NVIDIA Corporation (NVDA) Earnings Call Transcript & Summary

Please welcome to the stage NVIDIA Founder and CEO, Jensen Huang.

Welcome to GTC. I hope you realize this is not a concert. You have arrived at a developers' conference. There will be a lot of science described, algorithms, computer architecture, mathematics. I sense a very heavy weight in the room all of a sudden, almost that like you were in the wrong place. No conference in the world is there a greater assembly of researchers from such diverse fields of science, from climate tech to radio sciences, trying to figure out how to use AI to robotically control MIMOs for next-generation 6G radios, robotic self-driving cars, even artificial intelligence. Even artificial intelligence. Everybody's -- first, I noticed a sense of relief there all of a sudden. Also, this conference is represented by some amazing companies. This list, this is not the attendees. These are the presenters. And what's amazing is this: if you take away all of my friends, close friends -- Michael Dell is sitting right there, in the IT industry, all of the friends I grew up with in the industry, if you take away that list, this is what's amazing. These are the presenters of the non-IT industries using accelerated computing to solve problems that normal computers can't. It's represented in life sciences, health care, genomics, transportation, of course, retail, logistics, manufacturing, industrial. The gamut of industries represented is truly amazing. And you're not here to attend only, you're here to present to talk about your research. $100 trillion of the world's industries is represented in this room today. This is absolutely amazing. There is absolutely something happening. There is something going on. The industry is being transformed, not just ours, because the computer industry, the computer is the single most important instrument of society today. Fundamental transformations and computing affects every industry. But how did we start? How did we get here? I made a little cartoon for you, literally, I drew this. In one page, this is NVIDIA's journey, started in 1993. This might be the rest of the talk. 1993, this is our journey. We were founded in 1993. There are several important events that happened along the way. I'll just highlight a few. In 2006, CUDA, which has turned out to have been a revolutionary computing model, we thought it was revolutionary then. It was going to be an overnight success. And almost 20 years later, it happened. We saw it coming 2 decades later. In 2012, AlexNet, AI and CUDA made first contact. In 2016, recognizing the importance of this computing model, we invented a brand-new type of computer. We called it DGX-1, 170 teraflops in this supercomputer, 8 GPUs connected together for the very first time. I hand delivered the very first DGX-1 to a start-up located in San Francisco called OpenAI. DGX-1 was the world's first AI supercomputer. Remember, 170 teraflops. 2017, the Transformer arrived. 2022, ChatGPT captured the world's imaginations, helped people realize the importance and the capabilities of artificial intelligence. And 2023, generative AI emerged and a new industry begins. Why? Why is a new industry? Because the software never existed before. We are now producing software, using computers to write software. Producing software that never existed before. It is a brand-new category. It took share from nothing. It's a brand-new category. And the way you produce the software is unlike anything we've ever done before. In data centers, generating tokens, producing, floating point numbers at very large scale. As if in the beginning of this last industrial revolution when people realized that you would set up factories, apply energy to it and this invisible valuable thing called electricity came out, AC generators. And 100 years later, 200 years later, we are now creating new types of electrons, tokens, using infrastructure we call factories, AI factories, to generate this new incredibly valuable thing called artificial intelligence. A new industry has emerged. Well, we're going to talk about many things about this new industry. We're going to talk about how we're going to do computing next. We're going to talk about the type of software that you build because of this new industry, the new software, how you would think about this new software. What about applications in this new industry? And then maybe what's next and how can we start preparing today for what is about to come next? Well, but before I start, I want to show you the soul of NVIDIA, the soul of our company. At the intersection of computer graphics, physics and artificial intelligence, all intersecting inside a computer, in Omniverse, in a virtual world simulation. Everything we're going to show you today, literally, everything we're going to show you today is a simulation, not animation. It's only beautiful because it's physics. The world is beautiful. It's only amazing because it's being animated with robotics. It's being animated with artificial intelligence. What you're about to see all day is completely generated, completely simulated in Omniverse. And all of it, what you're about to enjoy is the world's first concert where everything is homemade. Everything is homemade. You're about to watch some home videos. So sit back and enjoy yourself. [Presentation]

God, I love NVIDIA. Accelerated computing has reached the tipping point. General-purpose computing has ran out of steam. We need another way of doing computing so that we can continue to scale, so that we can continue to drive down the cost of computing, so that we can continue to consume more and more computing while being sustainable. Accelerated computing is a dramatic speed up over general purpose computing. And in every single industry we engage, and I'll show you many, the impact is dramatic. But in no industry is it more important than our own, the industry of using simulation tools to create products. In this industry, it is not about driving down the cost of computing. It's about driving up the scale of computing. We would like to be able to simulate the entire product that we do completely in full fidelity, completely digitally in essentially what we call digital twins. We would like to design it, build it, simulate it, operate it completely digitally. In order to do that, we need to accelerate an entire industry. And today, I would like to announce that we have some partners who are joining us in this journey to accelerate their entire ecosystem so that we can bring the world into accelerated computing. But there's a bonus. When you become accelerated, your infrastructure is CUDA GPUs. And when that happens, it's exactly the same infrastructure for generative AI. And so I'm just delighted to announce several very important partnerships. They're some of the most important companies in the world. ANSYS, does engineering simulation for what the world makes. We're partnering with them to CUDA accelerate the ANSYS ecosystem, to connect ANSYS to the Omniverse digital twin. Incredible. The thing that's really great is that the installed base of NVIDIA GPU accelerated systems are all over the world: in every cloud, in every system, all over enterprises. And so the applications they accelerate will have a giant installed base to go serve. End users will have amazing applications. And of course, system makers and CSPs will have great customer demand. Synopsys. Synopsys is NVIDIA's literally first software partner. They were there in the very first day of our company. Synopsys revolutionized the chip industry with high-level design. We are going to CUDA accelerate Synopsys. We're accelerating computational lithography, one of the most important applications that nobody's ever known about. In order to make chips, we have to push lithography to a limit. NVIDIA has created a library, a domain-specific library, that accelerates computational lithography incredibly. Once we can accelerate and software define all of TSMC, who is announcing today that they're going to go into production with NVIDIA cuLitho. Once it's software-defined and accelerated, the next step is to apply generative AI to the future of semiconductor manufacturing, pushing geometry even further. Cadence builds the world's essential EDA and SDA tools. We also use Cadence. Between these 3 companies: ANSYS, Synopsys and Cadence, we basically build NVIDIA. Together, we are CUDA accelerating cadence. They're also building a supercomputer out of NVIDIA GPUs so that their customers could do fluid dynamic simulation at [ 100,000 ] time scale. Basically a wind tunnel in real time. Cadence Millennium, a supercomputer with NVIDIA GPUs inside. A software company building supercomputers, I love seeing that. Building Cadence copilots together. Imagine a day when Cadence could -- Synopsys, ANSYS tool providers would offer you AI copilots so that we have thousands and thousands of copilot assistance helping us design chips, design systems. And we're also going to connect Cadence digital twin platform to Omniverse. As you could see the trend here, we're accelerating the world's CAE, EDA and SDA so that we could create our future in digital twins. And we're going to connect them all to Omniverse, the fundamental operating system for future digital twins. One of the industries that benefited tremendously from scale, and you all know this one very well, large language models. Basically, after the Transformer was invented, we were able to scale large language models at incredible rates, effectively doubling every 6 months. Now how is it possible that by doubling every 6 months, that we have grown the industry, we have grown the computational requirements so far? And the reason for that is quite simply this: if you double the size of the model, you double the size of your brain, you need twice as much information to go fill it. And so every time you double your parameter count, you also have to appropriately increase your training token count. The combination of those 2 numbers becomes the computation scale you have to support. The latest, the state-of-the-art OpenAI model is approximately 1.8 trillion parameters. 1.8 trillion parameters required several trillion tokens to go train. So a few trillion parameters on the order, of a few trillion tokens on the order of, when you multiply the two of them together, approximately 30 billion, 40 billion , 50 billion, quadrillion floating point operations per second. Now we just have to do some CEO math right now, just hang with me. So you have 30 billion quadrillion. A quadrillion is like a peta. And so if you had a petaflop GPU, you would need 30 billion seconds to go compute, to go train that model. 30 billion seconds is approximately 1,000 years. Well, 1,000 years, it's worth it. I'd like to do it sooner but it's worth it, which is usually my answer when most people tell me, hey, how long is it going to take to do something? So we got 20 years? I'd say, it's worth it. But can we do it next week? And so 1,000 years, 1,000 years. So what we need are bigger GPUs. We need much, much bigger GPUs. We recognized this early on. And we realized that the answer is to put a whole bunch of GPUs together. And of course, innovate a whole bunch of things along the way like inventing Tensor Cores, advancing NVLink so that we could create essentially virtually giant GPUs and connecting them all together with amazing networks from a company called Mellanox, InfiniBand so that we could create these giant systems. And so DGX-1 was our first version, but it wasn't the last. We build supercomputers all the way, all along the way. In 2021, we had Selene 4500 GPUs or so. And then in 2023, we built one of the largest AI supercomputers in the world. It's just come online, Eos. And as we're building these things, we're trying to help the world build these things. And in order to help the world build these things, we've got to build them first. We build the chips, the systems, the networking, all of the software necessary to do this. You should see these systems. Imagine writing a piece of software that runs across the entire system, distributing the computation across thousands of GPUs, but inside are thousands of smaller GPUs. Millions of GPUs to distribute work across all of that and to balance the workload so that you can get the most energy efficiency, the best computation time, keep your cost down. And so those fundamental innovations is what got us here. And here we are, as we see the miracle of ChatGPT emerge in front of us, we also realize we have a long ways to go. We need even larger models. We're going to train it with multi-modality data, not just text on the Internet, but we're going to train it on text and images and graphs and charts. And just as we learn, watching TV, and so there's going to be a whole bunch of watching video so that these models can be grounded in physics, understands that an arm doesn't go through a wall. And so these models would have common sense by watching a lot of the world's video combined with a lot of the world's languages. They'll use things like synthetic data generation, just as you and I do, when we try to learn, we might use our imagination to simulate how it's going to end up, just as I did when I was preparing for this keynote. I was simulating it all along the way. I hope it's going to turn out as well as I had it in my head. As I was simulating how this keynote was going to turn out, somebody did say that another performer did her performance completely on a treadmill so that she could be in shape to deliver it with full energy, I didn't do that. If I get a low wind at about 10 minutes into this, you know what happened. And so where were we? We're sitting here using synthetic data generation. We're going to use reinforcement learning. We're going to practice it in our mind. We're going to have AI working with AI, training each other, just like student-teacher debaters. All of that is going to increase the size of our model, it's going to increase the amount of data that we have. And we're going to have to build even bigger GPUs. Hopper is fantastic. But we need bigger GPUs. And so ladies and gentlemen, I would like to introduce you to a very, very big GPU named Dr. David Blackwell, mathematician, game theorist, probability. We thought it was a perfect name, Blackwell. Ladies and gentlemen, enjoy this. [Presentation]

Blackwell is not a chip. Blackwell is the name of a platform. People think we make GPUs, and we do, but GPUs don't look the way they used to. Here's the, if you will, the heart of the Blackwell system, and this inside the company is not called Blackwell's, it's just a number. And this, this is Blackwell sitting next to, oh, this is the most advanced GPU in the world in production today. This is Hopper. This is Hopper. Hopper changed the world. This is Blackwell. It's okay, Hopper. You're very good. Good boy or good girl. 208 billion transistors and so you could see -- I can see that there's a small line between 2 dies. This is the first time 2 dies have abutted like this together in such a way that the 2 dies think it's 1 chip. There's 10 terabytes of data between it, 10 terabytes per second so that these 2 sides of the Blackwell chip have no clue which side they're on. There's no memory locality issues, no cache issues. It's just 1 giant chip. And so when we were told that Blackwell's ambitions were beyond the limits of physics, the engineer said, "So what?' And so this is what happened. And so this is the Blackwell chip, and it goes into 2 types of systems. The first one is for, fit, function compatible to Hopper. And so you slide out Hopper and you push in Blackwell, that's the reason why one of the challenges of ramping is going to be so efficient. There are installations of Hoppers all over the world and they could be the same infrastructure, same design. The power, the electricity, the thermals, the software, identical, push it right back. And so this is a Hopper version for the current HGX configuration. And this is what the -- the second Hopper looks like this. Now this is a prototype board. And [ Janine ], could I just borrow? Ladies and gentlemen, [ Janine Paul ]. And so this is a fully functioning board and I'll just be careful here. This right here is, I don't know, $10 billion. The second one is $5 billion. It gets cheaper after that. So any customers in the audience, it's okay. All right. But this is -- this one is quite expensive. This is to bring-up board. And the way it's going to go to production is like this one here, okay? And so you're going to take this, it has 2 Blackwell chips and 4 Blackwell dies connected to a Grace CPU. The Grace CPU has a super-fast chip-to-chip link. What's amazing is this computer is the first of its kind where this much computation, first of all, fits into this small of a place. Second, it's memory coherent. They feel like they're just one big happy family working on one application together. And so everything is coherent within it. Just the amount of -- you saw the numbers. There's a lot of terabytes this and terabytes that. But this is a miracle. This, let's see, what are some of the things on here. There's NVLink on top. PCI Express on the bottom on your -- which one is my and your left. One of them, it doesn't matter. One of them is a CPU chip-to-chip link. It's my left or your, depending on which side. I was trying to sort that out and I just kind -- it doesn't matter. Hopefully, it comes plugged in. Okay, so this is the Grace Blackwell system. But there's more. So it turns out all of the specs is fantastic, but we need a whole lot of new features in order to push the limits beyond, if you will, the limits of physics. We would like to always get a lot more x factors. And so one the things that we did was we invented another Transformer engine. Another Transformer engine, the second generation. It has the ability to dynamically and automatically rescale and recast numerical formats to a lower precision whenever it can. Remember, artificial intelligence is about probability. And so you kind of have 1.7 -- approximately 1.7x, approximately 1.4 to be approximately something else. Does that make sense? And so the ability for the mathematics to retain the precision and the range necessary in that particular stage of the pipeline, super important. And so this is -- it's not just about the fact that we designed a smaller ALU, it's not -- the world is not quite that simple. You've got to figure out when you can use that across a computation that is thousands of GPUs. It's running for weeks and weeks and weeks and you want to make sure that the training job is going to converge. And so this new Transformer engine. We have a fifth generation NVLink. It's now twice as fast as Hopper, but very importantly, it has computation in the network. And the reason for that is because when you have so many different GPUs working together, we have to share our information with each other. We have to synchronize and update each other. And every so often, we have to reduce the partial products and then rebroadcast out the partial products that -- some of the partial products back to everybody else. And so there's a lot of what is called AllReduce and all2all and AllGather, it's all part of this area of synchronization and collectives so that we can have GPUs working with each other. Having extraordinarily fast lengths and being able to do mathematics right in the network allows us to essentially amplify even further. So even though it's 1.8 terabytes per second, it's effectively higher than that. And so it's many times that of Hopper. The likelihood of a supercomputer running for weeks on end approximately 0 and the reason for that is because there are many components working at the same time. The statistic -- the probability of them working continuously is very low. And so we need to make sure that whenever there is a -- well, we check point and restart as often as we can, but if we have the ability to detect a weak chip or a weak node early, we can retire it and maybe swap in another processor. That ability to keep the utilization of the supercomputer high, especially when you just spent $2 billion building it, is super important. And so we put in a RAS engine, a reliability engine, that does 100% self-test in system test of every single gate, every single bit of memory on the Blackwell chip and all the memory doesn't connect to it. It's almost as if we ship with every single chip its own advanced tester that we test our chips with. This is the first time we're doing this, super excited about it. Secure AI. Only this conference do they clap for RAS. The secure AI. Obviously, you've just spent hundreds of millions of dollars creating a very important AI. And the code, the intelligence of that AI is encoded in the parameters. You want to make sure that on the one hand, you don't lose it, on the other hand, it doesn't get contaminated. And so we now have the ability to encrypt data, of course, at rest, but also in transit and while it's being computed. It's all encrypted. And so we now have the ability to encrypt in transmission. And when we're computing it, it is in a trusted environment, trusted engine environment. And the last thing is decompression. Moving data in and out of these nodes when the compute is so fast becomes really essential. And so we've put in a high line speed compression engine and it effectively moves data 20x faster in and out of these computers. These computers are so powerful and there's such a large investment that the last thing we want to do is have them be idle. And so all of these capabilities are intended to keep Blackwell fed and as busy as possible. Overall, compared to Hopper, it is 2.5x, 2.5x the FP8 performance per training -- per chip. It is -- it also has this new format called FP6 so that even though the computation speed is the same, the bandwidth that's amplified because of the memory, the amount of parameters you can store in the memory is now amplified. FP4 effectively doubles the throughput. This is vitally important for inference. One of the things that is becoming very clear is that whenever you use a computer with AI on the other side, when you're chatting with the chatbot, when you're asking it to review or make an image. Remember, in the back is a GPU generating tokens. Some people call it inference, but it's more appropriately generation. The way that computing has done in the past was retrieval. You would grab your phone, you would touch something. Some signals go off. Basically, an e-mail goes off to some storage somewhere. There's prerecorded content, somebody wrote a story or somebody made an image or somebody recorded a video. That prerecorded content is then streamed back to the phone and recomposed in a way based on a recommender system to present the information to you. You know that in the future the vast majority of that content will not be retrieved. And the reason for that is because that was prerecorded by somebody who doesn't understand the context, which is the reason why we have to retrieve so much content. If you can be working with an AI that understands the context, who you are, for what reason you're fetching this information, and produces the information for you just the way you like it, the amount of energy we save, the amount of networking bandwidth we save, the amount of waste of time we save will be tremendous. The future is generative, which is the reason why we call it generative AI, which is the reason why this is a brand new industry. The way we compute is fundamentally different. We created a processor for the generative AI era. And one of the most important parts of it is content token generation. We call it -- this format is FP4. Well, that's a lot of computation. 5x the token generation, 5x the inference capability of Hopper. Seems like enough. But why stop there? The answer is it's not enough and I'm going to show you why. I'm going to show you why. And so we would like to have a bigger GPU, even bigger than this one. And so we decided to scale it and notice, but first, let me just tell you how we've scaled. Over the course of the last 8 years, we've increased computation by 1,000 times, 8 years, 1,000 times. Remember, back in the good old days of Moore's Law, it was 2x -- well, 5x every -- 10x every 5 years, that's the easiest math. 10x every 5 years, 100 times every 10 years, 100 times every 10 years at the -- in the middle and the heydays of the PC revolution. 100 times every 10 years. In the last 8 years, we've gone 1,000 times. We have 2 more years to go. And so that puts it in perspective. The rate at which we're advancing computing is insane. And it's still not fast enough, so we built another chip. This chip is just an incredible chip. We call it the NVLink Switch. it's 50 billion transistors. It's almost a size of Hopper all by itself. This switch chip has 4 NVLinks in it, each 1.8 terabytes per second and it has computation in, as I had mentioned. What is this chip for? If we were to build such a chip, we can have every single GPU talk to every other GPU at full speed at the same time. That's insane. It doesn't even make sense. But if you could do that, if you can find a way to do that and build a system to do that, that's cost effective. That's cost effective. How incredible would it be that we could have all these GPUs connect over a coherent link so that they effectively are one giant GPU. Well, one of the great inventions in order to make it cost effective is that this chip has to drive copper directly. The SerDes of this chip is just a phenomenal invention so that we could do direct drive to copper. And as a result, you can build a system that looks like this. Now this system is kind of insane. This is 1-DGX. This is what a DGX looks like now. Remember, just 6 years ago, it was pretty heavy, but I was able to lift it. I delivered the first DGX-1 to OpenAI and the researchers there, it's on -- the pictures are on the Internet and we all autographed it. And if you come to my office, it's autographed there, it's really beautiful. But you can lift it. But this DGX, this DGX -- that DGX, by the way, was 170 teraflops. If you're not familiar with the numbering system, that's 0.17 petaflops. So this is 720. The first one I delivered to OpenAI was 0.17. You can round it up to 0.2, won't make any difference. But -- and by then, it was like, wow, 30 more teraflops. And so this is now 720 petaflops, almost an exaflop for training and the world's first 1 exaflops machine in 1 rack. Just so you know, there are only a couple of 2, 3 exaflops machines on the planet as we speak. And so this is an exaflops AI system in one single rack. Well, let's take a look at the back of it. So this is what makes it possible. That's the back, the DGX NVLink Spine. 130 terabytes per second goes to the back of that chassis. That is more than the aggregate bandwidth of the Internet. So we could basically send everything to everybody within a second. And so we have 5,000 cables, 5,000 NVLink cables. In total, 2 miles. Now this is the amazing thing. If we had to use optics, we would have had to use transceivers and retimers. And those transceivers and retimers alone would have cost 20,000 watts. Two kilowatts of just transceivers alone just to drive the NVLink spine. As a result, we did it completely for free over NVLink Switch and we were able to save the 20 kilowatts for computation. This entire rack is 120 kilowatts. So that 20 kilowatts makes a huge difference. It's liquid cooled. What goes in is 25 degrees C, about room temperature. What comes out is 45 degrees C, about your jacuzzi. So room temperature goes in, jacuzzi comes out, 2 liters per second. We could sell a peripheral. 600,000 parts. Somebody used to say, you guys make GPUs. And we do, but this is what a GPU looks like to me. When somebody says GPU, I see this. Two years ago, when I saw a GPU, it was the HGX. It was 70 pounds, 35,000 parts. Our GPUs now are 600,000 parts and 3,000 pounds. 3,000 pounds. 3,000 pounds, that's kind of like the weight of a carbon fiber Ferrari. I don't know if that's a useful metric, but everybody is going, I feel it. I feel it. I get it. I get that. Now that you mentioned that, I feel it. I don't know what's 3,000 pounds. Okay. So 3,000 pounds, 1.5 tons. So it's not quite an elephant. So this is what a DGX looks like. Now let's see what it looks like in operation, okay? Let's imagine what is -- how do we put this to work and what does that mean? Well, if you were to train a GPT model, 1.8 trillion parameter model, it took about -- apparently about 3 to 5 months or so with 25,000 amperes. If we were to do with Hopper, it would probably take something like 8,000 GPUs and it will consume 15 megawatts. 8,000 GPUs and 15 megawatts, it would take 90 days, about 3 months. And that would allow you to train something that is this groundbreaking AI model. And this is obviously not as expensive as anybody would think, but it's 8,000 GPUs. It's still a lot of money. And so 8,000 GPUs, 15 megawatts. If you were to use Blackwell to do this, it would only take 2,000 GPUs. 2,000 GPUs, same 90 days. But this is the amazing part, only 4 megawatts of power. So from 15 -- yes, that's right. And that's our goal. Our goal is to continuously drive down the cost and the energy, they're directly proportional to each other, cost and energy associated with the computing so that we can continue to expand and scale up the computation that we have to do to train the next-generation models. Well, this is training. Inference or generation is vitally important going forward. Probably some half of the time that NVIDIA GPUs are in the cloud these days, it's being used for token generation. They're either doing copilot this or ChatGPT that or all these different models that are being used when you're interacting with it or generating images or generating videos, generating proteins, generating chemicals. There's a bunch of generation going on. All of that is in the category of computing we call inference. But inference is extremely hard for large language models because these large language models have several properties. One, they're very large and so it doesn't fit on 1 GPU. This is -- imagine Excel doesn't fit on 1 GPU. And imagine some application you're running on a daily basis doesn't fit on 1 computer, like a video game doesn't fit on 1 computer. And most, in fact, do. And many times in the past, in hyperscale computing, many applications for many people fit on the same computer. And now all of a sudden, there's 1 inference application where you're interacting with this chatbot. That chatbot requires a supercomputer in the back to run it. And that's the future. The future is generative with these chatbots and these chatbots are trillions of tokens, trillions of parameters and they have to generate tokens at interactive rates. Now what does that mean? Well, 3 tokens is about a word. The space, the final frontier, these are the adventures. That's like 80 tokens, okay? I don't know if that's useful to you. And so the art of communications is selecting good analogies. Yes, this is not going well. I don't know what he's talking about. Never seen Star Trek. And so here we are, we're trying to generate these tokens. When you're interacting with it, you're hoping that the tokens come back to you as quickly as possible and it's as quick as you could read it. And so the ability for generation token is really important. You have to paralyze the work of this model across many, many GPUs so that you could achieve several things. On the one hand, you would like throughput because that throughput reduces the cost -- the overall cost per token of generating. So your throughput dictates the cost of delivering the service. On the other hand, you have another interactive rate, which is another tokens per second where it's about per user, and that has everything to do with quality of service. And so these 2 things compete against each other. And we have to find a way to distribute work across all of these different GPUs and paralyze it in a way that allows us to achieve both. And it turns out the search space is enormous. I told you there's going to be math involved. And everybody is going, "Oh, dear." I heard some gasps just now when I put up that slide. So this right here, the Y-axis is tokens per second, data center throughput. The X-axis as tokens per second, interactivity of the person. And notice the upper right is the best. You want interactivity to be very high, number of tokens per second per user. You want the tokens per second of per data center to be very high. The upper right is terrific. However, it's very hard to do that. And in order for us to search for the best answer across every single one of those intersections X, Y coordinates, okay? If you just look at every single X, Y coordinate, all those blue dots came from some repartitioning of the software. Some optimizing solution has to go and figure out whether to use tensor parallel, expert parallel, pipeline parallel or data parallel and distribute this enormous model across all these different GPUs and sustain the performance that you need. This exploration space would be impossible if not for the programmability of NVIDIA's GPUs. And so we could -- because of CUDA, because we have such a rich ecosystem, we can explore this universe and find that green roof line. It turns out that green roof line, notice you got TP2.EP8.DP4. It means 2 parallel -- 2 tensor parallel -- tensor parallel across 2 GPUs, expert parallels across 8, data parallel across 4. Notice on the other end, you've got tensor parallel across 4 and expert parallel across 16. The configuration, the distribution of that software, it's a different, different run time that would produce these different results and you have to go discover that roof line. Well, that's just one model. And this is just one configuration of a computer. Imagine all of the models being created around the world and all the different configurations of systems that are going to be available. So now that you understand the basics, let's take a look at inference of Blackwell compared to Hopper. And this is the extraordinary thing. In one generation because we created a system that's designed for trillion parameter generative AI, the inference capability of Blackwell is off the charts. And in fact, it is some 30x Hopper, yes. For large language models like ChatGPT and others like it, the blue line is Hopper. I gave you -- imagine, we didn't change the architecture of Hopper and we just made it a bigger chip. We just used the latest, the greatest 10 terabytes per second. We connected the 2 chips together. We got this giant 208 billion parameter chip. How would we have performed if nothing else changed? And it turns out quite wonderfully. Quite wonderfully, and that's the purple line, but not as great as it could be. And that's where the FP4 Tensor Core, the new Transformer engine, and very importantly, the NVLink Switch. And the reason for that is because all these GPUs have to share the results, partial products. Whenever they do all2all, AllGather, whenever they communicate with each other, that NVLink Switch is communicating almost 10x faster than what we could do in the past using the fastest networks, okay? So Blackwell is going to be just an amazing system for generative AI. And in the future, data centers are going to be thought of, as I mentioned earlier, as an AI factory. An AI factory's goal in life is to generate revenues. Generate, in this case, intelligence in this facility. Not generating electricity as in AC generators, but of the last industrial revolution and this industrial revolution, the generation of intelligence. And so this ability is super, super important. The excitement of Blackwell is really off the charts. When we first -- this is 1.5 years ago, 2 years ago, I guess, 2 years ago, when we first started to go to market with Hopper, we had the benefit of 2 CSPs joined us in launch and we were delighted. And so we had 2 customers. We have more now. Unbelievable excitement for Blackwell, unbelievable excitement. And there's a whole bunch of different configurations. Of course, I showed you the configurations that slide into the Hopper form factor, so that's easy to upgrade. I showed you examples that are liquid cooled, that are the extreme versions of it, one entire rack that's connected by NVLink 672. We're going to -- Blackwell is going to be ramping to the world's AI companies, of which there are so many now doing amazing work in different modalities. The CSPs, every CSP is geared up. All the OEMs and ODMs, regional clouds, sovereign AIs and telcos all over the world are signing up to launch with Blackwell. Blackwell would be the most successful product launch in our history, and so I can't wait to see that. I want to thank some partners that are joining us in this. AWS is gearing up for Blackwell. They're going to build the first GPU with secure AI. They're building out a 222 exaflops system. Just now when we animate it just now the digital twin, if you saw the -- all of those clusters are coming down. By the way, that is not just art. That is a digital twin of what we're building. That's how big it's going to be. Besides infrastructure, we're doing a lot of things together with AWS. We're CUDA accelerating SageMaker AI. We're CUDA accelerating Bedrock AI. Amazon Robotics is working with us using NVIDIA Omniverse and Isaac Sim. AWS Health has NVIDIA health integrated into it. So AWS has really leaned into accelerated computing. Google is gearing up for Blackwell. GCP already has A100s, H100s, T4s, L4s, a whole fleet of NVIDIA CUDA GPUs, and they recently announced the Gemma model that runs across all of it. We're working to optimize and accelerate every aspect of GCP. We're accelerating data product, for data processing -- their data processing engine, JAX, XLA, Vertex AI and MuJoCo for robotics. So we're working with Google and GCP across a whole bunch of initiatives. Oracle is gearing up for Blackwell. Oracle is a great partner of ours for NVIDIA DGX Cloud. And we're also working together to accelerate something that's really important to a lot of companies, Oracle database. Microsoft is accelerating, and Microsoft is gearing up for Blackwell. Microsoft-NVIDIA has a wide-ranging partnership. We're accelerating CUDA, accelerating all kinds of services when you chat obviously and AI services that are in Microsoft Azure. It's very, very likely NVIDIA is in the back, doing the inference and the token generation. We built -- they built the largest NVIDIA InfiniBand supercomputer, basically, a digital twin of ours or a physical twin of ours. We're bringing the NVIDIA ecosystem to Azure, NVIDIA DGX Cloud to Azure, NVIDIA Omniverse is now hosted in Azure. NVIDIA Healthcare is in Azure. And all of it is deeply integrated and deeply connected with Microsoft Fabric. The whole industry is gearing up for Blackwell. This is -- what I'm about to show you, most of the scenes that you've seen so far of Blackwell are the full fidelity design of Blackwell. Everything in our company has a digital twin. And in fact, this digital twin idea is really spreading. And it helps companies build very complicated things perfectly the first time. And what could be more exciting than creating a digital twin to build a computer that was built in a digital twin? And so let me show you what Wistron is doing. [Presentation]

That's how we -- that's the way it's going to be in the future. We're going to manufacturing everything digitally first, and then we'll manufacture it physically. People ask me, how did it start? What got you guys so excited? What was it that you saw that caused you to put it all in on this incredible idea? And it's this. Hang on a second. Guys, that was going to be such a moment. That's what happens when you don't rehearse. This, as you know, was first contact, 2012, AlexNet. You put a cat into this computer, and it comes out and it says, cat. And we said, oh my God, this is going to change everything. You take 1 million numbers across 3 channels, RGB. These numbers make no sense to anybody. You put it into the software, and it compressed -- it dimensionally reduced it. It reduces it from 1 million dimensions, 1 million dimensions. It turns it into 3 letters, 1 vector, 1 number. And it's generalized. You could have the cat be different cats, and you could have it be the front of the cat and the back of the cat. And you look at this thing, you say, unbelievable. You mean any cats? Yes, any cat. And it was able to recognize all these cats. And we realized how it did it, systematically, structurally. It's scalable. How big can you make it? Well, how big do you want to make it? And so we imagined that this is a completely new way of writing software. And now today, as you know, you can have -- you type in the word C-A-T, and what comes out is a cat. It went the other way. Am I right? Unbelievable. How is it possible? That's right. How is it possible? You took 3 letters, and you generated 1 million pixels from it, and it makes sense. Well, that's the miracle. And here we are, just literally 10 years later, 10 years later, where we recognize texts, we recognize images, we recognize videos and sounds and images. Not only do we recognize them, we understand their meaning. We understand the meaning of the text. That's the reason why I can chat with you. It can summarize for you. It understands the text. It understood -- not just recognizes the English, it understood the English. It doesn't just recognize the pixels. It understood the pixels. And you can even condition it between 2 modalities. You can have language condition image and generate all kinds of interesting things. Well, if you can understand these things, what else can you understand that you digitized? The reason why we started with text and images is because we digitize those. But what else have we digitized? Well, it turns out we digitized a lot of things, proteins and genes and brain waves. Anything you can digitize. So long as they're structured, we can probably learn some patterns from it. And if we can learn the patterns from it, we can understand its meaning. If we can understand its meaning, we might be able to generate it as well. And so therefore, the generative AI revolution is here. Well, what else can we generate? What else can we learn? Well, one of the things that we would love to learn is -- we would love to learn climate. We would love to learn extreme weather. We would love to learn what -- how we can predict future weather at regional scales at sufficiently high resolution such that we can keep people out of harm's way before harm comes. Extreme weather costs the world $150 billion, surely more than that. It's not evenly distributed. $150 billion is concentrated in some parts of the world and, of course, to some people of the world. We need to adapt, and we need to know what's coming. And so we are creating Earth-2, a digital twin of the Earth for predicting weather, and we've made an extraordinary invention called [ CoreDiff ], the ability to use generative AI to predict weather at extremely high resolution. Let's take a look. [Presentation]

The Weather Company is the trusted source of global weather prediction. We are working together to accelerate their weather simulation, first principled base of simulation. However, they're also going to integrate Earth-2 [ CoreDiff ] so that they could help businesses and countries do regional high-resolution weather prediction. And so if you have some weather prediction you'd like to know, you'd like to do, reach out to The Weather Company. Really exciting work. NVIDIA Healthcare. Something we started 15 years ago. We're super, super excited about this. This is an area where we're very, very proud. Whether it's medical imaging or it's gene sequencing or computational chemistry, it is very likely that NVIDIA is the computation behind it. We've done so much work in this area. Today, we're announcing that we're going to do something really, really cool. Imagine all of these AI models that are being used to generate images and audio, but instead of images and audio, because it understood images and audio, all the digitalization that we've done for genes and proteins and amino acids, that digitization capability is now passed through machine learning so that we understand the language of life. The ability to understand the language of life, of course, we saw the first evidence of it with AlphaFold. This is really quite an extraordinary thing. After decades of painstaking work, the world had only digitized and reconstructed using cryo electron microscopy or crystal x-ray crystallography. These different techniques painstakingly reconstructed the protein, 200,000 of them, in just -- what is it, less than a year or so? AlphaFold has reconstructed 200 million proteins, basically every protein, every living thing that's ever been sequenced. This is completely revolutionary. Well, those models are incredibly hard to use for -- incredibly hard for people to build. And so what we're going to do is we're going to build them. We're going to build them for the researchers around the world. And it won't be the only one. There will be many other models that we create. And so let me show you what we're going to do with it. [Presentation]

NVIDIA MolMIM, MolMIM, [ CoreDiff ], there's a whole bunch of other models, a whole bunch of other models, computer vision models, robotics models and even, of course, some really, really terrific open-source language models. These models are groundbreaking. However, it's hard for companies to use. How would you use it? How would you bring it into your company and integrate it into your workflow? How would you package it up and run it? Remember, earlier, I just said that inference is an extraordinary computation problem. How would you do the optimization for each and every one of these models and put together the computing stack necessary to run that supercomputer so that you can run these models in your company? And so we have a great idea. We're going to invent a new way for you to receive and operate software. This software comes basically in a digital box. We call it a container. And we call it the NVIDIA inference microservice, a NIM, and I [ will ] explain to you what it is. A NIM. It's a pretrained model. So it's pretty clever. And it is packaged and optimized to run across NVIDIA's installed base, which is very, very large. What's inside it is incredible. You have all these pretrained state-of-the-art open-source models. They could be open source. They could be from one of our partners. It could be created by us like NVIDIA MolMIM. It is packaged up with all of its dependencies. So CUDA, the right version; cuDNN, the right version; TensorRT-LLM, distributing across the multiple GPUs; Triton Inference Server, all completely packaged together. It's optimized depending on whether you have a single GPU, multi-GPU or multi-node of GPUs. It's optimized for that. And it's connected up with APIs that are simple to use. Now this -- think about what an AI API is. An AI API is an interface that you just talk to. And so this is a piece of software in the future that has a really simple API, and that API is called human. And these packages, incredible bodies of software, will be optimized and packaged, and we'll put it on a website, and you can download it. You can take it with you. You can run it in any cloud. You can run it in your own data center. You can run in work stations if they fit. And all you have to do is come to ai.nvidia.com. We call it NVIDIA inference microservice, but inside the company, we all call it NIMs. Okay. Just imagine, one -- someday, there's going to be one of these chatbots and these chatbots is going to just be in the NIM. And you'll assemble a whole bunch of chatbots. And that's the way software is going to be built someday. How do we build software in the future? It is unlikely that you'll write it from scratch or write a whole bunch of Python code or anything like that. It is very likely that you assemble a team of AIs. There's probably going to be a super AI that you use that takes the mission that you give it and breaks it down into an execution plan. Some of that execution plan could be handed off to another NIM. That NIM would maybe understand SAP. The language of SAP is ABAP. It might understand ServiceNow and go retrieve some information from their platforms. It might then hand that result to another NIM who then goes off and does some calculation on it. Maybe it's an optimization software, a combinatorial optimization algorithm. Maybe it's some just basic calculator. Maybe it's pandas to do some numerical analysis on it. And then it comes back with its answer. And it gets combined with everybody else's. And because it's been presented with, this is what the right answer should look like. It knows what right answers to produce, and it presents it to you. We can get a report every single day at top of the hour that has something to do with a build plan or some forecast or some customer alert or some bugs database or whatever it happens to be, and we could assemble it using all these NIMs. And because these NIMs have been packaged up and ready to work on your systems, so long as you have video GPUs in your data center or in the cloud, these NIMs will work together as a team and do amazing things. And so we decided this is such a great idea. We're going to go do that. And so NVIDIA has NIMs running all over the company. We have chatbots being created all over the place. And one of the most important chatbots, of course, is a chip-designer chatbot. You might not be surprised. We care a lot about building chips. And so we want to build chatbots, AI copilots that are co-designers with our engineers. And so this is the way we did it. So we got ourselves a Llama 2. This is a 70B, and it's packaged up in a NIM. And we asked it, what is a CTL? It turns out, CTL is an internal program, and it has an internal proprietary language, but it thought the CTL was a combinatorial timing logic. And so it describes conventional knowledge of CTL. But that's not very useful to us. And so we gave it a whole bunch of new examples. This is no different than onboarding an employee. We say, thanks for that answer. It's completely wrong. And then we present to them, this is what a CTL is, okay? And so this is what a CTL is at NVIDIA. And the CTL, as you can see, CTL stands for [ compute trace library ], which makes sense. We're tracing compute cycles all the time, and it wrote the program. Isn't that amazing? And so the productivity of our chip designers can go up. This is what you can do with the NIM. First thing you can do with it is customize it. We have a service called NeMo microservice that helps you curate the data, preparing the data so that you could teach this, onboard this AI, you fine-tune them, and then you guardrail it, you can even evaluate the answer, evaluate its performance against other examples. And so that's called the NeMo microservice. Now the thing that's emerging here is this, there are 3 elements, 3 pillars of what we're doing. The first pillar is, of course, inventing the technology for AI models and running AI models and packaging it up for you. The second is to create tools to help you modify it. First is having the AI technology. Second is to help you modify it. And third is infrastructure for you to fine-tune it and, if you like, deploy it. You could deploy it on our infrastructure called DGX Cloud, or you can employ -- deploy it on-prem, or you can deploy it anywhere you like. Once you develop it, it's yours to take anywhere. And so we are effectively an AI foundry. We will do for you and the industry on AI what TSMC does for us building chips. And so we go to it with our -- go to TSMC with our big ideas, they manufacture, and we take it with us. And so exactly the same thing here, AI foundry, and the 3 pillars are the NIMs, NeMo microservice and DGX Cloud. The other thing that you could teach the NIM to do is to understand your proprietary information. Remember, inside our company, the vast majority of our data is not in the cloud. It's inside our company. It's been sitting there being used all the time. And gosh, it's basically NVIDIA's intelligence. We would like to take that data, learn its meaning, like we learned the meaning of almost anything else that we just talked about, learn its meaning and then reindex that knowledge into a new type of database called the vector database. And so you essentially take structured data or unstructured data, you learn its meaning, you encode its meaning. So now this becomes an AI database. And that AI database in the future, once you create it, you can talk to it. And so let me give you an example of what you could do. So suppose you create -- you've got a whole bunch of multi-modality data. And one good example of that is PDF. So you take the PDF, you take all of your PDFs, all of your favorite, the stuff that is proprietary to you, critical to your company. You can encode it just as we encoded pixels with a cat, and it becomes the word cat. We can encode all of your PDF, and it turns into vectors that are now stored inside your vector database. It becomes the proprietary information of your company. And once you have that proprietary information, you can chat to it. It's a smart database. And so you just chat with data. And how much more enjoyable is that? For our software team, they just chat with the bugs database. How many bugs was there last night? Are we making any progress? And then after you're done talking to this bugs database, you need therapy. And so we have another chatbot for you. You can do it. Okay. So we call this NeMo Retriever. And the reason for that is because ultimately, its job is to go retrieve information as quickly as possible. And you just talk to it. Hey, retrieve me this information. It goes "hnnng." It brings it back to you. Do you mean this? You go, yes, perfect, okay? And so we call it the NeMo Retriever. Well, the NeMo service helps you create all these things, and we have all these different NIMs. We even have NIMs of digital humans.

I'm Rachel, your AI care manager.

Okay. So it's a really short clip, but there were so many videos to show you, I guess so -- many other demos to show you. And so I had to cut this one short. But this is Diana. She is a digital human NIM. And you just talk to her. And she's connected, in this case, to Hippocratic AI's large language model for health care, and it's truly amazing. She is just super smart about health care things. And so after my -- Dwight, my VP of Software Engineering, talks to the chatbot for bugs database, then you come over here and talk to Diane (sic) [ Diana ]. And so Diane (sic) [ Diana ] is completely animated with AI, and she's a digital human. There are so many companies that would like to build. They're sitting on gold mines. The enterprise IT industry is sitting on a gold mine. It's a gold mine because they have so much understanding of the way work is done. They have all these amazing tools that have been created over the years, and they're sitting on a lot of data. If they could take that gold mine and turn them into copilots, these copilots could help us do things. And so just about every IT franchise, IT platform in the world, that has valuable tools that people use is sitting on a gold mine for copilots, and they would like to build their own copilots and their own chatbots. And so we're announcing that NVIDIA AI foundry is working with some of the world's great companies. SAP generates 87% of the world's global commerce. Basically, the world runs on SAP. We run on SAP. NVIDIA and SAP are building SAP Joule copilots using NVIDIA NeMo and DGX Cloud. ServiceNow, they run 85% of the world's Fortune 500 companies, run their people and customer service operations on ServiceNow. And they're using NVIDIA AI foundry to build ServiceNow Assist virtual assistants. Cohesity backs up the world's data. They're sitting on a gold mine of data. Hundreds of exabytes of data, over 10,000 companies. NVIDIA AI foundry is working with them, helping them build their Gaia generative-AI agent. Snowflake is a company that stores the world's digital warehouse in a cloud and serves over 3 billion queries a day for 10,000 enterprise customers. Snowflake is working with NVIDIA AI foundry to build copilots with NVIDIA NeMo and NIMs. NetApp, nearly half of the files in the world are stored on-prem on NetApp. NVIDIA AI foundry is helping them build chatbots and copilots like those vector databases and retrievers with NVIDIA NeMo and NIMs. And we have a great partnership with Dell. Everybody who is building these chatbots and generative AI, when you're ready to run it, you're going to need an AI factory. And nobody is better at building end-to-end systems, a very large scale for the enterprise than Dell. And so anybody, any company, every company will need to build AI factories, and it turns out that Michael is here. He's happy to take your order. Ladies and gentlemen, Michael Dell. Okay. Let's talk about the next wave of robotics -- the next wave of AI robotics, physical AI. So far, all of the AI that we've talked about is one computer. Data comes into one computer, lots of the world, if you will, experience in digital text form. The AI imitates us by reading a lot of the language to predict the next words. It's imitating you by studying all of the patterns and all the other previous examples. Of course, it has to understand context and so on and so forth. But once it understands the context, it's essentially imitating you. We take all of the data. We put it into a system like DGX. We compress it into a large language model. Trillions and trillions of parameters become billions and billion -- trillions of tokens becomes billions of parameters. These billions of parameters becomes your AI. Well, in order for us to go to the next wave of AI where the AI understands the physical world, we're going to need 3 computers. The first computer is still the same computer. It's that AI computer that now is going to be watching video and maybe it's doing synthetic data generation. Maybe there's a lot of human examples. Just as we have human examples in text form, we're going to have human examples in articulation form, and AIs will watch us, understand what is happening and try to adapt it for themselves into the context. And because it can generalize with these foundation models, maybe these robots can also perform in the physical world fairly generally. So I just described, in very simple terms, essentially, what just happened in large language models, except the ChatGPT moment for robotics may be right around the corner. And so we've been building the end-to-end systems for robotics for some time. I'm super, super proud of the work. We have the AI system, DGX. We have the lower system, which is called AGX for autonomous systems, the world's first robotics processor. When we first built this thing, people were, what are you guys building? It's an SoC. It's one chip. It's designed to be very low power but is designed for high-speed sensor processing and AI. And so if you want to run transformers in a car or you want to run transformers in anything that moves, we have the perfect computer for you. It's called Jetson. And so the DGX on top of training the AI, the Jetson is the autonomous processor. And in the middle, we need another computer. Whereas large language models have the benefit of you providing your examples and then doing reinforcement learning human feedback, what is the reinforcement learning human feedback of a robot? Well, it's reinforcement learning physical feedback. That's how you align the robot. That's how you -- that's how the robot knows that as it's learning these articulation capabilities and manipulation capabilities, it's going to adapt properly into the laws of physics. And so we need a simulation engine that represents the world digitally for the robot so that the robot has a gym to go learn how to be a robot. We call that virtual world Omniverse. And the computer that runs Omniverse is called OVX. And OVX, the computer itself is hosted in the Azure cloud, okay? And so basically, we built these 3 things, these 3 systems. On top of it, we have algorithms for every single one. Now I'm going to show you one super example of how AI and Omniverse are going to work together. The example I'm going to show you is kind of insane, but it's going to be very, very close to tomorrow. It's a robotics building. This robotics building is called a warehouse. Inside the robotics building are going to be some autonomous systems. Some of the autonomous systems are going to be called humans, and some of the autonomous systems are going to be called forklifts. And these autonomous systems are going to interact with each other, of course, autonomously, and it's going to be overlooked upon by this warehouse to keep everybody out of harm's way. The warehouse is essentially an air traffic controller. And whenever it sees something happening, it will redirect traffic and give you waypoints, just new waypoints to the robots and the people, and they'll know exactly what to do. This warehouse, this building, you can also talk to. Of course, you could talk to it. Hey, SAP Center, how are you feeling today, for example. And so you could ask the same -- the warehouse, the same questions. Basically, the system I just described will have Omniverse cloud that's hosting the virtual simulation and AI running on DGX Cloud, and all of this is running in real time. Let's take a look. [Presentation]

Isn't that incredible? And so remember, a future facility warehouse factory building will be software defined. And so the software is running, how else would you test the software? So you test the software, the building, the warehouse, the optimization system in the digital twin. What about all the robots? All of those robots you are seeing just now, they're all running their own autonomous robotic stack. And so the way you integrate software in the future, CI/CD in the future for robotic systems is with digital twins. We've made Omniverse a lot easier to access. We're going to create basically Omniverse cloud APIs, 4 simple API in a channel, and you can connect your application to it. So this is going to be as wonderfully beautifully simple in the future that Omniverse is going to be. And with these APIs, you're going to have these magical digital twin capability. We also have turned Omniverse into an AI and integrated it with the ability to chat USD, the language of -- our language is human, and Omniverse's language, as it turns out, is Universal Scene Description. And so that language is rather complex. And so we've taught our Omniverse that language. And so you can speak to it in English, and it would directly generate USD. And we talked back in USD but converts back to you in English. You could also look for information in this world semantically. Instead of the world being encoded semantically in language, now it's encoded semantically in scenes. And so you could ask it of certain objects or certain conditions and certain scenarios, and it can go and find that scenario for you. It also can collaborate with you in generation. You could design some things in 3D. It could simulate some things in 3D, or you could use AI to generate something in 3D. Let's take a look at how this is all going to work. We have a great partnership with Siemens. Siemens is the world's largest industrial engineering and operations platform. You've seen now so many different companies in the industrial space. Heavy industry is one of the greatest final frontiers of IT, and we finally now have the necessary technology to go and make a real impact. Siemens is building the industrial metaverse. And today, we're announcing that Siemens is connecting their crown jewel, Xcelerator, to NVIDIA Omniverse. Let's take a look. [Presentation]

The professional voice actor happens to be a good friend of mine, Roland Busch, who happens to be the CEO of Siemens. Once you get Omniverse connected into your workflow, your ecosystem, from the beginning of your design to engineering to manufacturing planning, all the way to digital twin operations, once you connect everything together, it's insane how much productivity you can get. And it's just really, really wonderful. All of a sudden, everybody is operating on the same ground truth. You don't have to exchange data and convert data, make mistakes. Everybody is working on the same ground truth, from the design department to the art department, the architecture department, all the way to the engineering and even the marketing department. Let's take a look at how Nissan has integrated Omniverse into their workflow. And it's all because it's connected by all these wonderful tools and these developers that we're working with. Take a look. [Presentation]

That was not an animation. That was Omniverse. Today, we're announcing that Omniverse Cloud streams to the Vision Pro. And it is very, very strange that you walk around virtual doors when I was getting out of that car, and everybody does it. It is really, really quite amazing. Vision Pro connected to Omniverse, portals you into Omniverse, and because all of these CAD tools and all these different design tools are now integrated and connected to Omniverse, you can have this type of workflow, really incredible. Let's talk about robotics. Everything that moves will be robotics. There's no question about that. It's safer. It's more convenient. And one of the largest industries is going to be automotive. We build the robotic stack from top to bottom as I was mentioned, from the computer system, but in the case of self-driving cars, including the self-driving application. At the end of this year, or I guess, beginning of next year, we will be shipping in Mercedes and then, shortly after that JLR. And so these autonomous robotic systems are software-defined. They take a lot of work to do, has computer vision, has obviously artificial intelligence, control and planning, all kinds of very complicated technology and takes years to refine. We're building the entire stack. However, we open up our entire stack for all of the automotive industry. This is just the way we work. The way we work in every single industry, we try to build as much of it as we can so that we understand it, but then we open it up so that everybody can access it, whether you would like to buy just our computer, which is the world's only full functional safe ASIL D system that can run AI. This functional safe ASIL D-quality computer or the operating system on top or, of course, our data centers, which is in basically every AV company in the world. However you would like to enjoy it, we're delighted by it. Today, we're announcing that BYD, the world's largest EV company, is adopting our next generation, it's called Thor. Thor is designed for transformer engines. Thor, our next-generation AV computer, will be used by BYD. You probably don't know this fact that we have over 1 million robotics developers. We created Jetson, this robotics computer. We're so proud of it. The amount of software that goes on top of it is insane. But the reason why we can do it at all is because it's 100% CUDA compatible. Everything that we do, everything that we do in our company, is in service of our developers. And by us being able to maintain this rich ecosystem and make it compatible with everything that you access from us, we can bring all of that incredible capability to this little tiny computer we call Jetson, a robotics computer. We also, today, are announcing this incredibly advanced new SDK. We call it Isaac Perceptor, Isaac Perceptor. Most of the robots today are preprogrammed. They're either following rails on the ground, digital rails, or they'd be following AprilTags. But in the future, they're going to have perception. And the reason why you want that is so that you could easily program it. You say, I would like to go from point A to point B. And it will figure out a way to navigate its way there. So by only programming waypoints, the entire route could be adaptive. The entire environment could be reprogrammed, just as I showed you at the very beginning with the warehouse. You can't do that with preprogrammed AGVs. If those boxes fall down, they just all gum up, and they just wait there for somebody to come clear it. And so now with the Isaac Perceptor, we have incredible state-of-the-art vision odometry, 3D reconstruction, and, in addition to 3D reconstruction, depth perception. The reason for that is so that you can have 2 modalities to keep an eye on what's happening in the world. Isaac Perceptor. The most used robot today is the manipulator, manufacturing arms, and they are also preprogrammed. The computer vision algorithms, the AI algorithms, the control and path planning algorithms that are geometry aware, incredibly computationally intensive. We have made these CUDA accelerated. So we have the world's first CUDA-accelerated motion planner that is geometry aware. You put something in front of it, it comes up with a new plan and articulates around it. It has excellent perception for pose estimation of a 3D object, not just -- not it's posed in 2D, but it's posed in 3D. So it has to imagine what's around and how best to grab it. So the foundation pose, the grip foundation and the articulation algorithms are now available. We call it Isaac Manipulator. And they also just run on NVIDIA's computers. We are starting to do some really great work in the next generation of robotics. The next generation of robotics will likely be a humanoid robotics. We now have the necessary technology. And as I was describing earlier, the necessary technology to imagine generalized human robotics. In a way, human robotics is likely easier. And the reason for that is because we have a lot more imitation training data that we can provide the robots because we are constructed in a very similar way. It is very likely that the humanoid robotics will be much more useful in our world because we created the world to be something that we can interoperate in and work well in. And the way that we set up our workstations and manufacturing and logistics, they were designed for humans. They were designed for people. And so these humanoid robotics will likely be much more productive to deploy. While we're creating just like we're doing with the others, the entire stack, starting from the top, a foundation model that learns from watching video, human examples, it could be in video form, it could be in virtual reality form. We then created a gym for it, called Isaac reinforcement learning gym, which allows the humanoid robot to learn how to adapt to the physical world. And then an incredible computer, the same computer that's going to go into a robotic car, this computer will run inside a humanoid robot called Thor. It's designed for transformer engines. We've combined several of these into one video. This is something that you're going to really love. Take a look. [Presentation]

The soul of NVIDIA, the intersection of computer graphics, physics, artificial intelligence. It all came to bear at this moment. The name of that project in General Robotics 003. I know. Super good, super good. Well, I think we have some special guests. Do we? So I understand you guys are powered by Jetson. They're powered by Jetson. Little Jetson robotics computers inside. They learned to walk in Isaac sim. Ladies and gentlemen, this is Orange, and this is the famous Green. They are the BDX robots of Disney. Amazing Disney research. Come on, you guys. Let's wrap up. Let's go. Five things -- where are you going? I sit right here. Don't be afraid. Come here, Green. Hurry up. What are you saying? No, it's not time to eat. It's not time to eat. I'll give you a snack in a moment. Let me finish up real quick. Come on, Green. Hurry up. Stop wasting time. Five things. First, a new industrial revolution. Every data center should be accelerated. $1 trillion worth of installed data centers will become modernized over the next several years. Second, because of the computational capability we brought to bear, a new way of doing software has emerged, generative AI, which is going to create new infrastructure dedicated to doing one thing and one thing only. Not for multi-user data centers but AI generators. These AI generation will create incredibly valuable software. A new industrial revolution. Second, the computer of this revolution, the computer of this generation, generative AI, trillion parameters, Blackwell. Insane amounts of computers and computing. Third -- I'm trying to concentrate. Good job. Third, new computer creates new types of software. New type of software should be distributed in a new way so that it can, on the one hand, be an endpoint in the cloud and easy to use but still allow you to take it with you because it is your intelligence. Your intelligence should be packaged up in a way that allows you to take it with you. We call them NIMs. And third, these NIMs are going to help you create a new type of application for the future. Not one that you wrote completely from scratch, but you're going to integrate them like teams create these applications. We have a fantastic capability between NIMs, the AI technology, the tools, NeMo and the infrastructure DGX Cloud in our AI foundry to help you create proprietary applications, proprietary chatbots. And then lastly, everything that moves in the future will be robotic. You're not going to be the only one. And these robotic systems, whether they are humanoid, AMRs, self-driving cars, forklifts, manipulating arms, they will all need one thing. Giant stadiums, warehouses, factories, there can be factories that are robotic, orchestrating factories, manufacturing lines that are robotics, building cars that are robotics. These systems all need one thing. They need a platform, a digital platform, a digital twin platform, and we call that Omniverse, the operating system of the robotics world. These are the 5 things that we talked about today. What does NVIDIA look like? What does NVIDIA look like? When we talk about GPUs, there's a very different image that I have when I -- when people ask me about GPUs. First, I see a bunch of software stacks and things like that. And second, I see this. This is what we announced to you today. This is Blackwell. This is the -- amazing processors, NVLink switches, networking systems, and the system design is a miracle. This is Blackwell. And this, to me, is what a GPU looks like in my mind. Listen, Orange, Green, I think we have one more treat for everybody. What do you think? Should we? Okay. We have one more thing to show you. Roll it. [Presentation]

Thank you. Thank you. Have a great GTC. Thank you all for coming. Thank you.