All right. Hello everybody. Welcome. We're literally 10 seconds late. It's not even that late. Uh, thank you everybody for being here. We are here for Nvidia GTC. Jensen's going to be speaking in about five minutes. It might be a little bit later, but it should be in five minutes at 12 p.m. Eastern. Uh, Tanner, what are you expecting? This should be a big one.

Should be a big one. Um, you know, GTC is all about artificial intelligence nowadays. So, I expect a lot of agentic stuff, uh, physical AI, self-driving, robotics, that sort of stuff. Maybe if we're lucky, Vera Rubin, or what the next steps are. You know, that would be very, very cool to see what their next rack system would look like. Uh, we already know. I mean, they've showed it off a bunch, but um, Jensen, if he can deliver enough hype on that, that will push the stock. A lot of the extra stuff is good to know, but if you're talking about what makes a financial difference of the business, it's, you know, what are the products that they're selling to Microsoft, Google, Amazon, etc.

But we'll find out. I mean, uh, we don't know 100%. A lot of people are down there though in Washington right now. Um, and I've heard nothing but good things so far.

Yeah, Jensen's there, Brad Gerstner's there. Um, you got the figure CEO. You got a lot of people there. I think we're I think it's going to be some interesting stuff. I mean, this is the event where they do show off their chips. So, I think it's likely, you would say, right, that we might get some Vera Rubin updates.

Yeah. Yeah. Um, yeah, I'm I'm I'm pretty confident in it. Um, but there has been some times where they'll skip a year before showing off their next big product. I think last time was a lot of um, Blackwell Ultra talk, but you know, they've been pushing out a lot of updates on Vera Rubin, their NVL 144s. So, I hope to see that. That would be really cool.

Agreed. Nvidia also invested a billion in Nokia. Uh, not sure why, but they did. I imagine there's going to be some announcement of a partnership. This is what I mean. Like you're going to get a bunch of different random stuff happening today. Uh here's a little bit of a headline. Nvidia to make 1 billion equity investment in Nokia in in addition to new strategic partnership Nokia's board resolved on directed share issuance to Nvidia.

Yeah. Do you have any reason why they would invest? I I I don't know of a reason right now, but whenever you're making this much money, I' I've been saying this for a long time, they are definitely going to be uh having a large venture arm and and giving out a lot of money to get equity stakes in businesses that are only going to push Nvidia further.

Yeah. So, um, you know, they're in pole position right now and they get to make these great investments and then the products that they sell to those customers or or the products that they're going to design with these customers like Intel and like all these other partnerships that they've recently given uh, you know, money to, that only goes to further the Nvidia advantage or or their their lead position. So, um, but it all comes down to GTC. I mean, I'm I'm really excited to see.

So, Jensen is not live yet, but this is a clip that we had from the pre-show, and I think this clip is just pretty freaking incredible. Uh, take a look at this. He crashed a panel, delivered water, and said, "Once a bus boy, always a bus boy."

This guy's worth a hundred billion delivering water. I mean, you got to love it. You got to love it. What is up with this mumbo jumbo that is quantum?

No, but but then he gives then he gives a little bit of a compliment from Denny's to DC. From Denny's to DC.

You you caused a kurfuffle uh last year when you you you made some comments about quantum. anything you would like to revise, you know, as we're about ready to launch our quantum panel here about how Nvidia thinks about quantum.

You know, I got to tell you, I'm afraid to say a word.

If I just said quantum, the stock price goes up. Quantum quantum quantum. I mean, he did c he caused those stocks to go 50% down in a day around March of last year or no of earlier this year of 2025 when he said, you know, quantum is decades away. I don't know about you, but I'm hearing a lot more coverage in the overall universe around quantum. So, maybe Jensen is going to embrace it. Also, the the a lot of the quantum bulls will say it's a threat to the dominance of GPUs. So, there's a lot of perspectives there to understand.

Yeah. Yeah. I mean, um, I don't have too many takes on quantum. We'll see what ends up happening, but, uh, I'm sure Jensen is going to keep his eye on it and, and if there is, uh, you know, if he sees large investment going that way, obviously Nvidia is going to be a big part of that as well, or at least try to position themselves there. And, and I think by having a quantum segment and all of these things, he's definitely, you know, not avoiding it. He's not brushing it off like um

I do think it's important that that if we're going to have a QPU that uh Jensen also is involved in that. So I'm not looking to get an individual quantum name in the portfolio. I have that through you know Google or um you know you could play that through Microsoft if you want. I don't know.

What are you looking forward to from GTC really quick? Oh, also, are you actually to stay on SoFi? Are you holding on to your position?

I mean, yeah. What else am I going to do? I feel like I can sell calls against it, right?

I'm just expecting him to be pretty confident and continue to deliver updates on this. I this rift that he's on now is AI factories. So, I think we're going to hear a lot about that. I think the S-curve about robotics, he really wants the world to understand physical AI is coming. And then from there, hopefully some Verar Rubin stuff would be be nice to see.

Um, no, not necessarily. I don't go in with with too high of expectations. Um, so no, I I'm here to learn, hear what he has to say. Um, you know, I don't know if we're going to get any Yeah, it's not an investor day, right? So, I'd be more interested in hearing what the earnings call has to say around China and stuff like this. This is going to be more product based. Um, so no, I don't have large expectations. I'm sure there's going to be a lot of networking talk, AI talk. If if Brett um Adcock is there, there's going to be a lot of robotics talk. There has been at GTC the last three years. So, um or last three events, but I don't have many expectations directly. No.

Yeah. I think robotics is going to be a big part. I agree.

Washington DC. Welcome to GTC. It's hard not to be sentimental and proud of America. I got to tell you that. Was that video amazing? Thank you. Nvidia's creative team does an amazing job. Welcome to GTC. We have a lot to cover with you today. Um GTC is where we talk about industry, science, computing, the present, and the future.

before I start, I want to thank all of our partners who helped sponsor this great event. You'll see all of them around the show. They're here to meet with you and uh uh really great. We couldn't do what we do without all of our ecosystem partners. Uh this is the Super Bowl of AI, people say. And therefore, every Super Bowl should have an amazing pregame show. What do you guys think about the pregame show? and our all allstar allstar athletes and allstar cast. Look at these guys. Somehow I turned out the buffest. What do you guys think?

Nvidia invented a new computing model for the first time in 60 years. As you saw in the video, a new computing model rarely comes about. It takes an enormous amount of time and set of conditions. We invented this computing model because we wanted to solve problems that generalpurpose computers, normal computers could not. We also observed that someday transistors will continue. The number of transistors will grow, but the performance and the power of transistors will slow down. that Moore's law will not continue beyond limited by the laws of physics and that moment has now arrived.

Dinard scaling has stopped. It's called dinard scaling. Dard scaling has stopped nearly a decade ago and in fact the transistor performance and its power associated has slowed tremendously and yet the number of transistor continued. We made this observation a long time ago and for 30 years we've been advancing this form of computing we call accelerated computing. We invented the GPU. We invented the the programming model called CUDA. And we observed that if we could add a processor that takes advantage of more and more and more transistors, apply parallel computing, add that to a sequential processing CPU that we could extend the capabilities of computing well beyond well beyond. And that moment has really come. We have now seen that inflection point. Accelerated computing its moment has now arrived.

However, accelerated computing is a fundamentally different programming model. You can't just take a CPU software software written by hand executing sequentially and put it onto a GPU and have it run properly. In fact, if you just did that, it actually runs slower. And so, you have to reinvent new algorithms. You have to create new libraries. You have to in fact rewrite the application which is the reason why it's taken so long. It's taken us nearly 30 years to get here. But we did it one domain at a time.

This is the treasure of our company. Most people talk about the GPU. The GPU is important, but without a programming model that sits on top of it, and without dedication to that programming model, keeping it compatible over generations, we're now CUDA 13 coming up with CUDA 14, hundreds of millions of GPUs running in every single computer, perfectly compatible. If we didn't do that, then developers wouldn't target this computing platform. If we didn't create these libraries, then developers wouldn't know how to use the algorithm and use the architecture to its fullest.

One application after another. I mean, these this is really the this is really the treasure of our company. CU litho computational lithography. It took us nearly seven years to get here with K litho and now TSMC uses it, Samsung uses, ASML uses it. This is an incredible library for computational lithography. the first step of making a chip. Sparse solvers for CAE applications. Co-op, a numerical optimization is broken just about every single record. The traveling salesperson problem, how to connect millions of products with millions of customers in the supply chain. Warp Python solver for CUDA for simulation. QDF a dataf frame approach basically accelerating SQL dataf frame pro dataf frame databases. Um this library is the one that started AI alto together coupnn the the library on top of it called megatron core made it possible for us to simulate and train extremely large language models.

The list goes on. Uh, Monai, really, really important, the number one medical imaging AI framework in the world. Uh, by the way, we're not going to talk a lot about health care today, but be sure to see Kimberly's keynote. She's going to talk a lot about the work that we do in healthcare. And the list goes on. Uh, genomics processing, Ariel, pay attention. We're going to do something really important here today. Um, coup quantum quantum computing. This is just a representative of 350 different libraries in our company. And each one of these libraries redesigned the algorithm necessary for accelerated computing. Each one of these libraries made it possible for all of the ecosystem partners to take advantage of accelerated computing. And each one of these libraries opened new markets for us. Let's take a look at what CUDA X can do.

Is that amazing? Every thing you saw was a simulation. There was no art, no animation. This is the beauty of mathematics. This is deep computer science, deep math. And it's just incredible how beautiful it is. Every industry was covered from healthcare and life sciences to manufacturing, robotics, autonomous vehicles, computer graphics, even video games. That first shot that you saw was the first application Nvidia ever ran. And that's where we started in 1993. And we kept believing in what we were trying to do. And it took, it's hard to imagine that you could see that first virtual fighter scene come alive and that same company believed that we would be here today. It's just really, really incredible journey. I want to thank all the NVIDIA employees for everything that you've done. It's really incredible.

We have a lot of industries to cover today. I'm going to cover AI, 6G, quantum, models, enterprise computing, robotics, and factories. Let's get started. We have a lot to cover, a lot of big announcements to make, a lot of new partners that would very much surprise you.

Telecommunications is the backbone, the lifeblood of our economy, our industries, our national security. And yet, ever since the beginning of wireless where we defined the technology, we defined the global standards, we exported American technology all around the world so that the world can build on top of American technology and standards. It has been a long time since that's happened. wireless technology around the world largely today deployed on foreign technologies. Our fundamental communication fabric built on foreign technologies.

That has to stop and we have an opportunity to do that especially during this fundamental platform shift. As you know computer technology is at the foundation of literally every single industry. It is the single most important instrument of science. It's the single most important instrument of industry. And I just said we're going through a platform shift. That platform shift should be the once-in-a-lifetime opportunity for us to get back into the game for us to start innovating with American technology. Today, today we're announcing that we're going to do that.

We have a big partnership with Nokia. Nokia is the second largest telecommunications maker in the world. It's a $3 trillion industry. Infrastructure is hundreds of billions of dollars. There are millions of base stations around the world. If we could partner, we could build on top of this incredible new technology fundamentally based on accelerated computing and AI. and for United States, for America to be at the center of the next revolution in 6G.

So today we're announcing that Nvidia has a new product line. It's called the Nvidia Arc. The aerial radio network computer, aerial RAM computer, Arc. Arc is built from three fundamental new technologies. the gray CPU, the Blackwell GPU, and our ConnectX Melanox Connect X networking designed for this application. And all of that makes it possible for us to run this library, this CUDA X library that I mentioned earlier called Aerial. Aerial is essentially a wireless communication system running on top of CUDA X. We're going to we're going to create for the first time a softwaredefined programmable computer that's able to communicate wirelessly and do AI processing at the same time. This is completely revolutionary. We call it Nvidia Arc.

And Nokia is going to work with us to integrate our technology, rewrite their stack. This is a company with 7,000 fundamental essential 5G patents. Hard to imagine any greater leader in telecommunications. So, we're going to partner with Nokia. They're going to make Nvidia Arc their future base station. Nvidia Arc is also compatible with Airscale, the current Nokia base stations. So what that means is we're going to take this new technology and we'll be able to upgrade millions of base stations around the world with 6G and AI.

Now 6G and AI is really quite fundamental in the sense that for the first time we'll be able to use AI technology AI for RAN to make radio communications more spectral efficient doing using artificial intelligence reinforcement learning adjusting the beam forming in real time in context depending on the surroundings and the traffic and the mobility the weather all of that could be taken into account so that we could improve spectral efficiency. Spectral efficiency consumes about one and a half to 2% of the world's power. So improving spectral efficiency not only improves the amount of data we can put through wireless networks without increasing the amount of energy necessary.

The other thing that we could do AI for RAN is AI on RAM. This is a brand new opportunity. Remember the internet enabled communications but amazingly smart companies AWS built a cloud computing system on top of the internet. We are now going to do the same thing on top of the wireless telecommunications network. This new cloud will be an edge industrial robotics cloud. This is where AI on RAN the first is AI for RAN to improve radio radio spectrum efficiency. The second is AI on RAN essentially cloud computing for wireless telecommunications. Cloud computing will be able to go right out to the edge where data centers are not are not because we have base stations all over the world.

This announcement is really exciting. Justin Hodar the CEO I think he's somewhere in the room. Thank you for your partnership. Thank you for helping United States bring telecommunication technology back to America. This is really a fantastic, fantastic partnership. Thank you very much.

Let's talk about quantum computing. 1981 particle physicist quantum physicist Richard Feman imagined a new type of computer that can simulate nature directly. To simulate nature directly because nature is quantum. He called it a quantum computer. 40 years later the industry has made a fundamental breakthrough. 40 years later, just last year, a fundamental breakthrough. It is now possible to make one logical cubit. One logical cubit. One logical cubit that's coherent, stable, and error corrected in past.

Now that one logical cubit consists of could be sometimes tens, sometimes hundreds of physical cubits all working together. As you know, cubits, these particles are incredibly fragile. They could be unstable very easily. Any observation, any sampling of it, any environmental condition causes it to become decoherent. And so, it takes an extraordinarily well-controlled environments. And now also a lot of different physical cubits for them to work together and for us to do error correction on these what are called auxiliary or syndrome cubits for us to error correct them and infer what that logical cubit state is.

There are all kinds of different types of quantum computers. superconducting, photonic, trapped ion, stable atom, all kinds of different ways to create a quantum computer. Well, we now realize that it's essential for us to connect a quantum computer directly to a GPU supercomputer so that we could do the error correction, so that we could do the artificial intelligence calibration and control of the quantum computer and so that we could do simulations collectively working together. the right algorithms running on the GPUs, the right algorithms running on the QPUs and the two processors, the two computers working side by side. This is the future of quantum computing.

So today we're announcing the MV MVQ link MVQL link and it's made possible by two things. Of course this interconnect that does quantum computer control and calibration quantum error correction as well as connects two computers the QPU and our GPU supercomputers to do hybrid simulations. It is also completely scalable. It doesn't just do the error correction for today's number of few cubits. It does error correction for tomorrow where we're going to essentially scale up these quantum computers from the hundreds of cubits we have today to tens of thousands of cubits, hundreds of thousands of cubits in the future. So we now have an architecture that can do control, co- simulation, quantum error correction and scale into that future.

The industry support has been incredible between the invention of CUDA Q. Remember CUDA was designed for GPU CPU accelerated computing. Basically using both processors to do use the right tool to do the right job. Now CUDAQ has been extended beyond CUDA so that we could support QPU and have the two processors QPU and the GPU work and have computation move back and forth within just a few microsconds. The essential latency to be able to cooperate with the quantum computer. So now CUDAQ is such an incredible breakthrough adopted by so many different developers.

We are announcing today 17 different quantum computer industry companies supporting the MVQ link and and I'm so excited about this eight different DOE labs Berkeley Brook Haven Fermy Labs in Chicago Lincoln Laboratory Los Alamos Oakidge Pacific Northwest San Die lab just about every single DOE lab has engaged us working with our ecosystem of quantum computer companies and these quantum controllers so that we could integrate quantum computing in into the future of science.

Well, I have one more additional announcement to make. Today, we're announcing that the Department of Energy is partnering with Nvidia to build seven new AI supercomputers to advance our nation's science.

I have to have a shout out for Secretary Chris Wright. He has brought so much energy to the DOE. A surge of energy, a surge of passion to make sure that America leads science. Again, as I mentioned, computing is the fundamental instrument of science. And we are going through several platform shifts. On the one hand, we're going to accelerated computing. That's why every future supercomputer will be GPUbased supercomputer.

We're going to AI. So that AI and principled solvers, principled simulation, principled physics simulation is not going to go away, but it could be augmented, enhanced, scaled, use surrogate models, AI models working together. We also know that principal solvers, classical computing could be enhanced to understand the state of nature using quantum computing. We also know that in the future we have so much signal, so much data we have to sample from the world. Remote sensing is more important than ever. And these laboratories are impossible to experiment at the scale and speed we need to unless they're robotic factories, robotic laboratories.

So all of these different technologies are coming into science at exactly the same time. Secretary Wright understands this and he wants the DOE to take this opportunity to supercharge themselves and make sure the United States stay at the forefront of science. I want to thank all of you for that. Thank you.

Let's talk about AI. What is AI? Most people would say that AI is a chatbot and it it's rightfully so. There's no question that Chad GPT is at the forefront of what people would consider AI. However, just as you see right now, these scientific supercomputers are not going to run chatbots. They're going to do basic science. Science, AI, the world of AI is much much more than a chatbot. Of course, the chatbot is extremely important and AGI is fundamentally critical. deep computer science, incredible computing, great breakthroughs are still essential for AGI. But beyond that, AI is a lot more.

AI is in fact, I'm going to describe AI in a couple different ways. This first way, the first way you think about AI is that it has completely reinvented the computing stack. The way we used to do software was hand coding. hand coding software running on CPUs. Today AI is machine learning training data inensive programming if you will trained and learned by AI that runs on a GPU. In order to make that happen, the entire computing stack has changed.

Notice you don't see Windows up here. You don't see CPU up here. You see a whole different a whole fundamentally different stack. Everything from the need for energy. And this is another area where our administration, President Trump gets deserves enormous credit. His pro- energy initiative, his recognition that this industry needs energy to grow. It needs energy to advance and we need energy to win. His recognition of that and putting the weight of the nation behind pro- energy growth completely changed the game. If this didn't happen, we could have been in a bad situation and I want to thank President Trump for that.

On top of energy are these GPUs and these GPUs are connected into built into infrastructure that I'll show you later. On top of this infrastructure which in consists of giant data centers like easily many times the size size of this room enormous amount of energy which then transfer transforms the energy through this new machine called GPU supercomputers to generate numbers. These numbers are called tokens.

the language, if you will, the computational unit, the vocabulary of artificial intelligence. You can tokenize almost anything. You can tokenize, of course, the English word. You can tokenize images. That's the reason why you're able to recognize images or generate images, tokenize video, tokenize 3D structures. You could tokenize chemicals and proteins and genes. You could tokenize cells, tokenize almost anything with structure, anything with information content. Once you could tokenize it, AI can learn that language and the meaning of it.

Once it learns the meaning of that language, it can translate. It can respond just like you respond just like you interact with chat GPT. And it could generate just as chat GPT can generate. So all of the fundamental things that you see Chad GPD do, all you have to do is imagine what if it was a protein, what if it was a chemical, what if it was a 3D structure like a factory, what if it was a robot and the token was understanding behavior and tokenizing motion and action. All of those concepts are basically the same, which is the reason why AI is making such extraordinary progress. And on top of these models are applications

This is a a profound understanding, a profound observation of artificial intelligence that the software industry of the past was about creating tools. Excel is a tool. Word is a tool. A web browser is a tool. The reason why I know these are tools is because you use them. The tools industry, just as screwdrivers and hammers, the tools industry is only so large. In the case of IT tools, they could be database tools. These IT tools is about a trillion dollars or so. But AI is not a tool.

AI is work. That is the profound difference. AI is in fact workers that can actually use tools. One of the things I'm really excited about is the work that Irvin's doing at Perplexity. Perplexity using web browsers to book vacations or do shopping. Basically an AI using tools. Cursor is an AI, a nigantic AI system that we use at NVIDIA. Every single software engineer at Nvidia uses cursor. That's improved our productivity tremendously. It's basically a partner for every one of our software engineers to generate code and it uses a tool and the tool it uses is called VS code. So cursor is an AI agentic AI system that uses VS code.

Well, all of these different industries whether it's chatbots or digital biology where we have AI assistant researchers or what is a robo taxi inside a robo taxi? Of course, it's invisible, but obviously there's a there's a AI chauffeur. That chauffeur is doing work. And the tool that it uses to do that work is the car. And so everything that we've made up until now, the whole world, everything that we've made up until now are tools. Tools for us to use. For the very first time, technology is now able to do work and help us be more productive.

The list of opportunities go on and on, which is the reason why AI addresses the segment of the economy that it has never addressed. It is a few trillion dollars that sits underneath the tools of a hundred trillion dollar global economy. Now, for the first time, AI is going to engage that hundred trillion dollar economy and make it more productive, make it grow faster, make it larger. We have a severe shortage of labor. Having AI that augments labor is going to help us grow.

Now what's interesting about this from a technology industry perspective also is that in addition to the fact that AI is new technology that addresses new segments of the economy AI in itself is also a new industry this token as I was explaining earlier these numbers after you tokenize all these different modalities of information there's a factory that needs to produce these numbers unlike the computer industry indry and the chip industry of the past.

Notice if you look at the chip industry of the past, the chip industry represents about 5 to 10% maybe less 5% or so of a multi- trillion dollar few trillion dollar IT industry. And the reason for that is because it doesn't take that much computation to use Excel. It doesn't take that much computation to use browsers. It doesn't take that much computation to use word. We do the computation. But in this new world, there needs to be a computer that understands context all the time. It can't precomputee that because every time you use the computer for AI, every time you ask the AI to do something, the context is different. So, it has to process all of that information.

Environmental, for example, in the case of a self-driving car, it has to process the context of the car. context processing. What is the instruction you're asking the AI to do? Then it's got to go and break down the problem step by step, reason about it, and come up with a plan and execute it. Every single one of that step requires enormous number of tokens to be generated which is the reason why we need a new type of system and I call it an AI factory.

It's an AI factory for short. It's unlike a data center of the past. is an AI factory because this factory produces one thing unlike the data centers of the past that does everything. Stores files for all of us, runs all kinds of different applications. You could use that data center like you can use your computer for all kinds of applications. You could use it to play game one day. You could use it to browse the web. You could use it, you know, to do your accounting. And so that is a computer of the past, a universal generalpurpose computer.

The computer I'm talking about here is a factory. It runs basically one thing. It runs AI and its purpose, its purpose is designed to produce tokens that are as valuable as possible, meaning they have to be smart. And you want to produce these tokens at incredible rates because when you ask an AI for something, you would like it to respond. And notice during peak hours, these AIs are now responding slower and slower because it's got a lot of work to do for a lot of people. And so you wanted to produce valuable tokens at incredible rates and you wanted to produce it cost effectively. Every single word that I used are consistent with an AI factory with a car factory or any factory. It is absolutely a factory. And these factories these factories never existed before. And inside these factories are mountains and mountains of chips.

The first is that we in the last couple years have figured out how to make AI much much smarter rather than just pre-training. Pre-training basically says let's take all of the all of the information that humans have ever created. Let's give it to the AI to learn from. It's essentially memorization and generalization. It's no it's not unlike going to school back when we were kids. the first stage of learning. Pre-training was never meant just as preschool was never meant to be the end of education.

Pre-training, preschool was simply teaching you the basic skills of intelligence so that you can understand how to learn everything else. Without vocabulary, without understanding of language and how to communicate, how to think, it's impossible to learn everything else. The next is post-training. Post-training after pre-training is teaching you skills. Skills to solve problem. Break down problems. Reason about it. How to solve math problems. How to code. How to think about these problems step by step. Use first principal reasoning.

And then after that is where computation really kicks in. We now have three fundamental technology skills. We have these three technology. Pre-training, which still requires enormous enormous amount of computation. We now have post training which uses even more computation and now thinking puts incredible amounts of computation load on the infrastructure because it's thinking on our behalf for every single human. So the amount of computation necessary for AI to think inference is really quite extraordinary.

Now I used to hear people say that inference is easy. Nvidia should do training. Nvidia is going to do you know they are really good at this so they're going to do training that inference was easy how could thinking be easy regurgitating memorized content is easy regurgitating the multiplication table is easy thinking is hard which is the reason why these three scales these three new scaling laws which is all of it in in full steam has put so much pressure on the amount of computation

The smarter your models are, the smarter your models are, the more people use it. It's now more grounded. It's able to reason. It's able to solve problems it never learn how to solve before because it could do research. Go learn about it. come back, break it down, reason about how to solve your how to answer your question, how to solve your problem and go off and solve it. The amount of thinking is making the models more intelligent. The more intelligent it is, the more people use it. The more intelligent it is, the more computation is necessary.

But here's what happened. This last year, the AI industry turned a corner. Meaning that the AI models are now smart enough. They're making they're worthy. They're worthy to pay for. Nvidia pays for every license of Cursor. And we gladly do it. We gladly do it because cursor is helping a several hundred,000 employee software engineer or AI researcher be many, many times more productive. So, of course, we'd be more than happy to do that. These AI models have become good enough that they are worthy to be paid for. Cursor, 11 Labs, Syntheasia, A Bridge, Open Evidence, the list goes on. Of course, open AI, of course, Claude.

These models are now so good that people are paying for it. And because people are paying for it and using more of it, and every time they use more of it, you need more compute. We now have two exponentials. These two exponentials, one is the exponential compute requirement of the three scaling law. And the second exponential, the more pe the smarter it is, the more people use it, the more people use it, the more computing it needs. Two exponentials now putting pressure on the world's computational resource at exactly the time when I told you earlier that Moore's law has largely ended.

And so the question is what do we do? If we have these two exponential demands growing and if we don't if we don't find a way to drive the cost down then this positive feedback system this circular feedback system essentially called the virtual cycle essential for almost any industry essential for any platform industry. AI has now reached the virtual cycle.

And so the more you use it because the AI is smart and we pay for it. The more profit is generated, the more profit generated, the more computes put to on the on the grid, the more compute is put into AI factories, the more comput the AI becomes smarter, the smarter, more more people use it, more applications use it, the more problems we can solve. This virtual cycle is now spinning. What we need to do is drive the cost down tremendously so that one the user experience is better when you prompt the AI it responds to you much faster and two so that we keep this virtual cycle going by driving its cost down so that it could get smarter so that more people use it so that so on so forth that virtual cycle is now spinning

but how do we do that when Moore's law has really reached this limit well the answer is called codeesign design. You can't just design chips and hope that things on on top of it is going to go faster. The best you could do in designing chips is add I don't know 50% more transistors every couple of years. And if you added more transistors just you we can add more transistors and TSMC's got a lot of transistor. Incredible company. We just keep adding more transistors. However, that's all in percentages not exponentials.

We need to compound exponentials to keep this virtual cycle going. We call it extreme code design. Nvidia is the only company in the world today that literally starts from a blank sheet of paper and can think about new fundamental architecture, computer architecture, new chips, new systems, new software, new model architecture, and new applications all at the same time. We fundamentally rearchitect everything from the ground up and then because AI is such a large problem, we scale it up.

We created a whole computer, a computer for the first time that has scaled up into an entire rack. That's one computer, one GPU. And then we scale it out by inventing a new AI Ethernet technology we call Spectrum X Ethernet. By doing so, we do code design at such a such an enormous level, such an extreme level that the performance benefits are shocking. Not 50% better each generation, not 25% better each generation, but much much more. This is the most extreme code-designed computer we've ever made and quite frankly made in modern times. Since the IBM system 360, I don't think a computer has been ground up, reinvented like this ever.

if we were to create one giant chip, one giant GPU, this is what it would look like. This is the level of wafer scale processing we would have to do. It's incredible. All of this, all of these chips are now put into one giant rack. this one giant rack makes all of these chips work together as one. It's actually completely incredible.

Now, these models are so gigantic. The way we solve it is we turn this model, this gigantic model, into a whole bunch of experts. It's a little bit like a team. And so, these experts are good at certain types of problems. And we collect a whole bunch of experts together. And so, this giant multi- trillion dollar AI model has all these different experts and we put all these different experts on a GPU. Now, this is NVLink 72.

We could put all of the chips into one giant fabric and every single expert can talk to each other. We have 72 GPUs. And because of that, we could put four experts in one GPU. versus here because each one of the computers can only put eight GPUs. We have to put 32 experts into one GPU. So this one GPU has to think for 32 experts versus this system each GPU only has to think for four. And because of that the speed difference is incredible.

And this just came out. This is the benchmark done by semi analysis. They do a really really thorough job and they benchmarked all of the a GPUs that are benchmarkable. the second best GPU in the world is the H200 and runs all the workload. Grace Blackwell per GPU is 10 times the performance. Now, how do you get 10 times the performance when it's only twice the number of transistors? Well, the answer is extreme code design. And by understanding the nature of the future of AI models and we're thinking across that entire stack, we can create architectures for the future.

This says that the lowest cost tokens in the world are generated by Grace Blackwell and Vlink 72. The most expensive computer. On the one hand, GB200 is the most expensive computer. On the other hand, its token generation capability is so great that it produces it at the lowest cost because the tokens per second divided by the t by the total cost of ownership of Grace Blackwell is so good. It is the lowest cost way to generate tokens. By doing so, delivering incredible performance, 10 times the performance, incre delivering 10 times lower cost, that virtual cycle can continue.

I just saw this literally yesterday. This is uh the CSP capex. People are asking me about capex these days and this is a good way to look at it. In fact, the capex of the top six CSPs and this one, this one is Amazon, Core Weave, Google, Meta, Microsoft, and Oracle. Okay, these CSPs together are going to invest this much in capex. And I would I would tell you the timing couldn't be better. And the reason for that is now we have the Grace Blackwell MVLink72 in all volume production, supply chain, everywhere in the world is manufacturing it. So we can now deliver to all of them this new architecture so that the capex invests in instruments computers that deliver the best TCO.

We're seeing extraordinary growth for Grace Blackwell for all the reasons that I just mentioned. It's driven by two exponentials. We now have visibility. I think we're probably the first technology company in history to have visibility into half a trillion dollars of cumulative blackwell and early ramps of Reubin through 2026. And as you know, 2025 is not over yet and 2026 hasn't started.

This is how much business is on the books. Half a trillion dollars worth so far. Now, this is out of that. We've already shipped 6 million of the Blackwells in the first several quarters. I guess the first four quarters of production, three and a half quarters of production. We still have one more quarter to go for 2025. And then we have four quarters. So the next five quarters there's $500 million $500 billion half a trillion dollars. That's five times the growth rate of Hopper. That kind of tells you something.

This is Hopper's entire life. This doesn't include China and and um and Asia. So this is just uh the West. Okay. This is just uh we're excluding China. So Hopper in its entire life 4 million GPUs. Blackwell. Each one of the Blackwells has two GPUs in it in one large package. 20 million GPUs of Blackwells in the early parts of Reuben. Incredible growth. So, I want to thank all of our supply chain partners. Everybody, I know how hard you guys are working. I made a video to celebrate your work. Let's play it.

We are manufacturing in America again. It is incredible. The first thing that President Trump asked me for is bring manufacturing back. Bring manufacturing back because it's it's necessary for national security. bring manufacturing back because we want the jobs and we want that part of the economy. And nine months later, nine months later, we are now manufacturing in full production Blackwell in Arizona.

Extreme Blackwell GB 200 MV Grace Blackwell Envy 72 extreme code design gives us 10x generationally. It's utterly incredible. Now, the part that's really incredible is this. This is the first AI supercomput we made. This is in 2016 when I delivered it to a startup in San Francisco which turned out to have been OpenAI. This was the computer. And in order to do the create that computer, we designed one chip.

Now, look at all of the chips we have to do. This is what it takes. You're not going to take one chip and make a computer 10 times faster. That's not going to happen. The way to make computers 10 times faster we can keep increasing the performance exponentially. We can keep driving cost down exponentially is extreme code design and working on all these different chips at the same time.

We now have Ruben back home. This is Ruben. This is the Vera Rubin and and uh Ruben. Ladies and gentlemen, Ruben. This is this is our third generation MVLink 72 rack scale computer. Third generation GB200 was the first one. All of our partners around the world, I know how hard you guys worked. It was insanely hard. It was insanely hard to do. Second generation, so much smoother. And this generation, look at this. Completely cableless.

completely cableless. And this is this is all back in the lab now. This is the next generation Reuben. While we're shipping GB300's, uh we're preparing Reuben to be in production. You know, this time next year, maybe slightly earlier. And so, every single year, we are going to come up with the most extreme code design system so that we can keep driving up performance and keep driving down the token generation cost. Look at this. This is just an incredibly beautiful computer.

compared to the DGX1 I delivered to OpenAI 10 years ago, 9 years ago, it's 100 times the performance right here versus 100 times of that supercomput. 100 of those would be like 25 of these racks all replaced by this one thing. One Vera Rubin. Okay. So this is this is the compute tray and this is so Vera Rubin super chip. Okay. And this is the compute tray. It's incredibly easy to install. If you decide you wanted to add a special processor, we've added another processor. It's called a context processor because the amount of context that we give AIS are larger and larger. We wanted to read a whole bunch of PDFs before it answer a question. Wanted to read a whole bunch of archive papers, watch a whole bunch of videos. All of that context processing could be added.

Now we used, as you notice, Nvidia started out by designing chips and then we started to design systems and we designed AI supercomputers. Now we're designing entire AI factories. Every single time we move out and we integrate more of the problem to solve, we come up with better solutions. We now build entire AI factories. This is going this AI factory is what we're building for Vera Rubin and we created a technology that makes it possible for all of our partners to integrate into this factory digitally. Let me show it to you.

completely completely in digital long long before Vera Rubin exists as a real computer we've been using it as a digital twin computer now long before these AI factories exist we will use it we will design it we'll plan it we'll optimize it and we'll operate it as a digital twin and so all of our partners that are working with us I'm incredibly happy for all of you supporting us

In the last couple years, several things have happened. One, open source models have become quite capable because of reasoning capabilities. It has become quite capable because they're multimodality and they're incredibly efficient because of distillation. So all these different capabilities have become uh has made open source models for the very first time incredibly useful for developers. They are now the lifeblood of startups.

Lifeblood of startups in different industries because obviously as I mentioned before each one of the industries have its own use case, it own use cases, it own data, it owns data, its own flywheels. All of that capability, that domain expertise needs to have the ability to embed into a model. Open source makes that possible. Researchers need open-source. Developers need open-source. Companies around the world, we need open source. Open- source models is really, really important.

The United States has to lead in open source as well. We have amazing proprietary models. We have amazing proprietary models. We need also amazing open source models. Our country depends on it. Our startups depend on it. And so Nvidia is dedicating ourselves to go do that. We are now the largest the largest we lead in open-source contribution. We have 23 models in leaderboards. We have all these different domains from language models the physical AI models. I'm going to talk about robotics models to biolog biology models. Each one of these models has enor enormous teams and that's one of the reasons why we built supercomputers for ourselves to enable all these models to be created. We have number one speech model, number one reasoning model, number one physical AI model. The number of downloads is really really terrific. We are dedicated to this and the reason for that is because science needs it, researchers need it, startups need it and companies need it.

AI will supercharge productivity. AI will transform just about every industry. But AI will also supercharge cyber security challenges, the bad AIs. And so we need an incredible defender. I can't imagine a better defender than Crowd Strike. George George is here. We are partnering with CrowdStrike to make cyber security speed of light to create a system that has cyber security AI agents in the cloud but also incredibly good AI agents on prem or at the edge. This way you whenever there's a threat you are moments away from detecting it. We need speed and we need a fast agentic AI super a super smart AIS.

I have a second announcement. This is the single fastest enterprise enterprise company in the world. Probably the single most important enterprise stack in the world today. Palunteer ontology. Anybody from Palunteer here? I just I was just talking to Alex earlier.

This is Palenter ontology. They take information, they take data, they take human judgment and they turn it into business insight. We work with Palunteer to accelerate everything Palunteer does so that we could do data processing data processing at a much much larger scale and more speed whether it's structured data of the past and of course we'll have structured data, human recorded data, unstructured data and process that data for our government for national security and for enterprises around the world process that data at speed of and to find insight from it. This is what it's going to look like in the future. Palunteer is going to integrate NVIDIA so that we could process at the speed of light and at extraordinary scale.

Physical AI requires three computers. Just as it takes two computers to train a language model. One that's to train it, evaluate it, and then inference it. In order to do it for physical AI, you need three computers. You need the computer to train it. This is GB the Grace Blackwell Envy 72. We need a computer that does all of the simulations that I showed you earlier with Omniverse DSX. It basically is a digital twin for the robot to learn how to be a good robot and for the factory to essentially be a digital twin. That computer is the second computer, the omniverse computer.

And once we train the model, simulate that AI inside a digital twin and that digital twin could be a digital twin of a factory as long as well as a whole bunch of digital twins of robots. Then you need to operate that robot. And this is the robotic computer. This is this one goes into a self-driving car. Half of it could go into a robot. These three computers all run CUDA. And it makes it possible for us to advance physical AI. AI that understand the physical world, understand laws of physic, causality, permanence, you know, physical AI.

That's the the future of manufacturing, the future of factories. I want to thank our partner Foxcon Young Leu, the CEO, is here, but all of these ecosystem partners make it possible for us to create the future of robotic factories. The factory is essentially a robot that's orchestrating robots to build things that are robotic.

Now, human robots is still in development. But meanwhile, there's one robot that is clearly at an inflection point and it is basically here and that is a robot on wheels. This is a robo taxi. A robo taxi is essentially an AI chauffeur. Now, one of the things that we're doing today, we're announcing the NVIDIA drive Hyperion. This is a big deal.

We created this architecture so that every car company in the world could create cars, vehicles, could be commercial, could be passenger, could be dedicated to robo taxi. create vehicles that are robo taxi ready. The sensor suite with surround cameras and radars and LAR make it possible for us to achieve the highest level of surround cocoon sensor perception and redundancy necessary for the highest level of safety. Hyperion drive drive Hyperion is now designed into Lucid Mercedes-Benz my friend Ola Ken Canel Kenas um the folks at Stalantis and there are many other cars coming and once you have a basic standard platform then developers of AV systems and there's so many talented ones wave wabby Aurora Momenta Neuro there's so many of them we ride there's so many of them that can then take their AV V system and run it on the standard chassis.

robo taxis inflection point is about to get here. And in the future, a trillion miles a year that are driven, a 100 million cars made each year. There's some 50 million taxis around the world. It's going to be augmented by a whole bunch of robo taxis. So, it's going to be a very large market to connect it and deploy it around the world. Today, we're announcing a partnership with Uber. Uber Dar Darra K Dara is going to we're working together to connect these Nvidia drive Hyperion cars into a global network and now in the future you'll you know be able to hail up one of these cars and the ecosystem is going to be incredibly rich

So this is what we talked about today. We talked about a large large number of things we spoke about. Remember at the core of this is two or two platform transitions from general purpose computing to accelerated computing. NVIDIA CUDA and those suite of libraries called CUDA X has enabled us to to address practically every industry and we're at the inflection point. It is now growing as a virtual cycle would suggest. The second inflection point is now upon us. The second platform transition AI from classical handwritten software to artificial intelligence. Two platform transitioning happening at the same time which is the reason why we're feeling such incredible growth.

Quant quantum computing. We spoke about open models. spoke about we spoke about enterprise with crowd strike and uh Palunteer accelerating their platforms. Uh we spoke about robotics a new large potentially one of the largest consumer electronics and industrial manufacturing sectors and of course we spoke about 6G. Nvidia has new platforms for 6G. We call it ARC. We have a new platform for robotics cars. We call that Hyperion. We have new platforms even for factories. Two types of factories. The AI factory we call that DSX and then factories with AI we call that mega. And so now we're also manufacturing in America.

Ladies and gentlemen, thank you for joining us today and thank you for allowing me to bring Thank Thank you for for allowing us to bring GTC to Washington DC. We're going to do it hopefully every year. And thank you all for your service and making America great again. Thank you.

Got to love Jensen, man. Thank you for making America great again. That was a little bit of a signal to a to a certain someone who controls the the China revenues for Nvidia. That's for sure.

He plays the political card so well. I mean, he brought it to Washington to be near Trump. You know what I mean? Like it he's just doing it amazingly. Okay. There was a lot of things to cover there, but honestly, it all boils down to the Palunteer comments. No, I'm kidding. uh to you maybe, but the the $500 billion in expected business over the next six quarters.

So, hold on. Is that confirmed? Did they like legit say that?

Yeah. Yeah. You you quoted Bloomberg on that. Jensen said it, bro.

Oh, so then Bloomberg was quoting Jensen. Okay, cool. So, that's So, so that's legit.

That's whenever the stock like really popped. You see those two uh or that huge candle? Well, I'm looking on the minutes, but um whenever it really popped, that was his comments.

Okay. So, do you think the street didn't think they were going to have that much in revenue? That was a surprise basically.

Yeah. Yeah. That candle right there is whenever he said it. So, essentially from I I looked on the street or expectations from Wall Street, they're expecting about 380 billion over the next six quarters.

Oh my.

So, he said 500 billion. and and that that he has insight into. You know, there could be a lot more than that.

Wow. F. So that's that's 120 billion more than the street expected without the second largest economy.

Isn't that crazy?

This shit's going to 10 trillion, bro.

Yeah, we're we're pumping right now. One. Oh my god. We're really pumping, dude.