single chip was new. Even the high-temperature capacitors were new, never existed. Even the HBM4 never existed. LPDDR5 SOCAM never existed. Are you guys following me? I'm talking about the stuff. I'm not even talking about my chips yet. Literally, every single chip in that computer was brand new. The fact that we made it work at all is a miracle. The fact that we made it work perfectly is just incredible. And so that there were -- that was where the risk of Vera Rubin was because there were so many new technologies coming together. You got co-packaged optics coming in on a switch, the largest switch ever made. You've got all these different things, all these technology. And so we had the wisdom of breaking down the problem, and we were working on Vera Rubin for several years, piecing together the technology, derisking the important parts of the technology and derisking important parts of the supply chain for several years now. Vera Rubin is not a 1-year project. Vera Rubin probably something close to 5 years. And so we take the most difficult parts, and we derisk every part of it. And sometimes we'll even mix it in with some other technology, and we've actually already shown it. It just wasn't a differentiating part. It wasn't the part that mattered. And we built these pieces, notice, we've been building BlueField-4 for some time. But I derisk the CPU, we need it to be -- because you have so many -- so much storage, the memory -- the performance per watt, the energy efficiency of the CPU has to be insanely great. You're not going to get -- you're not going to put up that rack of BlueFields and put your favorite x86 in there. That's just not going to happen. It's either not fast enough or is it going to draw too much power. And so we had to build a CPU that was -- that had the data rate, the perf per watt was as low as Grace. And then we built -- because the SerDes, we couldn't build it that fast several years ago. We had to wait until the SerDes came along. And when everything came together, that's when your BlueField-4 was realized, okay? So now we're on this rhythm. BlueField-5 will be easy to do. BlueField-6 will be easy to do. But that's derisking all of the technology components of Vera Rubin was just a massive undertaking. And so now we've derisked -- now everything has proven to work. Everything is in volume production. I'm just so incredibly proud of the team. I mean when you just look at all the chips, not just our chips, but other people's chips that have to work with our chips. It's a miracle. But the problem is if you don't do that, then we literally had 1.5x more transistors and call it 1.7x more transistors. Who cares? Are you guys following me? You're not going to rack up a whole new AI factory for $50 billion for 1.5x. You're just not going to do it. The bar is too high now. Just you got to imagine that person. It's not about, oh, 50% better camera, sure, I'll pay for that. It's 50% better AI factory. You're not going to pay for that. Does it make sense? It's $50 billion. You're not going to do it for 50%. You do it for 10x. You'll write a check for 10x, but you're not going to write a check for 50%, which is a challenge because as all of you have heard me say, Moore's Law is over. Moore's Law is completely over. If every single year, we're getting 50%, that -- and AI is going -- we're not getting 50%. But every single year, we're getting probably something like 20 -- 15% to 25% tops out of transistors these days. But AI is going 10x per year and token rates going 5x per year, there's no way to keep up. And so we have to do something like extreme codesign and really basically revolutionize everything.

Srini Pajjuri from RBC Capital Markets.

We're going to ramp Vera Rubin pretty fast. This is easier to ramp than this. This is literally what all of the world's computer companies are building right now. Just enormous workforces, enormous factory floors, building this tray. This compute tray is the limiter of the world's AI factories today. This is what everybody is building. Look at the number of connectors, and so we realized the labor content that went into this. And then once you get here, literally, this was 2 hours. You could just watch them. It takes 2 hours. Assembly of people's like operating room standing around this thing, building it like a car. And now you're happy to build this like a car because, of course, the economics is better than a car, right? This is a full car, okay? And so you're happy to build it like a car, but you'd rather build this, and the reliability is going to be better -- it's called RAS, reliability, availability and serviceability. The one thing I didn't say today, I should have remember, so NVLink, this generation, Vera Rubin, you could -- the world's first networking system, it's hot swappable. You take part of the network, you pull it out and you could service the rest of it while it's still running, update the software while it's running. It's craziness. So the goal is just to keep that entire AI factory running all the time. Does that make sense? You just paid $50 billion for it. You don't want to -- the concept of downtime, you would go insane. And so just the amount of technology and innovation we -- from all the learnings of working with everybody that went into Vera Rubin, all the things that I didn't say, it's just -- it's a miracle. And so we're going to try to ramp it as hard as we can. Second half, we should sell lots, ship lots.

Jensen, up here. I guess my question is more about the longer term. How do you see the frontier model market shaking out? If you look at the history of tech over the last 20, 25 years, there's always been 1 or 2 winners. We still have a lot of frontier models and a fairly fragmented market. At least from my usage point, they all seem pretty similar to me. So I'm just curious as to hear your thoughts about how do you see the market shaking out? Do you think AI is so different that we need so many frontier models going forward? Or if there's going to be a shakeout, what do you think will cause that?

Yes. Good question. As soon as -- like, for example, if everybody I work with is smarter than I am -- they're all smart enough. Okay. Are you guys following me? If all the AIs are smarter than we are, then they're good enough. And so that's what you're feeling. You're feeling the good enough for general use. But for domain-specific use, they're hardly good enough. And that's where I think you're going to see a fair amount of breakthroughs this year in vertical agentic systems because it's -- I think it's not easy to just boil the ocean and make every AI be great at, on the one hand, chemistry and the other hand, biology and also drive a car and do it effectively and efficiently. I don't think you could reasonably do that. I have a fairly good understanding of the technology. I don't think you're going to reasonably do that. The technology is related, but optimizing for all these different domains is quite different. And the specialization of the workflow, the flywheel, the data, the training, even the people to evaluate it, it's very vertical. And so I think long before I'm worried about the foundation model industry, I think verticalization is clearly going to happen. Now are the verticalization going to happen outside the foundation model companies or inside the foundation models? That's a different question. However, in many of the companies that you're thinking about, remember, they already have verticals. Meta already has very deep verticals in digital marketing and right, in AI ad serving. And so they have a lot of verticals that they're very, very good at. And it's not likely that they're going to -- they need to or it's not likely they have -- they will give up that model creation to somebody else because that vertical is too important to them and they own the channel, they own all the expertise. You could say the same with Google and you could say the same with X, you could say, does that make sense? I mean there are several different companies where their go-to-market is so good because they're already domain experts in those verticals. I think that, that flywheel is going to continue to sustain. In the case of OpenAI, they are the Google of our time. And I use all of the AI models, but I kind of, for some reason, always go back to ChatGPT. And I kind of always go back to Google. And so it's -- I think they've become that. And so that's a great outlet for them, the consumer outlet. In the case of Anthropic, their enterprise capability is really, really good. And so maybe that's their angle. I think that ultimately, people -- each one of these model makers have to find an outlet, a channel that they very, very significantly secure. And otherwise, for everybody else, it's going to be open models. I just mentioned 5 of them that actually are fairly secure. But for everybody else, I think open models is likely to be the answer. And then they're at the frontier. They're not the frontier, but they're at the frontier. Maybe they're months behind. And we'll keep everybody there. And companies like Lilly and companies like ServiceNow, and -- they could take it and create their own version of AI, the ServiceNow AI, the Snowflake AI and then just -- but they build it off of open models. My sense is that, that's likely the outcome for the future.

Jensen, over here, Louis Miscioscia, Daiwa Capital Markets. So congratulations on the Mercedes launch after 8 years and pointing to autonomous vehicles driving finally hitting now. Can you give us some thoughts about where the industry is for agentic and physical? You talked about it last year, you talked about it this year. And there is a lot of examples, but do you think we're going to see critical mass volume in 2026 from these areas, mainly from an inference standpoint and deployments?

If not for Agentic AI, there would be no Cursor. Cursor is agentic. If not for Agentic AI, there's no OpenEvidence. It's an agentic system. Almost all of the best AI native companies that you know are Agentic AI companies. But we call them Agentic AI today because we're trying to say that it's different than one shot or models that don't use tools. In the future, that's just called an AI application. There's no question in my mind now, this framework of AI systems is likely going to be the basic framework for building applications in the future. And so instead of off-the-shelf libraries, you're going to have off-the-shelf models and off-the-shelf agentic systems. And they're just going to -- you're going to plug them together, you're going to tell them what your goals are and they're going to try to work together. And so getting applications deployed and working together is going to be just easier and easier and easier. And so the technology is hard, but using it should be easier. Today, the technology is hard and using it is hard. And that's the reason for that is because the technology is not good enough. But as you see that in the last 2 years, the AI technologies have gotten so good that using ChatGPT for research and solving a lot of questions has become so much easier. And this is going to be the same with AI applications, and they're all going to become agentic. Alpamayo is an agentic AV. It's agentic in the sense that it looks at the world and it goes, I've never seen the circumstance before. What's going on? It breaks the problem down into things that are fairly routine. And it goes, I know that. I know that. I can -- based on that, this is what I would recommend not do. And so it's a reasoning system. And it's using -- it's using common sense, physical common sense that we taught it. And if you ask it to take you something -- it's a full -- it's called a VLA, vision language action model. It's a full robotic system. So you could just tell the car to take you somewhere. If the car is about to do something and you're not sure -- why are you doing that? The car will just talk back to you. And so it's a fully agentic system.

Joe Moore from Morgan Stanley. I wonder if you could talk about how to think about sizing the physical AI market. It seems intuitive that this is a very difficult problem to solve because of simulation, there's a lot of dollars that needs to be spent. But when you did this with large language models, you had cloud CapEx budgets that just sort of shifted towards you in the early stages. For Physical AI, do we need to see companies raising money? Is it going to be automotive and industrial companies? Just how do we think about what's going to fund what seems like a pretty big project ahead of us?

Physical AI has the benefit of riding on the shoulders of the large language model. And it's called multimodality, number one. It's just -- it's able to understand vision and language at the same time. It's multimodality, and it's aligned meaning that -- let me see if I can give you an example. So in your brain, and I'm pretty certain of it, this is true. C-A-T, those 3 words, Meow and the picture of a cat. To you, it's exactly aligned in the same place in your brain. It's aligned. Over the years, we've aligned -- it's multimodality alignment. And so you have to use this concept called cross-attention and you're learning 2 things at one time. And then your vector space, it becomes same, okay, or in the same geography. And so we have the benefit of large language models that were already trained. And so we take essentially Nemotron and then we take something that is trained very specifically on vision and world models, which uses a lot less data. In combination, we create Cosmos. So Cosmos still took tens of thousands of GPUs several years, but it's less. But now that we have Cosmos, and we give Cosmos away to everybody. That's the point. We're trying to lower the bar. You still have to do your own fine-tuning. You still have to do your own domain adaptation. And so we created Cosmos so that we can lower the bar for everybody to have Physical AI. If the bar for physical AI is such that we have to do what we're doing here, replicate it 3 other times, one for digital biology, one for physics, obviously, it's unnecessary and it will take longer. And so we -- in a lot of ways, we burdened not all of it, but probably 1/3 of it for the world. And now they can take this and run with it.

Jensen, thanks a lot for doing this. Colette, thanks a lot for doing this. Ananda Baruah with Loop Capital. A neocloud question for you. I would love to get your view on how big picture, the neocloud to the GPU clouds fit into the space structurally. NVIDIA has continued to deepen its partnerships with the neoclouds. At this point, the core customer bases from the AI labs to the hyperscalers, sovereign and now even enterprise are moving closer to embracing the neoclouds. So I would love to get your take on the role you see them playing big picture. And then just as a quick part B, to the extent that enterprise and sovereign adopt neoclouds, does that help NVIDIA sell their Enterprise OS?

We believed in the existence of this category because the technology is still changing fairly fast. When we started cultivating what people call neoclouds or at the time, they were just called GPU clouds because that's all they had in their clouds. We started cultivating them because -- and partnering with them because we realized that the AI technology was moving fairly quickly, and we knew that our technology was going to move fairly quickly. So the market was moving quickly. The technology is moving quickly. And the access to land, power and shell was also not something you could take for granted. And so in this combination of circumstances, we felt that a community of fast-moving, agile regional players would likely be quite successful. We were right. And so Enscale is a European region. You have Yotta in India. They're a regional player. And of course, there's CoreWeave that we know about, right? And Lambda that people know about. And so we have all these different regional players, Humain in the Middle East. And so there's a lot of different regions. My sense is that you're going to find more. And each one of these geographies are going to build up their AI infrastructure. And that AI infrastructure and community is going to have to include everything from researchers and start-ups and isn't that right, and large companies. And oftentimes, being where your customer is makes a difference. And so that's the reason why we did it. And now it's given us a network of outlets to the marketplace and partners who are constantly seeking out land, power and shell opportunities, speaking back to the power challenges, but customers and partners who are trying to build this ecosystem with us. And so we're quite informed as a result of all that.

It's Chris Caso from Wolfe Research. The question is about margins and really sustainability of margins at these high levels. It's one of the most frequent questions we get from investors. I know you've answered the question for this year that they stay in the mid-70s. But 2 questions on that. One is, what's different with the Rubin ramp that allows you to maintain those margins in the early stages of ramp because typically, your margins will come down in the early stages. And then longer term, how does NVIDIA continue to price to value in the face of competition? How do you maintain these high margins?

I think that -- so for the ramp questions, it's just whether it's in the plus or minus 0.5%, that's kind of obviously hard to predict, nor I think is that your question. In the long term, our margins are exactly directly related to the value that we deliver. And I simplified the world of value creation down to basically 3 charts. And those 3 charts are insanely hard to get done right. The first one is how many GPUs does it take for you to train a reasonable sized model in the time that you need to train it. And so you've got to get to market every year, and you need to iterate on that model several times. And you like to win the front -- you like not to lose the frontier. And so your training capability matters a lot. And that's not just a flops thing, because the shape of that curve, the shape of that curve that I showed you is directly related to co-design. It's got networking problems. It's got memory bandwidth problems. It's got NVLink problems, it's got software problems. It's got every problem. The shape of that curve, otherwise, it would just be, have you noticed, I'm the only CEO that shows you guys shape of curves instead of just a bar. Do you guys know the bar? You just pick a point. It's like that's not life. Life is not like a bar. And so life is -- and I can't show you the multidimensional curves that we have, and we call them frontier Pareto, Pareto frontiers. And we sweep across a multidimensional space. And that's the simplest version that we -- and it's the one that makes sense most to deliver value. And so there's the training side of it, there's the inference side of it, and then there's throughput side of it. The inference side of it has to do with your -- the cost it takes to generate the tokens, given the latency, the quality of service requirements of that time. People are expecting more and more tokens generated, which means that the token rate has to be much shorter and yet you need to deliver it much more cost effectively. That multidimensional problem was described in that chart. And then the next one is in the final analysis, if you're going to build your infrastructure this year, you better be darn sure that your revenues will go up next year. And that has -- and you've got only 3 gigawatts or 2 gigawatts or 1 gigawatt. That's it. So within your 1 gigawatt, your perf per dollar, your perf per watt, that last chart that I showed, it was the second or third. That chart is insanely hard. That's not a bar because the workload differs across all these different scenarios. And so your job is to, one, support your research team to get to the frontier. Two, once you have a model, you better make money at it because otherwise, it has to generate tokens fast enough for a good quality of service. And then three, the entire data center has to generate enough revenues that the CapEx you put down next year is going to cause your company to grow, not shrink. And so these are such complicated issues, and I distilled it into one chart, but there's mounts of simulators behind it. And to the extent that we can continue to do that, at the levels that we're doing that, it's easy to go 1.5x. Actually, I take that back. That's not even that easy, okay? If you build a chip that's 1.5x more transistors, you put it into that supercomputer, you don't get 1.5x, you'll get 1.2x because of Amdahl's law. And so you have to do extreme engineering, extreme codesign across all of this and invent things like NVFP4 to bust out of Moore's Law. Are you guys following me? Otherwise, you're like everybody else. And so that's the challenge. I think our ability to sustain value, which is deliver the value across the dimensions that I've said, is fairly -- I wouldn't say it's singular, but I would say it's fairly close to singular. And we're doing it at the scale, and it's getting harder every time. As you know, it's getting -- obviously, it gets harder every time. And so I think -- so that's -- those are the dimensions. You take the performance. In the end, it's about that 1 gigawatt data center. That's the simple math. It's $50 billion. Does your $50 billion deliver more revenues than somebody else? Do you deliver more tokens than somebody else? Okay. It's $50 billion. If somebody else is willing to do it for $40 billion because $20 billion of it is just land, power and shell, isn't that right? Chips are free, still $20 billion. If everything was free, it was still $20 billion. Are you guys following me? Okay? And so you better be sure that whatever you put on top of that $20 billion, you're going to be happy with. And so that's why I think the difference between good margins and poor margins. It's such a small difference across that $50 billion. So long as we continue to deliver the throughput, I hope that customers will continue to reward us for that. But that's our basic strategy. And then today, I spoke about the new dimensions of that data center, which is we really want to invest in making sure that the resilience, availability, reliability, availability and serviceability of the data center is world-class. And that's very, very hard as well.

So Jensen, following up on the previous question on the AI models. What do you think might be the game changer for the current competitive landscape? Because since now, it seems we are in an oligopoly where 3 to 5 big players dominate the market in different verticals. But any game changer in the next several years? And what would that be? And my second question is, seeing the AI token is growing 5x year-over-year. And when would that to reaccelerate or slow down? And what would be the catalyst for the change? And my final question that what's the next milestone for both AI and NVIDIA that you are looking for that you are super excited about?

If I told you the last one, you're not going to come to the next one. Some of the big breakthroughs that are still right around the corner, how to deal with memory is a big deal. And today, context lengths could be 100,000, it could be 1 million. But clearly, the right context length is infinite. And so how do you deal with infinite context length where you have to do attention across all of that. So there's some good research that needs to be done there. We're playing with a lot of research in this area ourselves. That's why our model is hybrid. State space models, the SSMs are highly efficient at compressing context and -- versus transformers. And so we've created a hybrid version. And so Nemotron 3 was very efficient at context processing and token generation. I think there's a lot of research in this direction but still -- I think the -- some of it is related to the system architecture, as I mentioned today, BlueField-4 to bring memory closer. It's kind of like your long-term memory is not -- doesn't sit in your head. It sits on the network somewhere. We just took your long-term memory and we put it in your brain, okay? We put BlueField-4 in the same rack. And so we're going to put your long-term memory in your brain. I think it makes a lot of sense. There's a lot of continuous learning type of things, which is you come up to a circumstance you don't know before, you reason about it and you still don't want to solve it. Maybe it's a domain. You just don't even have first principal knowledge of it to even break it down. And so you might know that you don't know it, to know that you don't know something and to go study up, go do research so that you have some first principal knowledge of something and then you come back and you reason about it. So it's kind of almost like the AI first, you prompted, and it doesn't know the answer at all. But it's okay, behind your back, it just went and when it did some research and I learned it. Now I can reason about your question, doesn't make sense. And so continuous learning doesn't have to -- I think that there's a fair amount of research opportunity there. And your second question, what's going to be the inflection -- the next inflection of token rate generation? The 5x came about because of reasoning. I have a feeling that we have a 50x in front of us. And the 50x in front of us is because of agentic systems. The reasoning is going to come with it, tool use and planning and simulation. It's almost like AlphaGo in real time. And so I think that's probably around the corner. These agentic systems is likely to cause token rates to go way up. The demand for computing is really, really high right now, as you guys know. Because of the 3 scaling laws, reasoning and agentic systems is putting enormous amounts of pressure, multimodality, larger models is putting an enormous amount of pressure on the training systems. On the inference systems because of long thinking, putting enormous amount of pressure. And then all of a sudden, agentic systems comes along, putting enormous amount of pressure on the -- and then because these models are now so useful, the number of AI start-ups is going through the roof. If you look at the number of AI start-ups from 2025 versus the year before, it almost doubled. And the amount of money they raised, most of it is going to go towards compute, right, so that you can be an AI company. And so the amount of burden on -- the pressure on the computing companies is really high. That's why our customer demand is so high.

Jensen, Ruben Roy from Stifel. We've talked about Extreme co-design quite a bit. And the stats are staggering, right? The Spectrum-X run rate, getting to 400 gig SerDes. What does that say about R&D intensity going forward? And are you seeing tangible benefits today from some of the things you're doing with either the EDA companies or Cursor? Is that impacting your R&D? And then I guess the last part of that question is just your M&A philosophy and just thinking through some of these recent deals, Grok and Enfabrica and how you're thinking about M&A relative to getting and accelerating the design process?

Our -- if you look at all of our investments, you could see us investing at almost every layer of the stack. From land, power, shell, chips, infrastructure, models and applications. We either invest in it directly, meaning we're building it like, for example, NVIDIA's open models. We're one of the largest model builders in the world. We just don't ever talk about it. And now we're getting so much attention in this area, positive attention in this area. You'll hear us talking about it more. And it also explains why NVIDIA's cloud spend is so high because we're building the world's leading models for open science and all these open markets. And so -- but that creates such an incredible flywheel for our architecture. It's one of the best investments we have. And so you see us invest across that entire stack that way. You then see us invest across the industry. Like, for example, we invest from digital biology to agentic systems, all the way to robotics and autonomous vehicles. And so we'll invest across the industries. And then we'll invest across the dimension up and down the supply chain. So when you see us invest, and I conflate R&D budget with invest because to me, it's the same. It's improper that most people split the 2. It actually -- investment is investment. What difference does it make? You're investing internally or externally to enhance your company's market position. Isn't that right? That's our fundamental goal. That's what all of you would like me to do. And so I see this large investment portfolio in this larger universe in that simple way. Where we could do something very uniquely, we'll likely do it internally and bulk it up. Like, for example, NVLink is clearly revolutionary, and we're singularly the only company in the world with it today. The first version of it is likely going to be scale out Ethernet called scale up Ethernet, and it will work out the same way. And -- but here, it's such an incredible capability and the rhythm of our company is so fast that it made sense for us to bring like-minded people to our company, and this is where Enfabrica came along. Rochan and the team were always thinking about scale up, and they just described it in different ways. And their market traction was difficult because it's hard to build a company that does scale up. The compute and the fabric are highly integrated. The software stack is completely integrated. And so it's hard to disentangle them into different companies. Some of the things that I'm thinking about with Grok, it's hard to be disentangled. There's a lot of innovation and invention that has to happen at the 2 teams level. And so there, again, I think the teams out has just joined us in that particular case. But to the extent that we can invest outside, I prefer that. I prefer to keep NVIDIA as small as possible as large as necessary. And if you look at our company today, we're what 40,000 people. We're probably one of the leanest fighting machines on the planet. And yet our ecosystem is really sprawling. And that's the size -- that's the mental model of a company of the future. And so I think the level of R&D spend of our company is quite sustainable.

Natalia Winkler, UBS. So one question I want to follow up on Grok. I was wondering if there's any software technology from that deal that might be -- you could leverage across the NVIDIA portfolio broadly?

Yes, no doubt because the programming model of the Grok chips are very different than the programming models of our chips, which is the reason why they're extremely low latency, and we're extremely high throughput. Some of the things that I'm thinking about doing would rely on the reshaping of some of that stuff. But we're still -- we're just really at the beginning part of it. We have plenty of time to go do this. And if we succeed in doing it, it'd just be yet another dimension of capability we can bring to the world's AI factories. I think the vast majority would still just be things like Grace Blackwell and Vera Rubin just be, call it, 90%. But maybe in the future, 10% of it is an extreme version of something. It's kind of like it's okay to build a mid-engine and an SUV. It's okay. It's still, both Ferraris, right? And so I think what NVIDIA does is not build GPUs. What NVIDIA does is build AI infrastructure. Are you guys following me? I barely showed you a GPU today. I stood in front of a pod. And so our goal is to build AI infrastructure that is insanely amazing, not just for 80% of the world, but -- or 87%, but hopefully for 100% of the world. And even use cases you didn't even know about yet.

Okay. I believe you have time for one last question.

Ken from Robocap. So it's a question about both margin and technology. So actually, you currently already have CPX technology and through the acquisition of Grok, you also can have the access to SRAM that can be used in inferencing. So actually -- so actually, your team also published a paper so a month ago, so about how to use CPX in GPU and so as to reduce the usage of HBM because you can use GDDR7 instead of HBM. And we all know that HBM is very expensive. So going forward, so because of the combination of Grok and your in-house CPX technology, so how would you see your usage of HBM? Can it be more under control so as that could be more positive to your margin going forward?

Sure. So I can describe -- I'll describe the benefits of each one of these things, and then I'll describe the challenge, okay, why it's not so obvious. For example, CPX does prefill per dollar better than like, for example, Rubin CPX versus just Rubin, it does prefill for dollar better. So it's prefilled -- it's perf per dollar is higher than Vera Rubin normally. And if I keep everything on SRAM, then, of course, I don't need HBM memory. And -- but the problem is then the size of my model that I can keep inside these SRAMs is like 100x smaller. okay? However, for some workloads, it could be insanely fast because SRAM is a lot faster than going off to even HBM memories, okay? And so I think you can kind of see the benefits in prefill and decode. The problem is workloads are changing shape all the time. And sometimes you have MOE, sometimes you have multimodality stuff, sometimes you got diffusion models, diffusion models, right? And so sometimes you have auto regressive models, sometimes you have SSMs. These models are all slightly different in shape and size. And sometimes they move the pressure on the NVLink, sometimes they move the pressure on HBM memory. Sometimes they move the pressure on all 3. And so my point is because the workloads are changing so fast and because the world is innovating so fast, that's one of the reasons why NVIDIA is just universally the right answer because we're flexible. Does that make sense? If your workload is changing from morning to night and depending on what customer you have, we're equally -- we're versatile. We're good at almost everything. And you might be able to take one particular workload and push it to the extreme. But that 10% of the workload or even 5% of the workload, 12% of the workload, if it's not being used, then all of a sudden, that part of the data center could have been used for something else for 90% of the workload and you deprived it because you only have 1 gigawatt. The trick is to think through that one data center, not as infinite money and infinite space, but you have finite power. And so you have to utilize that finite power for the overall consumption of the data center. And the more flexible it is, the better it is. The more unified architecture it is, like, for example, if we updated a new DeepSeek model and every single GPU in the data center, all of a sudden, every one of their performance go up. I uploaded the -- updated the library for our Qwen model and the whole data center go up. I do it for -- does that make sense? But if you have 17 different architectures, one is good for this thing, one is good for that thing, then as it turns out, the overall TCO is not as good. So that's the challenge. And even when I'm building these things, I know what the challenge is. It's very hard. I'm exploring ways to beat myself all the time, and it's hard. We're exploring all kinds of different chips that try to build a better solution than even the one we have. It is extremely hard. But the exercise is worth it, isn't that right? If I'm constantly trying to come up with a new way to even do better than what I currently have, if I'm doing that myself, then I'm exploring all of the nooks and crannies. I'm trying to disrupt myself all the time, okay? So I think that at the moment, CPX is exactly as you say, but it also reduces the flexibility of the data center. And I think with that, let's see, what should I tell you guys? One, demand is really high. If I didn't say that earlier, demand is really high. Two, Grace Blackwell GB300, the transition has been wonderful, and everybody is building it out. Vera Rubin, next generation is here, another giant step up. It couldn't have been possible without co-design, took us all these different chips to make it possible. Second half of this year, we'll start shipping. And next year will be the Vera Rubin year, and we'll be shipping at scale. In the meantime, I think that you should know that the AI community is doing really, really good work. ChatGPT was followed on by o1, which is completely revolutionary, which triggered reasoning models of all kinds. Open models is now the second largest model in the world. That's probably the best way to think about it. And our company is -- has been doing this for some time, but we are a giant model builder as well. And we build models and do software that we share with the entire community. And I think this year, Agentic AI and Physical AI are really going to hit strides. And so have a great year. I wish I had voice for you, but I'll go -- I don't know where I left it, but I will find it. All right, guys. Happy New Year.