NVIDIA Corporation (NVDA) Earnings Call Transcript & Summary

Welcome to the stage NVIDIA Founder and CEO, Jen-Hsun Huang.

Welcome to GTC. What an amazing year. We wanted to do this at NVIDIA. So through the magic of artificial intelligence, we're going to bring you to NVIDIA's headquarters. I think I'm bringing you to NVIDIA's headquarters. What do you think? This is where we work. This is where we work. What an amazing year it was and we have a lot of incredible things to talk about. And I just want you to know that I'm up here without a net. There are no scripts. There's no teleprompter. And I've got a lot of things to cover, so let's get started. First of all, I want to thank all of the sponsors, all the amazing people who are part of this conference. Just about every single industry is represented. Health care is here, transportation, retail, gosh, the computer industry, everybody in the computer industry is here. And so it's really, really terrific to see all of you, and thank you for sponsoring it. GTC started with GeForce. It all started with GeForce. And today, I have here a GeForce 5090 and 5090 unbelievably, 25 years later, 25 years after we started working on GeForce, GeForce is sold out all over the world. This is the 5090, the Blackwell generation and comparing it to the 4090, look how it's 30% smaller in volume. It's 30% better at dissipating energy and incredible performance, hard to even compare. And the reason for that is because of artificial intelligence. GeForce brought CUDA to the world, CUDA enabled and AI has now come back to revolutionize computer graphics. What you're looking at is real-time computer graphics, 100% path traced. For every pixel that's rendered, artificial intelligence predicts the other 15. Think about this for a second. For every pixel that we mathematically rendered, artificial intelligence inferred the other 15. And it has to do so with so much precision that the image looks right and it's temporarily accurate, meaning that from frame to frame to frame going forward or backwards because it's computer graphics, it has to stay temporarily stable. Incredible. Artificial intelligence has made extraordinary progress. It has only been 10 years. Now we've been talking about AI for a little longer than that. But AI really came into the world's consciousness about a decade ago, started with perception AI, computer vision, speech recognition, then generative AI. The last 5 years, we've largely focused on generative AI, teaching an AI how to translate from one modality to another modality, text to image, image to text, text to video, amino acids to proteins, properties to chemicals, all kinds of different ways that we can use AI to generate content. Generative AI fundamentally changed how computing is done, from a retrieval computing model, we now have a generative computing model, whereas almost everything that we did in the past was about creating content in advance, storing multiple versions of it and fetching whatever version we think is appropriate at the moment of use. Now AI understands the context, understands what we're asking, understands the meaning of our request and generates what it knows. If it needs, it will retrieve information, augments its understanding and generate answer for us. Rather than retrieving data, it now generates answers, fundamentally changed how computing is done. Every single layer of computing has been transformed. The last several years, the last couple 2, 3 years, major breakthrough happened, fundamental advance in artificial intelligence. We call it agentic AI. Agentic AI basically means that you have an AI that has agency. It can perceive and understand the context of the circumstance. It can reason, very importantly, can reason about how to answer or how to solve a problem and it can plan an action. It can plan and take action. It can use tools because it now understands multimodality information that can go to a website and look at the format of the website, words and videos, maybe even play a video, learns from -- what it learns from that website, understands it and come back and use that information, use that newfound knowledge to do its job. Agentic AI. At the foundation of agentic AI, of course, something that's very new, reasoning. And then, of course, the next wave is already happening. We're going to talk a lot about that today, robotics, which has been enabled by physical AI. AI that understands the physical world. It understands things like friction and inertia, cause and effect, object permanence, when someone [indiscernible] doesn't mean has disappeared from this universe, it's still there, just not seeable. And so that ability to understand the physical world, the 3-dimensional world, is what's going to enable a new era of AI, we call it physical AI, and it's going to enable robotics. Each one of these phases, each one of these waves opens up new market opportunities for all of us. It brings more and new partners to GTC. As a result, GTC is now jam-packed. The only way to hold more people at GTC is we're going to have to grow San Jose. And we're working on it. We've got a lot of land to work with. We've got to grow San Jose. So that we can make GTC -- I've just -- just as I'm standing here, I wish all of you could see what I see. And we're in the middle of a stadium. And last year was the first year back that we did this live. And it was like a rock concert and it was described, GTC as -- was described as the Woodstock of AI. And this year, it's described as the Super Bowl of AI. The only difference is, everybody wins at this Super Bowl, everybody is a winner. And so every single year, more people come because AI is able to solve more interesting problems for more industries and more companies. And this year, we're going to talk a lot about agentic AI and physical AI. At its core, what enables each wave and each phase of AI, 3 fundamental matters are involved. The first is how do you solve the data problem. And the reason why that's important is because AI is a data-driven computer science approach. It needs data to learn from. It needs digital experience to learn from, to gain -- to learn knowledge and to gain digital experience. How do you solve the data problem? The second is how do you solve the training problem without human in the loop? The reason why human in the loop is fundamentally challenging is because we only have so much time and we would like an AI to be able to learn at super human rates, at super real-time rates and to be able to learn at a scale that no humans can keep up with. And so the second question is, how do you train the model? And the third is how do you scale? How do you create -- how do you find an algorithm, whereby the more resource you provide, whatever the resource is, the smarter the AI becomes. The scaling law -- well, this last year, this is where almost the entire world got it wrong. The computation requirement, the scaling law of AI is more resilient and in fact, hyper accelerated. The amount of computation we need at this point, as a result of agentic AI, as a result of reasoning is easily 100x more than we thought we needed this time last year. And let's reason about why that's true. The first part is let's just go from what the AI can do. Let me work backwards. Agentic AI, as I mentioned, at its foundation is reasoning. We now have AIs that can reason, which is fundamentally about breaking a problem down step by step, maybe it approaches a problem in a few different ways and selects the best answer. Maybe it solves the problems -- the same problem in a variety of ways and ensure it has the best -- the same answer, consistency checking or maybe after it's done deriving the answer, it plugs it back into the equation, maybe a quadratic equation to confirm that, in fact, it's the right answer, instead of just one shot blurbing it out. Remember, 2 years ago, when we started working with ChatGPT, a miracle as it was, many complicated questions and many simple questions, it simply can't get it right and it's understandably so. It took a one shot whatever it learned by studying pretrained data, whatever it saw from other experiences, pretrained data, it does a one shot, blurbs it out, like a [indiscernible]. Now we have AIs that can reason step by step by step, using a technology called Chain of Thought, best of end, consistency checking, a variety of different path planning, a variety of different techniques. We now have AIs that can reason, break a problem down, reason step by step by step. Well, you can imagine, as a result, the number of tokens we generate and the fundamental technology of AI is still the same, generate the next token, predict the next token. It's just that the next token now makes up step one. Then the next token after that, after it generates step one, that step one has gone into the input of the AI again as it generates step 2 and step 3 and step 4. So instead of just generating 1 token or 1 word after next, it generates a sequence of words that represents a step of reasoning. The amount of tokens that's generated as a result is substantially higher and I'll show you in a second, easily 100x more. Now 100x more, what does that mean? Well, it could generate 100x more tokens and you can see that happening, as I explained previously, or the model is more complex, it generates 10x more tokens. And in order for us to keep the model responsive, interactive so that we don't lose our patients waiting for it to think, we now have to compute 10x faster. And so 10x tokens, 10x faster, the amount of computation we have to do is 100x more easily. And so you're going to see this in the rest of the presentation, the amount of computation we have to do for inference is dramatically higher than it used to be. Well, the question then becomes how do we teach an AI how to do what I just described? How to execute this Chain of Thought? Well, one method is you have to teach the AI how to reason. And as I mentioned earlier, in training, there are 2 fundamental problems we have to solve. Where does the data come from? Where does the data come from? And how do we not have it be limited by human in the loop? There's only so much data and so much human demonstration we can perform. And so this is the big breakthrough in the last couple of years, reinforcement learning, verifiable results. Basically, reinforcement learning of an AI as it attacks or tries to engage, solving a problem step-by-step. Well, we have many problems that have been solved in the history of humanity, where we know the answer. We know the equation of a quadratic equation, how to solve that. We know how to solve a pythagorean theorem. The rules of a right triangle. We know many, many rules of math and geometry and logic and science. We have puzzle games that we could give it, constrained type of problems like Sudoku. Those kind of problems on and on and on, we have hundreds of these problem spaces where we can generate millions of different examples and give the AI hundreds of chances to solve it step by step by step as we use reinforcement learning to reward it as it does a better and better job. So as a result, you take hundreds of different topics, millions of different examples, hundreds of different tries, each one of the tries generating tens of thousands of tokens. You put that all together, we're talking about trillions and trillions of tokens in order to train that model. And now with reinforcement learning, we have the ability to generate an enormous amount of tokens. Synthetic data generation, basically using a robotic approach to teach an AI. The combination of these 2 things has put an enormous challenge of computing in front of the industry. And you can see that the industry is responding. This is -- what I'm about to show you is Hopper shipments of the top 4 CSPs -- the top 4 CSPs, they're the ones with the public clouds, Amazon, Azure, GCP and OCI. The top 4 CSPs, not the AI companies, that's not included, not all the startups, not included, not enterprise, not included, a whole bunch of things not included, just those 4. Just to give you a sense of comparing the peak year of Hopper and the first year of Blackwell, okay, the peak year of Hopper and the first year of Blackwell. So you can kind of see that, in fact, AI is going through an inflection point. It has become more useful because it's smarter, it can reason. It is more used. You can tell it's more used because whenever you go to ChatGPT these days, it seems like you have to wait longer and longer and longer, which is a good thing. It says a lot of people are using it with great effect. And the amount of computation necessary to train those models and to inference those models has grown tremendously. So in just 1 year and Blackwell has just started shipping, in just 1 year, you could see the incredible growth in AI infrastructure. Well, that's being reflected in computing across the board. We're now seeing -- and this is -- the purple is the forecast of analysts about the next the increase of capital expense of the world's data centers, including CSPs and enterprise and so on, the world's data enters through the end of the decade, so 2030. I've said before that I expect data center build-out to reach $1 trillion. And I am fairly certain we're going to reach that very soon. Two dynamics is happening at the same time. The first dynamic is that the vast majority of that growth is likely to be accelerated, meaning we've known for some time that general-purpose computing has run out of course -- run its course and that we need a new computing approach. And the world is going through a platform shift from hand-coded software running on general-purpose computers to machine learning software, running on accelerators and GPUs. This way of doing computation is at this point, past this tipping point and we are now seeing the inflection point happening, the inflection happening in the world's data center build-outs. So the first thing is a transition in the way we do computing. Second, is an increase in recognition that the future of software requires capital investment. Now this is a very big idea. Whereas in the past, we wrote the software and we ran it on computers, in the future, the computer is going to generate the tokens for the software. And so the computer has become a generator of tokens, not a retrieval of files from retrieval based computing to generative based computing from the old way of doing data centers to a new way of building these infrastructure and I call them AI factories. They're AI factories because it has 1 job and 1 job only, generating these incredible tokens that we then reconstitute into music, into words, into videos, into research, into chemicals and proteins, we reconstitute it into all kinds of information of different types. So the world is going through a transition in not just the amount of data centers that will be built but also how it's built. Well, everything in the data center will be accelerated, not all of it's AI. And I want to say a few words about this. This slide is genuinely my favorite. And the reason for that is because for all of you who have been coming to GTC all of these years, you've been listening to me talk about these libraries this whole time. This is, in fact, what GTC is all about. This one slide. And in fact, a long time ago, 20 years ago, this is the only slide we had, one library after another library after another library. You can't just accelerate software. Just as we needed an AI framework in order to create AIs and we accelerate the AI frameworks, you need frameworks for physics and biology and multiphysics and all kinds of different quantum physics, you need all kinds of libraries and frameworks. We call them CUDA-X libraries, acceleration frameworks for each one of these fields of science. And so this first one is incredible. This is cuPyNumeric. NumPy is the #1 most downloaded Python library, most used Python library in the world, downloaded 400 million times since last year. cuLitho is -- and cuPyNumeric is a 0 change drop in acceleration for NumPy. So if any of you are using NumPy out there, give cuPyNumeric a try. You're going to love it. A cuLitho, a computational lithography library, over the course of 4 years, we've now taken the entire process of processing lithography, computational lithography, which is the second factory in a fab. There's the factory that manufactures the wafers and then there's the factory that manufactures the information to manufacture the wafers. Every industry, every company that has factories will have 2 factories in the future, the factory for what they build and the factory for the mathematics, the factory for the AI, factory for cars, factory for AIs for the cars, factory for smart speakers and factories for AI for the smart speakers. And so cuLitho is our computational lithography, TSMC, Samsung, ASML, our partner, Synopsys, Mentor, incredible support all over. I think that this is now at its tipping point. In another 5 years' time, every mask, every single lithography will be processed on NVIDIA CUDA. Aerial is our library for 5G turning a GPU into a 5G radio. Why not? Signal processing is something we do incredibly well. Once we do that, we can layer on top of it, AI, AI for RAN or what we call AI RAN. The next generation of radio networks will be -- will have AI deeply inserted into it. Why is it that we're limited by the limits of information theory? Because there's only so much information spectrum we can get, not if we add AI to it. Co-opt numerical or mathematical optimization, almost every single industry uses this when you plan seats and flights, inventory and customers, workers and plants, drivers and riders, so on and so forth, where we have multiple constraints, multiple constraints, a whole bunch of variables and you're optimizing for time, profit, quality of service, usage of resource, whatever happens to be. NVIDIA uses it for our supply chain management. Co-opt is an incredible library. It takes what would take hours and hours and it turns it into seconds. The reason why that's a big deal is so that we can now explore much larger space. We announced that we are going to open source Co-opt. The almost -- everybody is using either Gurobi or IBM's CPLEX or FICO. We're working with all 3 of them. The industry is so excited. We're about to accelerate the living daylights out of that industry. Parabricks for gene sequencing and gene analysis. MONAI is the world's leading medical imaging library. Earth-2, multiphysics for predicting and very high-resolution local weather, cuQuantum and CUDA-Q. We're going to have our first Quantum Day here at GTC. We're working with just about everybody in the ecosystem, either helping them research on quantum architectures, quantum algorithms or in building a classical accelerated quantum heterogeneous architecture. And so really exciting work there. cuEquivariance and cuTENSOR for tensor contraction, quantum chemistry. Of course, this stack is world-famous. People think that there's one piece of software called CUDA. But in fact, on top of CUDA is a whole bunch of libraries that's integrated into all different parts of the ecosystem and software and infrastructure in order to make AI possible. I've got a new one here to announce today. cuDSS, our sparse solvers, really important for CAE. This is one of the biggest things that has happened in the last year. Working with Cadence and Synopsys and ANSYS and Dassault and -- well, all of the systems companies, we've now made possible just about every important EDA and CAE library to be accelerated. What's amazing is until recently, NVIDIA has been using general-purpose computers running software super slowly to design accelerated computers for everybody else. And the reason for that is because we never had that software, that body of software optimized for CUDA until recently. And so now our entire industry is going to get supercharged as we move to accelerated computing. cuDF, a data frame for structured data. We now have a drop in acceleration for Spark and drop in acceleration for pandas, incredible. And then we have, Warp, a library for physics that runs in a python library for physics for CUDA. We have a big announcement there. I will save it in just a second. This is just a sampling of the libraries that make possible accelerated computing. It's not just CUDA. We're so proud of CUDA. But if not for CUDA and the fact that we have such a large installed base, none of these libraries would be useful for any of the developers who use them. For all the developers that use them, you use it because, one, it's going to give you incredible speed up. It's going to give you incredible scale up. And two, because the installed base of CUDA is now everywhere. It's in every cloud. It's in every data center. It's available from every computer company in the world. It's every -- it's literally everywhere. And therefore, by using one of these libraries, your software, your amazing software can reach everyone. And so we've now reached the tipping point of accelerated computing. CUDA has made it possible. And all of you -- this is what GTC is about, the ecosystem, all of you made this possible. And so we made a little short video for you. Thank you. [Presentation]

I love what we do. I love even more what you do with it. And one of the things that most touch me, in my 33 years doing this, one scientist said to me, Jen-Hsun because of the work -- because of your work, I can do my life's work in my lifetime. And boy, if that doesn't touch you, well, you got to be a corpse. So this is all about you guys. Thank you. All right. So we're going to talk about AI. But you know AI started in the cloud. It started in the cloud for a good reason because it turns out that AI needs infrastructure, it's machine learning. If the science says machine learning, then you need a machine to do the science. And so machine learning requires infrastructure and the cloud data centers have the infrastructure. They also have extraordinary computer science, extraordinary research, the perfect circumstance for AI to take off in the cloud and the CSPs. But that's not where AI is limited to. AI will go everywhere. And we're going to talk about AI in a lot of different ways. And the cloud service providers, of course, they like our leading-edge technology. They like the fact that we have full stack because accelerated computing, as you know, as I was explaining earlier, is not about the chip. It's not even just a chip in the library, the programming model is the chip, the programming model and a whole bunch of software that goes on top of it. That entire stack is incredibly complex. Each one of those layers, each one of those libraries, it's essentially like SQL. SQL as you know, is called in-storage computing. It was the big revolution of computation by IBM. SQL is 1 library, just imagine. I just showed you a whole bunch of them. And in the case of AI, there's a whole bunch more. So the stack is complicated. They also love the fact that -- CSPs love that NVIDIA CUDA developers are CSP customers because in the final analysis, they're building infrastructure for the world to use. And so the rich developer ecosystem is really valued and really, really deeply appreciated. Well, now that we're going to take AI out to the rest of the world, the rest of the world has different system configurations, operating environment differences, domain-specific library differences, usage differences. And so AI as it translates to enterprise IT, as it translates to manufacturing, as it translates to robotics or self-driving cars or even companies that are starting GPU clouds. There's a whole bunch of companies, maybe 20 of them, who started during the NVIDIA time. And what they do is just one thing, they host GPUs. They call themselves GPU clouds. And one of our great partners, CoreWeave, is in the process of going public and we're super proud of them. And so GPU clouds, they have their own requirements. But one of the areas that I'm super excited about is Edge. And today, we announced today that Cisco, NVIDIA, T-Mobile, the largest telecommunications company in the world, Cerberus ODC are going to build a full stack for radio networks here in the United States. And that's going to be the second stack. So this current stack, this current stack we're announcing today, will put AI into the Edge. Remember, $100 billion of the world's capital investments each year is in the radio networks and all of the data centers provisioning for communications. In the future, there is no question in my mind that's going to be accelerated computing infused with AI. AI will do a far, far better job adapting the radio signals, the massive MIMOs to the changing environments and the traffic conditions. Of course, it would. Of course, we would use reinforcement learning to do that. Of course, MIMO is essentially one giant radio robot. Of course, it is. And so we will, of course, provide for those capabilities. Of course, AI could revolutionize communications. When I call home, you don't have to say but that few words because my wife knows where I work, what the condition is like, conversation carries on from yesterday, she kind of remembers what I like, don't like. And oftentimes, just a few words, you communicated a whole bunch. The reason for that is because of context and human priors, prior knowledge. Well, combining those capabilities could revolutionize communications, look what it's doing for video processing. Look what I just described earlier in 3D graphics. And so of course, we're going to do the same for Edge. So I'm super excited about the announcement that we made today, T-Mobile, Cisco, NVIDIA, Cerberus ODC, are going to build a full stack. Well, AI is going to go into every industry. That's just one. One of the earliest industries that AI went into was autonomous vehicles. The moment I saw AlexNet and we've been working on computer vision for a long time. The moment I saw AlexNet was such an inspiring moment, such an exciting moment, it caused us to decide to go all in on building self-driving cars. So we've been working on self-driving cars now for over a decade. We build technology that almost every single self-driving car company uses. It could be either in the data center, for example, Tesla uses a lots of NVIDIA GPUs in the data center. It could be in the data center or the car, Waymo and Wayve uses NVIDIA computers in data centers as well as the car. It could be just in the car. It's very rare but sometimes it's just in the car or they use all of our software in addition. We work with the car industry, however, the car industry would like us to work with them. We built all 3 computers, the training computer, the simulation computer and the robotics computer, the self-driving car computer, all the software stack that sits on top of it, models and algorithms, just as we do with all of the other industries that I've demonstrated. And so today, I'm super excited to announce that GM has selected NVIDIA to partner with them to build their future self-driving car fleet. The time for autonomous vehicles has arrived. And we're looking forward to building with GM, AI in all 3 areas: AI for manufacturing, so they can revolutionize the way they manufacture; AI for enterprise, so they can revolutionize the way they work, design cars and simulate cars; and then also AI for in the car. So AI infrastructure for GM, partnering with GM and building with GM, their AI. So I'm super excited about that. One of the areas that I'm deeply proud of and it rarely gets any attention is safety, automotive safety. It's called Halos. In our companies it's called Halos. Safety requires technology from silicon to systems to system software, the algorithms, the methodologies, everything from diversity to ensuring diversity, monitoring and transparency, explainability. All of these different philosophies have to be deeply ingrained into every single part of how you develop the system and the software. We're the first company in the world, I believe, to have every line of code safety assessed, 7 million lines of code safety assessed. Our chip, our system, our system software and our algorithms are safety assessed by third parties that crawl through every line of code to ensure that it is designed to ensure diversity, transparency and explainability. We also have filed over 1,000 patents. And during this GTC and I really encourage you to do so, is to go spend time in the Halos workshop so that you could see all of the different things that comes together to ensure that cars of the future are going to be safe as well as autonomous. And so this is something I'm very proud of. It barely -- it rarely gets any attention. And so I thought I would spend the extra time this time to talk about that. Okay? NVIDIA Halos. All of you have seen cars drive by themselves. The Waymo robo taxis are incredible. But we made a video to share with you some of the technology we use to solve the problems of data and training and diversity so that we could use the magic of AI to go create AI. Let's take a look. [Presentation]

NVIDIA is the perfect company to do that. Gosh, that's our destiny, use AI to recreate AI. The technology that we showed you there is very similar to the technology that you're enjoying to take you to a digital twin, we call NVIDIA. All right. Let's talk about data centers. That's not bad, huh. GauGAN's plants, just in case. GauGAN's plants. Well, let's talk about data centers. Blackwell is in full production, and this is what it looks like. It's an incredible, incredible -- for people, for us, this is a site of beauty. Would you agree? This is -- how is this not beautiful? How is this not beautiful? Well, this is a big deal because we made a fundamental transition in computer architecture. I just want you to know that, in fact, I've shown you a version of this about 3 years ago. It was called Grace Hopper and the system was called Ranger. The Ranger system is about -- maybe about half of the width of the screen. And it was the world's first NVLink 32. Three years ago, we showed Ranger working, and it was way too large, but it was exactly the right idea. We were trying to solve scale-up. Distributed computing is about using a whole lot of different computers working together to solve a very large problem. But there is no replacement for scaling up before you scale out. Both are important, but you want to scale up first before you scale out. Well, scaling up is incredibly hard, there is no simple answer for it. You're not going to scale it up. You're not going to scale it out like Hadoop, take a whole bunch of commodity computers hook it up into a large network and do in-storage computing using Hadoop. Hadoop was a revolutionary idea as we know. It enabled hyperscale data centers to solve problems of gigantic sizes and using off-the-shelf computers. However, the problem we're trying to solve is so complex that scaling in that way would have simply cost way too much power, way too much energy, it would have never -- deep learning would have never happened. And so the thing that we had to do was scale up first. Well, this is the way we scaled up. I'm not going to lift this. This is 70 pounds. This is the last generation system architecture is called HGX. This revolutionized computing, as we know it, this revolutionized artificial intelligence. This is 8 GPUs, 8 GPUs, each one of them is kind of like this, okay? This is 2 GPUs, 2 Blackwell GPUs in 1 Blackwell package, 2 Blackwell GPUs in 1 Blackwell package. And there are 8 of these underneath this, okay? And this connects into what we call NVLink 8. This then connects to a CPU shelf like that. So there's dual CPUs, and that sits on top. And we connect it over PCI Express and then many of these get connected with InfiniBand, which turns into what is an AI supercomputer. This is the way it was in the past. This is the way -- this is how we started. Well, this is as far as we scaled up before we scaled out. But we wanted to scale up even further. And I told you that Ranger took this system and scaled it out -- scaled it up by another factor of 4. And so we had NVLink 32, but the system was way too large. And so we had to do something quite remarkable, reengineer how NVLink worked and how scale-up worked. And so the first thing that we did was we said, listen, the NVLink switches are in this system embedded on the motherboard. We need to disaggregate the NVLink system and take it out. So this is the NVLink system, okay? This is an NVLink switch. This is the most -- this is the highest performance switch the world's ever made. And this makes it possible for every GPU to talk to every GPU at exactly the same time at full bandwidth, okay? So this is the NVLink switch. We disaggregated it. We took it out and we put it in the center of the chassis. So there's all the -- there are 18 of these switches in 9 different racks, 9 different switch trays, we call them. And then the switches are disaggregated. The compute is now sitting in here. This is equivalent to these 2 things in compute. What's amazing is this is completely liquid cooled. And by liquid cooling it, we can compress all of these compute nodes into one rack. This is the big change of the entire industry. All of you in the audience, I know how many of you are here. I want to thank you for making this fundamental shift from integrated NVLink to disaggregated NVLink, from air cooled to liquid cooled, from 60,000 components per computer or so to 600,000 components per rack, 120 kilowatts fully liquid cooled, and as a result, we have a 1 exaflops computer in one rack. Isn't incredible? So this is the compute node, this is the compute node, okay? And that now fits in one of these. Now we -- 3,000 pounds, 5,000 cables, about 2 miles worth, just an incredible electronics, .600,000 parts, I think that's like 20 cars, 20 cars worth of parts and integrates into one supercomputer. Well, our goal is to do this. Our goal is to do scale-up, and this is what it now looks like. We essentially wanted to build this chip. It's just that no radical limits can do this. No process technology can do this. It's 130 trillion transistors, 20 trillion of it is used for computing. So it's not like you can't reasonably build this anytime soon. And so the way to solve this problem is to disaggregate it, as I described, into the Grace Blackwell NVLink 72 rack. But as a result, we have done the ultimate scale-up. This is the most extreme scale-up the world has ever done. The amount of computation that's possible here, the memory bandwidth, 570 terabytes per second, everything in this machine is now in Ts. Everything is in trillion. And you have an exaflops, which is a million trillion floating point operations per second. Well, the reason why we wanted to do this is to solve an extreme problem. And that extreme problem a lot of people misunderstood to be easy. And in fact, it is the ultimate extreme computing problem, and it's called inference. And the reason for that is very simple. Inference is token generation by a factory and a factory is revenue and profit generating or lack of. And so this factory has to be built with extreme efficiency, with extreme performance because everything about this factory directly affects your quality of service, your revenues and your profitability. Let me show you how to read this chart because I want to come back to this a few more times. Basically, you have 2 axes. On the X-axis, is the tokens per second. Whenever you chat -- when you put a prompt into ChatGPT, what comes out is tokens, those tokens are reformulated into words. It's more than a token per word, okay? And they'll tokenize things like T-H-E could be used for the, it could be used for them, it could be used for theory, it could be used for theatrics, it could be used for all kinds of -- okay? And so T-H-E is an example of a token. They reformulate these tokens to turn into words. Well, we've already established that if you want your AI to be smarter, you want to generate a whole bunch of tokens, those tokens are reasoning tokens, consistency checking tokens. It's coming up with a whole bunch of ideas so they can select the best of those ideas tokens. And so those tokens, it might be second guessing itself. It might be -- is this the best work you could do? And so ask -- it talks to itself, just like we talk to ourselves. And so but more tokens you generate the smarter your AI. But if you take too long to answer a question, the customer is not going to come back. This is no different than web search. There is a real limit to how long it can take before it comes back with a smart answer. And so you have these 2 dimensions that you're fighting against. You're trying to generate a whole bunch of tokens, but you're trying to do it as quickly as possible. Therefore, your token rate matters. So you want your tokens per second for that one user to be as fast as possible. However, in computer sciences and factories, there's a fundamental tension between latency response time and throughput. And the reason is very simple. If you're in the large, high-volume business, you batch up, it's called batching, you batch up a lot of customer demand and you manufacture a certain version of it for everybody to consume later. However, from the moment that they batched up and manufacture whatever they did to the time that you consumed it could take a long time. So no different for computer science, no different than -- no different from AI factories that are generating tokens. And so you have these 2 fundamental tensions. On the one hand, you would like the customer's quality of service to be as good as possible, smart AIs that are superfast. On the other hand, you're trying to get your data center to produce tokens for as many people as possible so you can maximize your revenues. The perfect answer is to the upper right. Ideally, the shape of that curve is a square that you could generate very fast tokens per person up until the limits of the factory, but no factory can do that. And so it's probably some curve. And your goal is to maximize the area under the curve, okay, the product of X and Y, and the further you push out the, more likely it means the better of a factory that you're building. Well, it turns out that in tokens per second for the whole factory and tokens per second response time, one of them requires enormous amount of computation FLOPS and then the other dimension requires an enormous amount of bandwidth and FLOPS. And so this is a very difficult problem to solve. The good answer is that you should have lots of FLOPS and lots of bandwidth and lots of memory and lots of everything. That's the best answer to start, which is the reason why this is such a great computer. You start with the most FLOPS you can, the most memory you can, the most bandwidth you can, of course, the best architecture you can, the most energy efficiency you can. And you have to have a programming model that allows you to run software across all of this insanely hard so that you could do this. Now let's just take a look at this one demo to give you a tactical feeling of what I'm talking about. Please play it. [Presentation]

Okay. as all of you know, if you have a wedding party of 300 and you're trying to find the perfect -- well, the optimal seating for everyone, that's a problem that only AI can solve or a mother-in-law can solve. And so that's one of those problems that co-op cannot solve. Okay. So what you see here is that we gave it a problem that requires reasoning, and you saw R1 goes off and it reasons about it, tries all these different scenarios, and it comes back and it tests its own answer. It asks itself whether it did it right. Meanwhile, the last generation language model does a one shot. So the one shot is 439 tokens. It was fast. It was effective, but it was wrong. So it's 439 wasted tokens. On the other hand, in order for you to reason about this problem, and this is just a -- that was actually a very simple problem. We just give it a few more difficult variables and it becomes very difficult to reason through and it took 8,000, almost 9,000 tokens. And it took a lot more computation because the model is more complex. Okay. So that's one dimension. Before I show you some results, let me just -- let me explain something else. So the answer, if you look at Blackwell -- if you look at the Blackwell system, and it's now the scaled up NVLink 72. The first thing that we have to do is we have to take this model. And this model is not small. It's -- in the case of R1, people think R1 is small, but it's 608 billion parameters. Next-generation models could be trillions of parameters. And the way that you solve that problem is you take these trillions and trillions of parameters and this model and you distribute the workload across the whole system of GPUs. You can use tensor parallel. You can take one layer of the model and run it across multiple GPUs. You could take a slice of the pipeline and call that pipeline parallel and put that on multiple GPUs. You could take different experts and put it across different GPUs, we call it expert parallel. The combination of pipeline parallelism and tensor parallelism and expert parallelism, the number of combinations is insane. And depending on the model, depending on the workload, depending on the circumstance, how you configure that computer has to change so that you can get the maximum throughput out of it. You also sometimes optimize for very low latency, sometimes you try to optimize for throughput, and so you have to do some in-flight batching, a lot of different techniques for batching and aggregating work. And so the software, the operating system for these AI factories is insanely complicated. Well, one of the observations, and this is a really terrific thing about having a homogeneous architecture like NVLink 72 is that every single GPU could do all the things that I just described. And we observe that these reasoning models are doing a couple of phases of computing. One of the phases of computing is thinking. When you're thinking you're not producing a lot of tokens, you're producing tokens that you're maybe consuming yourself, you're thinking. Maybe you're reading, you're digesting information. That information could be a PDF, that information could be a website. You can literally be watching a video, ingesting all of that at super linear rates, and you take all of that information and you then formulate the answer, formulate a planned answer. And so that digestion of information, context processing is very FLOPS intensive. On the other hand, during the next phase, it's called decode. So the first part we call prefill. The next phase of decode requires floating point operations but it requires an enormous amount of bandwidth. And it's fairly easy to calculate. If you have a model and it's a few trillion parameters, well, it takes a few terabytes per second. Notice, I was mentioning 576 terabytes per second. It takes terabytes per second to just pull the model in from HBM memory and to generate literally one token. And the reason it generates one token is because remember that these large language models are predicting the next token, that's why they say the next token. It's not predicting every single token, it's predicting the next token. Now we have all kinds of new techniques, speculative decoding and all kinds of new techniques for doing that faster. But in the final analysis, you're predicting the next token, okay? And so you ingest, pull in the entire model and the context we call it a KV cache, and then we produce one token. And then we take that one token, we put it back into our brain, we produce the next token. Every single one -- every single time we do that, we take trillions of parameters in would produce one token. Trillions of parameters in, produce another token. Trillions of parameters in, produce another token. And notice that demo, we produced 8,600 tokens. So trillions of bytes of information, trillions of bytes of information have been taken into our GPUs and produce one token at a time, which is fundamentally the reason why you want NVLink. NVLink gives us the ability to take all of those GPUs and turn them into one massive GPU, the ultimate scale-up. And the second thing is that now that everything is on NVLink, I can disaggregate the prefill from the decode and I could decide I want to use more GPUs for prefill, less for decode because I'm thinking a lot. I'm doing -- it's agenetic. I'm reading a lot of information. I'm doing deep research. Noticed during deep research. And earlier, I was listening to Michael and Michael was talking about him doing research, and I do the same thing. And we go off and we write these really long research projects for our AI, and I love doing that because I already paid for it. And I just love making our GPUs work and nothing gives me more joy. So I write -- and then it goes off and it does all this research, and it went off to like 94 different websites and it read all this thing, I'm reading all this information and it formulates an answer, and writes the report, it's incredible, okay? During that entire time, prefill is super busy, and it's not really generating that many tokens. On the other hand, when you're chatting with the chatbot and millions of us are doing the same thing, it is very token generation heavy. It's very decode heavy, okay? And so depending on the workload, we might decide to put more GPUs into decode. Depending on the workload, put more GPUs into prefill. Well, this dynamic operation is really complicated. So I've just now described pipeline parallel, tensor parallel, expert parallel, in-flight batching, disaggregated inferencing, workload management. And then I've got to take this thing called the KV cache. I got to route it to the right GPU. I've got to manage it through all the memory hierarchies. That piece of software is insanely complicated. And so today, we're announcing the NVIDIA Dynamo. NVIDIA Dynamo does all that. It is essentially the operating system of an AI factory. Whereas in the past, in the way that we ran data centers, our operating system would be something like VMware, and we would orchestrate and we still do. We're a big user, we would orchestrate a whole bunch of different enterprise applications running on top of our enterprise IT. But in the future, the application is not enterprise IT, it's agents. And the operating system is not something like VMware, it's something like Dynamo. And this operating system is running on top of not a data center, but on top of an AI factory. Now we call it Dynamo a good reason. As you know, the dynamo was the first instrument that started the last industrial revolution, the industrial revolution of energy. Water comes in, electricity comes out. It's pretty fantastic. Water comes in, you light it on fire, turn to the steam. And what comes out of this is an invisible thing that's incredibly valuable. It took another 80 years to go to alternating current, but Dynamo. Dynamo Is where it all started, okay? So we decided to call this operating system, this piece of software insanely complicated software, the NVIDIA Dynamo. It's open source. It's open source, and we're so happy that so many of our partners are working with us on it. And one of my favorite partners, I just love them so much because the revolutionary work that they do and also because Aravind is such a great guy, but Perplexity is a great partner of ours in working through this, okay? So anyhow, really, really great. Okay. So now we're going to have to wait until we scale up all these infrastructure. But in the meantime, we've done a whole bunch of very in-depth simulation. We have supercomputers doing simulation of our supercomputers, which makes sense. And I'm now going to show you the benefit of everything that I've just said. And remember, the factory diagram on the X-axis is tokens per second throughput -- excuse me, on the Y-axis tokens per second throughput of the factory and the X-axis tokens per second of the user experience. And you want super smart AIs and you want to produce a whole bunch of it. This is Hopper, okay? So this is Hopper. And it can produce -- it can produce for one user about -- for each user, about 100 tokens per second. This is 8 GPUs, and it's connected with InfiniBand and the -- I'm normalizing it to tokens per second per megawatt. So it's a 1-megawatt data center, which is not a very large AI factory, but anyhow 1-megawatt, okay? And so it can produce for each user, 100 tokens per second and it can produce at this level, whatever that happens to be 100,000 tokens per second for that 1-megawatt data center or it can produce about 2.5 million tokens per second, 2.5 million tokens per second for that AI factory if it was super batched up and the customer is willing to wait a very long time, okay? Does that make sense? All right. So -- not. All right, because this is where -- every GTC, there's the price for entry you guys know. And you get tortured with mouth, okay? This is the only -- only at NVIDIA do you get tortured with mouth. All right. So Hopper -- you get 2.5 -- now what's that 2.5 million? What's -- how do you translate that? 2.5 million, remember, ChatGPT is like $10 per million tokens, right? $10 per million tokens. Let's pretend for a second that that's -- I think the 10 million -- $10 per million tokens is probably down here, okay? I'd probably say it's down here. But let me pretend is up there because 2.5 million 10, million, so $25 million per second. Does that make sense? That's how you think through it. Or on the other hand, if it's way down here, then the question is, so it's 100,000, 100,000 just divide that by 10, okay, $250,000 per factory per second. And then what's it, 31 million, 30 million seconds in a year, and that translates into revenues for that 1 million -- that 1-megawatt data center. And so that's your goal. On the one hand, you would like your token rate to be as fast as possible so that you can make really smart AIs. And if you have smart AIs, people pay you more money for it. On the other hand, the smarter the AI, the less you can make in volume. Very sensible trade-off. And this is the curve we're trying to bend. Now what I'm showing you right now is the fastest computer in the world. Hopper, it's the computer that revolutionized everything. And so how do we make that better? So the first thing that we do is we come up with Blackwell with NVLink 8. Same Blackwell, that one same compute, and then one compute node with NVLink 8 using FP8. And so Blackwell is just faster. Faster, bigger, more transistors, more everything. But we like to do more than that. And so we introduced a new precision. It's not quite as simple as 4-bit floating point. But using 4-bit floating point, we can quantize the model, use less energy, use less energy to do the same. And as a result, when you use less energy to do the same, you could do more. Because remember, one big idea is that every single data center in the future will be power limited. Your revenues are powered limited. You can figure out what your revenues are going to be based on the power you have to work with. This is no different than many other industries. And so we are now a power-limited industry. Our revenues were associated with that. Well, based on that, you want to make sure you have the most energy-efficient compute architecture you can possibly get. The next -- then we scale up with NVLink 72. Does it make sense? Look at the difference between that NVLink 72 FP4. And then because our architecture is so tightly integrated, and now we add Dynamo to it, Dynamo can extend that even further. Are you following me? So Dynamo also helps Hopper, but Dynamo helps Blackwell incredibly. Now -- yes. Only at GTC do you get an applause for that. And so now notice what I put those 2 shiny parts, that's kind of where your Max-Q is. That's likely where you run your factory operations. You're trying to find that balance between maximum throughput and maximum quality of AI, smart as AI, the most of it. Those 2, that X/Y intercept is really what you're optimizing for. And that's what it looks like if you look underneath those 2 squares. Blackwell is way, way better than Hopper. And remember, this is not ISO chips. This is ISO power. This is ultimate Moore's Law. This is what Moore's Law was always about in the past. And now here we are 25x in one generation as ISO power. It's not ISO chips, it's not ISO transistors, it's not ISO anything, ISO power, the ultimate limiter, there's only so much energy we can get into a data center. And so within ISO power, Blackwell is 25x. Now here's that rainbow, that's incredible. That's the fun part. Look, all the different -- every -- underneath the Pareto, the frontier Pareto, we call it the frontier Pareto. Under the Frontier Pareto are millions of points we could have configured the data center to do. We could have paralyzed and split the work and charted the work in a whole lot of different ways. And we found the most optimal answer, which is the Pareto, the frontier Pareto. Okay, the Pareto frontier. And each one of them because of the color shows you it's a different configuration, which is the reason why this image says very, very clearly, you want a programmable architecture that is as homogeneously fungible, as fungible as possible because the workload changes so dramatically across the entire frontier. And look, we got, on the top, expert parallel 8, batch of 3,000, disaggregation off, Dynamo off. In the middle, expert parallel 64 with -- oh, the 26% of -- 26% is used for context. So Dynamo is turned on, 26% context. The other 64% is -- 74% is not, batch of 64 and expert parallel 64 on one, expert parallel 4 on the other. And then down here all the way to the bottom, you got tensor parallel 16 with expert parallel 4, batch of 2, 1% context. The configuration of the computer is changing across that entire spectrum. And then this is what happens. So this is with input sequence length, this is kind of a commodity test case. This is a test case that you can benchmark relatively easily. The input is 1,000 tokens. The output is 2,000. Notice earlier, we just showed you a demo where the output is very simply 9,000, right, 8,000, okay? And so obviously, this is not representative of just that one chat. Now this one is more representative, and this is what the goal is to build these next-generation computers for next-generation workloads. And so here's an example of a reasoning model. And in a reasoning model, Blackwell is 40x, 40x the performance of Hopper, straight up. Pretty amazing. I've said before, somebody actually asked why would I say that? But I said before that when Blackwell starts shipping in volume, you couldn't give Hoppers away. And this is what I mean. And this makes sense. If anybody -- if you're still looking to buy a Hopper, don't be afraid. It's okay. But -- I'm the chief revenue destroyer. My sales guys are going, "Oh, no, don't say that." There are circumstances where Hopper is fine. That's the best thing I could say about Hopper. There are circumstances where you're fine. Not many. If I had to take a swing and so that's kind of my point. When the technology is moving this fast, you -- and because the workload is so intense and you're building these things, there are factories, we really like you to invest in the right versions, okay? Just to put it in perspective, this is what a 100-megawatt factory looks like. There's a 100-megawatt factory. You have -- based on Hoppers, you have 45,000 dies, 1,400 racks and it produces 300 million tokens per second, okay? And then this is what it looks like with Blackwell. You have -- yes, I know. That doesn't make any sense. Okay. So we're not trying to sell you less, okay, our sales guys are going "Jen-Hsun, you're selling them less." This is better, okay? And so anyways, the more you buy, the more you save. It's even better than that. Now the more you buy, the more you make. And so anyhow, remember, everything is in the context -- everything is now in the context of AI factories. And although we talk about the chips, you always start from scale-up. We talk about the chips, but you always start from scale-up, the full scale-up. What can you scale up to the maximum? I want to show you now what an AI factory looks like, but AI factories are so complicated. I just gave you an example of one rack, it is 600,000 parts. It's 3,000 pounds. Now you've got to take that and connect it with a whole bunch of others. And so we are starting to build what we call the digital twin of every data center. Before you build a data center, you have to build a digital twin, let's take a look at this. This is just incredibly beautiful. [Presentation]

This is the first time anybody who builds data centers goes, "Oh, that's so beautiful." All right. I got a race here because it turns out, I got a lot to tell you. And so if I go a little too fast, it's not because I don't care about you. It's just I got a lot of information to go through, right? So first, our road map. We're now in full production of Blackwell. Computer companies all over the world are ramping these incredible machines at scale. And I'm just so pleased and so grateful that all of you worked hard on transitioning into this new architecture. And now in the second half of this year, we'll easily transition into the upgrade. So we had the Blackwell Ultra NVLink 72. It's 1.5x more FLOPS, it's got a new instruction for attention, it's 1.5x more memory. All that memory is useful for things like KV cache. It's 2x more bandwidth, okay, for networking bandwidth. And so you're going to -- now that we have the same architecture, which is kind of gracefully glide into that, and that's called Blackwell Ultra, okay? So that's coming second half of this year. Now there's a reason why we -- this is the only product announcement in any company where everybody's going, yes, next. And in fact, that's exactly the response I was hoping to get. And here's why. Look, we're building AI factories and AI infrastructure. It's going to take years of planning. This isn't like buying a laptop. This isn't discretionary spend. this is spend that we have to go plan on. And so we have to plan on having, of course, the land and the power, and we have to get to our CapEx ready and we get engineering teams and we have to lay it out a couple of 2, 3 years in advance, which is the reason why I show you our road map a couple 2, 3 years in advance. So that we don't surprise you in May. Hi -- in another month, we're going to go to this incredible new system, and I'll show you an example in a second. And so we plan to sell in multiple years. The next click, one year out, is named after an astronomer and her grandkids are here. Her name is Vera Rubin. She discovered Dark Matter. Okay. Yes. Vera Rubin is incredible because the CPU is new, is twice the performance of Grace, more memory, more bandwidth and yet just a little tiny 50-watt CPU. It's really quite incredible, okay? And Rubin, brand-new GPU, CX9, brand-new networking, SmartNIC, NVLink 6, brand-new NVLink, brand-new memories, HBM4. Basically, everything is brand new, except for the chassis. And this way, we could take a whole lot of risk in one direction and not risk a whole bunch of other things related to the infrastructure. And so Vera Rubin, NVLink 144, is the second half of next year. Now one of the things that I made a mistake on, I just need you to make this pivot, we're going to do this one time. Blackwell is really 2 GPUs in one Blackwell chip. We call that one chip a GPU, and that was wrong. And the reason for that is it screws up all the NVLink nomenclature and things like that. So going forward, without going back to Blackwell to fix it, going forward, when I say NVLink 144, it just means that it's connected to 144 GPUs. And each one of those GPUs is a GPU die, and it could be assembled in some package. How it's assembled could change from time to time, okay? And so each GPU die is a GPU, each NVLink is connected to the GPU. And so Vera Rubin NVLink 144. And then this now sets the stage for the second half of the year, the following year we call Rubin Ultra, okay? So Vera Rubin Ultra. I know. This one is what you should -- you go. All right. So this is Vera Rubin -- Rubin Ultra. Second half of '27. It's NVLink 576 extreme scale-up, each rack is 600 kilowatts, 2.5 million parts, okay? And obviously, a whole lot of GPUS. And everything is X factor more. So 14x more FLOPS, 15 exaflops instead of 1 exaflop, as I mentioned earlier, is now 15 exaflops, scaled up exaflops, okay? And it's 300 -- 4.6 petabytes, so 4,600 terabytes per second scale-up bandwidth. I don't mean aggregate, I mean scale-up bandwidth. And of course, lots -- a brand-new NVLink switch and CX9, okay? And so notice 16 sites, 4 GPUs in one package. Extremely large NVLink. Now, just to put that in perspective, this is what it looks like, okay? Now this is going to be fun. So this -- you are just literally ramping up Grace Blackwell at the moment. And I don't mean to make it look like a laptop, but here we go, okay? So this is what Grace Blackwell looks like, and this is what Rubin looks like. ISO dimension. And so this is another way of saying, before you scale out, you have to scale up. Does it make sense? Before you scale up, scale out, you scale up. And then after that, you scale out with amazing technology that I'll show you in just a second, right? So first, you scale up. And then now that gives you a sense of the pace at which we're moving. This is the amount of scale-up FLOPS. This is scale-up FLOPS. Hopper is 1x, Blackwell is 68x, Rubin is 900x, scale-up FLOPS. And then if I turn it into essentially your TCO, which is power on top, power per and the underneath is the area underneath the curve, that I was talking to you about, the square underneath the curve, which is basically FLOPS x bandwidth, okay? So the way you think about -- a very easy gut feel, gut check on whether your AI factories are making progress is watts divided by those numbers. And you can see that Rubin is going to drive the cost down tremendously, okay? So that's very quickly NVIDIA's road map. Once a year like clock ticks, once a year. Okay, how do we scale up? Well, we introduced -- we were preparing to scale out. That will scale up as NVLink. Our scale-out network is InfiniBand and Spectrum-X. Most were quite surprised that we came into the ethernet world. And the reason why we decided to do ethernet is if we could help ethernet become like InfiniBand, have the qualities of InfiniBand, then the network itself would be a lot easier for everybody to use and manage. And so we decided to invest in spectrum, we call it Spectrum-X. We brought to it the properties of congestion control and very low latency and amount of software that's part of our computing fabric. And as a result, we made Spectrum-X incredibly high-performing. We scaled up the largest single GPU cluster ever as one giant cluster with Spectrum-X, right? And that was Colossus. And so there are many other examples of it. Spectrum-X is unquestionably a huge home run for us. One of the areas that I'm very excited about its largest enterprise networking company to take Spectrum-X and integrate it into their product line so that they could help the world's enterprises become AI companies. We're at 100,000 with CX8 -- CX7, now CX8 is coming, CX9 is coming. And during Rubin's time frame, we would like to scale out the number of GPUs to many hundreds of thousands. Now the challenge what's scaling up GPUs to many hundreds of thousands is the connection of the scale-out. The connection on scale-up is copper. We should use copper as far as we can. And that's, call it, 1 meter or 2. And that's incredibly good connectivity, very high reliability, very good energy efficiency, very low cost. And so we use copper as much as we can on scale-up. But on scale-out where the data centers are now the size of the stadium, we're going to need something much long-distance running, and that is where silicon photonics comes in. The challenge of silicon photonics has been that the transceivers consume a lot of energy. To go from electrical to photonic has to go through a SerDes, go through a transceiver and a SerDes, several SerDes. And so each one of these, each one of these, each one of these -- am I alone? Is there anybody? What happened to my networking guys? Can I have this up here? Yes. Yes, let's bring it up. So I could show people what I'm talking about. Okay. So first of all, we're announcing NVIDIA's first co-packaged option, silicon photonic system. It is the world's first 1.6 terabit per second CPO. We're going to -- it is based on a technology called micro ring resonator modulator, and it is completely built with this incredible process technology at TSMC that we've been working with for some time. And we partnered with just a giant ecosystem of technology providers to invent what I'm about to show you. This is really crazy technology, crazy, crazy technology. Now the reason why we decided to invest in MRM is so that we could prepare ourselves using MRM's incredible density and power, better density and power compared to M+W Zander which is used for telecommunications when you drive from one data center to another data center in telecommunications or even in the transceivers that we use, we use M+W Zander because the density requirement is not very high. Until now. And so if you look at these transceivers, this is an example of a transceiver. They did a very good job of tangling this up for me. Thank you. Okay. This is where you go to turn reasoning on. It's not as easy as you think. These are squirrely little things. All right. So this one right here, this is 30 watts. Just -- keep remembered, this is 30 watts. And if you get it on -- if you buy in a high volume, it's $1,000. This is a plug. On this side is electrical. On this side is optical, okay? So optics come in through the yellow. You plug this into a switch. It's electrical on this side. There's transceivers, lasers, and so technology called M+W Zander and incredible. And so we use this to go from the GPU to the switch, to the next switch, and then the next switch down and the next switch down to the GPU, for example. And so each one of these, if we had 100,000 GPUs, we would have 100,000 of this side and then another 100,000, which connects the switch to switch and then on the other side, I'll attribute that to the other NIC. If we had 250,000, we add another layer of switches. And so each GPU, every GPU, 250,000, every GPU would have 6 transceivers. Every GPU would have 6 of these plugs. And these 6 plugs would add 180 watts per GPU, 180 watts per GPU and $6,000 per GPU, okay? And so the question is how do we scale up now to millions of GPUs? Because if we had 1 million GPUs multiplied by 6, right, it would be 6 million transceivers x 30 watts, 180 megawatts of transceivers. They didn't do any math, they just move signals around. And so the question is how do we -- how could we afford? And as I mentioned earlier, energy is our most important commodity. Everything is related ultimate to energy. So this is going to limit our revenues, our customers' revenues by subtracting out 180 megawatts of power. And so this is the amazing thing that we did. We invented the world's first MRM, micromirror. And this is what it looks like. There's a little waveguide, you see that on that -- waveguide goes to a ring. That ring resonates and it controls the amount of reflectivity of the wave guide as it goes around and it limits and modulates the energy, the amount of light that goes through, and it then shuts it off by absorbing it or pass it on, okay? It turns the light -- this direct continuous laser beam into 1s and 0s. And that's the miracle. And that technology is then -- that photonic IC is stacked with the electronic IC, which is then stacked with a whole bunch of micro lenses, which is stacked with this thing called fiber array. These things are all manufactured using this technology at TSMC, they call it COUPE and package using a CoWoS technology working with all of these technology providers, a whole bunch of them, the names I just showed you earlier, and it turns it into this incredible machine. So let's take a look at the video. [Presentation]

Just a technology marvel. And they turn into these switches. Our InfiniBand switch, the silicon is working fantastically. Second half of this year, we will ship the silicon photonics switch in the second half of this year. And the second half of next year, we will ship the Spectrum-X. Because of the MRM choice, because of the incredible technology risks that over the last 5 years that we did and filed hundreds of patents, and we've licensed it to our partners so that we can all build them, now we're in a position to put silicon photonics with co-packaged options, no transceivers, fiber -- direct fiber in into our switches, with a Radix of 512. This is the 512 ports. This would just simply not be possible any other way. And so this is -- this now set us up to be able to scale up to these multi-hundred thousand GPUs and multimillion GPUs. And the benefit, just so you imagine this is incredible, in a data center, we could save tens of megawatts. Tens of megawatts. Let's say, 10 megawatts -- well, let's say, 60 megawatts. 60 -- or 6 megawatts is 10 Rubin Ultra racks. 6 megawatts is 10 Rubin Ultra racks, right? And 60 that's a lot, 100 Rubin Ultra racks of power that we can now deploy into Rubins, right? So this is our road map once a year, once a year, an architecture every 2 years, a new product line every single year, X factors up, and we try to take silicon risk or networking risk or system chassis risk in pieces so that we can move the industry forward as we pursue these incredible technology. Vera Rubin, and I really appreciate the grandkids for being here. This is our opportunity to recognize her and to honor her for the incredible work that she did. Our next generation will be named after Feynman. Okay, NVIDIA's road map. Let me talk to you about enterprise computing. This is really important. In order for us to bring AI to the world's enterprise, first, we have to go to a different part of NVIDIA. The beauty of GauGAN's plants. Okay. In order for us to take AI to enterprise, take a step back for a second and remind yourself this. Remember, AI and machine learning has reinvented the entire computing stack. The processor is different. The operating system is different. The applications on top are different. The way -- the applications are different, the way you orchestrate it are different and the way you run them are different. Let me give you one example. The way you access data will be fundamentally different than the past. Instead of retrieving precisely the data that you want and you read it to try to understand it, in the future, we will do what we do with Perplexity. Instead of doing retrieval that way, I'll just ask Perplexity what I want. Ask it a question and it will tell you the answer. This is the way enterprise IT will work in the future as well. We'll have AI agents, which are part of our digital workforce. There's 1 billion knowledge workers in the world. They're probably going to be 10 billion digital workers working with us side by side. 100% of software engineers in the future, there are 30 million of them around the world, 100% of them are going to be AI-assisted. I'm certain of that. 100% of NVIDIA software engineers will be AI-assisted by the end of this year. And so AI agents will be everywhere, how they run, what enterprises run and how we run it will be fundamentally different. And so we need a new line of computers. This is what a PC should look like. 20 petaflops. Unbelievable, 72 CPU cores, chip-to-chip interface, HBM memory, and just in case some PCI Express lots for your GeForce, okay? So this is called DGX Station. DGX Spark and DGX Station are going to be available by all of the OEMs, HP, Dell, Lenovo, ASUS. It's going to be manufactured for data scientists and researchers all over the world. This is the computer of the age of AI. This is what computers should look like, and this is what computers will run in the future. And we have a whole lineup for enterprise now from little tiny one to workstation ones, the server ones to supercomputer ones, and these will be available by all of our partners. We will also revolutionize the rest of the computing stack. Remember, computing has 3 pillars. There's computing, you're looking at it. There's networking, as I mentioned earlier, Spectrum-X going to the world's enterprise and AI network. And then third is storage. Storage has to be completely reinvented. Rather than a retrieval-based storage system, it's going to be a semantics-based retrieval system, a semantics-based storage system. And so the storage system has to be continuously embedding information in the background, taking raw data, embedding it into knowledge, and then later, when you access it, you don't retrieve it, you just talk to it. You ask it questions, you give it problems. And one of the examples I wish we had a video of it, but Aaron at Box even put one up in the cloud, worked with us to put it up in the cloud. And it's basically a super smart storage system. And in the future, you're going to have something like that in every single enterprise. That is the enterprise storage of the future. And we're working with the entire storage industry, really fantastic partners, DDN and Dell and HP Enterprise and Hitachi and IBM and NetApp and Nutanix and Pure Storage and VAST and WEKA. Basically, the entire world storage industry will be offering this stack. For the very first time, your storage system will be GPU accelerated. And so somebody thought I was -- I didn't have enough slides. And so Michael thought I didn't have enough slides so he said Jen-Hsun, just in case you don't have enough slides, can I just put this in there? And so this is Michael's slides. But this is -- this -- he sent this to me, he goes, just in case you don't have any slides, and I got too many slides. But this is such a great slide, and let me tell you why. In one single slide he's explaining that Dell is going to be offering a whole line of NVIDIA Enterprise IT AI infrastructure systems and all the software that runs on top of it, okay? So you can see that we're in the process of revolutionizing the world's enterprise. We're also announcing today this incredible model that everybody can run. And so I showed you earlier, R1, a reasoning model. I showed you versus Llama 3, a non-reasoning model. And obviously, R1 is much smarter, but we can do it even better than that. And we can make it possible to be enterprise ready for any company, and it's now completely open source as part of our system, we call NIMs and you can download it. You can run it anywhere. You can run it on DGX Spark. You can run it on a DGX Station. You can run it on any of the servers that the OEMs make. You can run it in the cloud. You can integrate into any of your agentic AI frameworks. And we're working with companies all over the world. And I'm going to flip through these so watch very carefully. I've got some great partners in the audience, I want to recognize Accenture, Julie Sweet and her team are building their AI factory and their AI framework. Amdocs, the world's largest telecommunication software company. AT&T, John Stankey and his team building an AT&T AI system, agentic system. Larry Fink and BlackRock team building theirs. Anirudh, in the future, not only will we hire ASIC designers, we're going to hire a whole bunch of digital ASIC designers from Anirudh's Cadence that will help us design our chips. And so Cadence is building their AI framework. And as you can see in every single one of them is NVIDIA models, NVIDIA NIMs, NVIDIA libraries integrated throughout so that you can run it on-prem, in the cloud, any cloud. Capital One, one of the most advanced financial services companies and using technology has NVIDIA all over it. Deloitte, Jason and his team, E&Y, Janet and his team, Nasdaq and Adena and her team, integrating NVIDIA technology into their AI frameworks and then Christian and his team at SAP, Bill McDermott and his team at ServiceNow. That was pretty good. First -- this is one of those keynotes where the first slide took 30 minutes. And then all the other slides took 30 minutes, all right? So next, let's go somewhere else. Let's go talk about robotics. Shall we? Let's talk about robots. Well, the time has come for robots. Robots have the benefit of being able to interact with the physical world and do things that otherwise digital information cannot. We know very clearly that the world is -- has severe shortage of human labor, human workers. By the end of this decade, the world is going to be at least 50 million workers short. We'd be more than delighted to pay them each $50,000 to come to work. We're probably going to have to pay robots $50,000 a year to come to work. And so this is going to be a very, very large industry. There are all kinds of robotic systems. Your infrastructure will be robotic. Billions of cameras and warehouses and factories, 10 million, 20 million factories around the world. Every car is already a robot, as I mentioned earlier, and then now we're building general robots. Let me show you how we're doing that. [Presentation]

Physical AI and robotics are moving so fast. Everybody pay attention to this space. This could very well likely be the largest industry of all. At its core, we have the same challenges. As I mentioned before, there are 3 that we focus on. They are rather systematic. One, how do you solve the data problem? How, where do you create the data necessary to train the AI? Two, what's the model architecture? And then three, what's the scaling loss? How can we scale either the data, the compute or both so that we can make AI smarter and smarter and smarter? How do we scale? And those 2 -- those fundamental problems exist in robotics as well. In robotics, we created a system called Omniverse. It's our operating system with physical AIs. You've heard me talk about Omniverse for a long time. We added 2 technologies to it. Today, I'm going to show you 2 things. One of them is so that we could scale AI with generative capabilities and generative model that understand the physical world, we call it Cosmos. Using Omniverse to condition Cosmos and using Cosmos to generate an infinite number of environments allows us to create data that is grounded, grounded, controlled by us and yet be systematically infinite at the same time, okay. So you see Omniverse, we use candy colors to give you an example of us controlling the robot in the scenario perfectly and yet Cosmos can create all these virtual environments. The second thing just as we were talking about earlier, one of the incredible scaling capabilities of language models today is reinforcement learning, verifiable rewards. The question is what's the verifiable rewards in robotics. And as we know very well, it's the laws of physics, verifiable physics rewards. And so we need an incredible physics engine. Well, most physics engines have been designed for a variety of reasons. It could be designed because we want to use it for large machineries or maybe we design it for virtual worlds, video games and such. But we need a physics engine that is designed for very fine grained, rigid and soft bodies, designed for being able to train, tactile feedback and fine motor skills and actuator controls. We needed to be GPU accelerated so that we -- these virtual worlds could live in super linear time, super real-time and train these AI models incredibly fast, and we needed to be integrated harmoniously into a framework that is used by roboticist all over the world with MuJoCo. And so today, we're announcing something really, really special. It is a partnership of 3 companies: DeepMind, Disney Research and NVIDIA, and we call it Newton. Let's take a look at Newton. [Presentation]

Thank you. All right, let's start that over. Shall we? Let's not run it for them. Hang on a second. Somebody talk to me, I need feedback. What happened? Who -- I just need a human to talk to. Come on that's a good joke. Give me a human to talk to. [ Janine, ] I know it's not your fault, but talk to me. We got -- we just got 2 minutes left.

I am right here. They're re-racking it.

They are re-racking it. I don't know what that means? Okay. [Presentation]

Tell me that wasn't amazing. Hey, Blue. How are you doing? How do you like your new physics engine? You like it? Yes, I bet. I know. Tactile feedback, rigid body, soft body, simulation, super real time. Can you imagine just now what you're looking at is complete real-time simulation? This is how we're going to train robots in the future. Just so you know, Blue has 2 computers, 2 NVIDIA computers inside. Look how smart you are? Yes, you're smart. Okay. All right. Hey, Blue, listen, how about let's take them home. Let's finish this keynote. It's lunchtime. Are you ready? Let's finish it up. We have another announcement -- you're good. You're good. Just stand right here. Stand right here. All right, good. Right there. That's good. All right, stand. Okay. We have another amazing news. I told you the progress of our robotics has been making enormous progress. And today, we're announcing that GR00T N1 is open sourced. I want to thank all of you to come -- let's wrap up. I want to thank all of you for coming to GTC. We talked about several things. One, Blackwell is in full production, and the ramp is incredible. Customer demand is incredible and for good reason because there is an inflection point in AI, the amount of computation we have to do in AI is so much greater as a result of reasoning AI and the training of reasoning AI systems and agentic systems. Second, Blackwell NVLink 72 with Dynamo is 40x the performance -- AI factory performance of Hopper and inference is going to be one of the most important workloads in the next decade as we scale out AI. Third, we have annual rhythm of road maps that has been laid out for you so that you could plan your AI infrastructure. And then we have 2 -- we have 3 AI infrastructures we're building: AI infrastructure for the cloud, AI infrastructure for enterprise and AI infrastructure for robots. We have one more treat for you, play it. [Presentation]

Thank you, everybody. Thank you for all the partners that made this video possible. Thank you, everybody, that made this video possible. Have a great GTC. Thank you. Hey, Blue, let's go home, good job. Good little man. Thank you. I love you, too. Thank you.