NVIDIA CEO Jensen Huang GTC 2026 Full Keynote

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the stage, NVIDIA founder and CEO, Jensen Wong.

Welcome to GTC!

I just want to remind you, this is a tech conference. All these

people lining up, So early in the morning, all of you in here, it's great to see you. GTC. GTC, we're going to talk about technology. We're going to talk about platforms. NVIDIA has three platforms. You think that we mostly talk about one of them. It's related to CUDAX. Our

systems is another platform. And now we have a new platform called AI Factories. We're

going to talk about all of them. And most importantly, we're going to talk about ecosystems. But before I start, let me thank our pre-game show hosts. I thought they did a great job. Sarah Goh of Conviction.

Alfred Lim, Sequoia Capital, NVIDIA's first venture capitalist. Gavin

Baker, NVIDIA's first major institutional investor. These

three people, are deep in technology, deep in what's going on, and of course, they have just a really broad reach of technology ecosystem. And then of course, all of the VIPs that I hand-selected to join us today, All-Star team, I want to thank all of you for that.

I also want to thank all the companies that are here. NVIDIA, as you know, is a platform company. We have technology, we have our platforms, we have a rich ecosystem. And today, there are probably 100%

ecosystem. And today, there are probably 100% of the $100 trillion of industry here. 450 companies sponsored this event.

I want to thank you. 1,000 technical sessions, 2,000 speakers. This is incredible. This conference is going to cover every single layer of

speakers. This is incredible. This conference is going to cover every single layer of the five layer cake of artificial intelligence. From land power and shell, the infrastructure, to chips, to the platforms, the models, and of course the most important and ultimately what's going to get this industry taken off is all of the applications.

What it all began, it all began here. This is the 20th anniversary of CUDA.

We've been working on CUDA for 20 years.

For 20 years, we've been dedicated to this architecture. This revolutionary invention, SIMD, single instruction, multi-threaded, writing scalar code could spawn off into multi-threaded application, much, much easier to program than SIMD. We

recently added tiles so that we could help people program tensor cores and the structures of mathematics that are so foundational to artificial intelligence today.

Thousands of tools and compilers and frameworks and libraries. In

open source, there's a couple of hundred thousand public projects. CUDA

literally is integrated into every single ecosystem. This chart

basically describes 100% of NVIDIA's strategies. You've been watching me talk about this slide from the very beginning. And ultimately, the single hardest thing to achieve is the thing on the bottom, installed base. It has taken us 20 years to now have built up hundreds of millions of GPUs and computing systems around the

world that run CUDA. We are in every cloud, we're in every computer company, We serve just about every single industry. The install base of CUDA is the reason why the flywheel is accelerating. The install base is what attracts developers, who then creates new algorithms that achieves a breakthrough. For example, deep learning.

There are so many others. Those breakthroughs leads to entirely new markets, which builds new ecosystems around them with other companies that join, which creates a larger install base. This flywheel is now accelerating. The number of downloads

install base. This flywheel is now accelerating. The number of downloads of NVIDIA libraries is incredibly accelerating. It's at a very large scale and growing faster than ever. This flywheel is what makes this computing platform able

than ever. This flywheel is what makes this computing platform able to sustain so much applications, so many new breakthroughs, but most importantly, it also enables these infrastructures to have extraordinarily useful life.

And the reason for that is very obvious. There's so many applications that you can run on NVIDIA CUDA. We support the entire, every single phase of the AI lifecycle.

We address every single data processing platform. We accelerate scientific principled solvers of all different kinds. And so the application reach is so great that once you install NVIDIA GPUs, the useful life of it is incredibly high.

It is also one of the reasons why Ampere that we shipped them some six years ago, the pricing of Ampere in the cloud is going up. And so all of that is made possible fundamentally because the install base is high, the flywheel is high, the developer reach is great. And when all of that happens and we continuously update our software, the computing cost declines.

The combination of accelerated computing speeding up applications tremendously. Meanwhile,

as we continue to nurture and continue to update software over its life, not only do you get the first time pop, you get the continuous cost reduction of accelerated computing over time. And we're willing to nurture, willing to support every single one of these GPUs in the world because they're all architecturally compatible. We're willing to do so because the install base is so large, if we release a new optimization,

it benefits millions. This applies to everybody in the world. This

combination of dynamics is what makes the NVIDIA architecture expand its reach, accelerating its growth, at the same time driving down computing costs, which ultimately encourages new growth. So CUDA is at the center of it. But our journey that could actually started 25 years ago. GeForce...

I know how many of you grew up with GeForce. GeForce is NVIDIA's greatest marketing campaign. We attract future customers starting

marketing campaign. We attract future customers starting long before you could afford to pay for it yourself. Your parents paid. Your

parents paid for you to be NVIDIA customers. And every single year, they paid up year after year after year until someday you became an amazing computer scientist and became a proper customer, a proper developer. But this is the house that GeForce made. 25 years ago, we started our journey which

led to CUDA. 25 years ago, we invented the programmable shader. A

perfectly unobvious invention to make an accelerator programmable. The world's first programmable accelerator, the pixel shader. 25 years ago,

programmable. The world's first programmable accelerator, the pixel shader. 25 years ago, that led us to explore further and further, 20 years later, five years later, the invention of CUDA. One of the biggest investments that we made. And we couldn't afford it at the time. And it consumed the vast majority of our company's profits.

was to take CUDA on the backs of GeForce to every single computer. We

dedicated ourselves to create this platform because we felt so strongly about its potential, but ultimately, the company's dedication to it, despite the hardships in the beginning, believing it every single day for 13 generations or 20 years, we now have CUDA installed everywhere. The Pixel Shader,

led to, of course, the revolution of GeForce. And then 10 years ago, we introduced... about 10 years ago, what is it, 8 years ago? We introduced RTX,

introduced... about 10 years ago, what is it, 8 years ago? We introduced RTX, a complete redesign of our architecture for the modern era of computer graphics. GeForce

brought CUDA to the world. GeForce, therefore, enabled Alex Krushevsky and Ilya Suskiver and Jeff Hinton, Andrew Ng, and so many others to discover that the GPU could be their friend in accelerating deep learning. It started the big bang of AI. Ten years ago, we decided that we would fuse

AI. Ten years ago, we decided that we would fuse programmable shading and introduce two new ideas. Ray tracing,

hardware ray tracing, which is incredibly hard to do, and a new idea at the time. Imagine, about ten years ago, we thought that AI would revolutionize computer graphics.

time. Imagine, about ten years ago, we thought that AI would revolutionize computer graphics.

GeForce brought AI to the world. AI is now going to go back and revolutionize how computer graphics is done all together. Well, today I'm going to show you something of the future. This is our next generation of graphics technology. We call it neural rendering, the fusion, the fusion of 3D graphics and

artificial intelligence. This is DLSS5. Take a look at it.

artificial intelligence. This is DLSS5. Take a look at it.

Computer graphics comes to life. Now, what did we do? We fused controllable 3D graphics, the ground truth of virtual worlds, the structured data. Remember this word, the structured data of virtual worlds, of generated worlds. We

combined 3D graphics, structured data, with generative AI, probabilistic computing. One of them is completely predictive, the other one, probabilistic,

probabilistic computing. One of them is completely predictive, the other one, probabilistic, yet highly realistic. We combine these two ideas, combined these two ideas, controlled through structured data, controlled perfectly, and yet generating at the same time. And as a result, the content is beautiful,

amazing, as well as controllable. This concept of fusing structured information and generative AI will repeat itself in one industry after another industry after another industry. Structured data is the foundation of trustworthy AI. Well, this is going to scare you a little bit. I'm going to flip

AI. Well, this is going to scare you a little bit. I'm going to flip the slide and don't gasp. So we're going to go through this schematic for the rest of the time. This is my best slide. Every time I asked the team, what's my best slide, repeatedly this was

best slide. Every time I asked the team, what's my best slide, repeatedly this was it. They say, don't do it, Jensen. Don't do it. I said, no.

it. They say, don't do it, Jensen. Don't do it. I said, no.

These seats are free for some of you.

So this is your price of admission. So this is structured data. You've heard of it. SQL, Spark, Pandas, Velox, some of these. really,

it. SQL, Spark, Pandas, Velox, some of these. really,

really important, very large platforms, Snowflake, Databricks, EMR, Amazon EMR, Azure Fabric, Google Cloud, BigQuery. All of these platforms are processing data frames. These data frames are giant spreadsheets, and they hold all of life's information.

frames. These data frames are giant spreadsheets, and they hold all of life's information.

This is the structured data, the ground truth, business. This is the ground truth of enterprise computing. Well, now we're going to have AI use structured data and we better accelerate the living daylights out of it. It used to be okay and we would, you know, of course, we would accelerate structured data so that we

could do more, we could do it more cheaply, we could do it more frequently per day and keep the company running at a much more synchronized way. However, in

the future, What's going to happen is these data structures are going to be used by AI. And AI is going to be much, much faster than us. Future agents

by AI. And AI is going to be much, much faster than us. Future agents

are going to use structured databases as well. And then, of course, the unstructured database, the generative database. This database represents the vast majority of the world.

Vector databases, unstructured data, PDFs, videos, speeches, All of the world's information, about 90% of what's generated every single year, is unstructured data. Until

now, this data has been completely useless to the world. We read it, we put it into our file system, and that's it. Unfortunately, we can't query it, we can't search for it, it's hard to do that. And the reason for that is because there's no easy indexing of unstructured data. You have to understand its meaning, its purpose.

And so now we have AI do that. Just as AI was able to solve multi-modality perception and understanding, you can use that same technology, multi-modality perception and understanding, to go read a PDF to understand its meaning. And from that meaning, embedded into a larger structure that we

meaning. And from that meaning, embedded into a larger structure that we can search into, we can query into. NVIDIA created two foundational libraries just like we created RTX for 3D graphics we created QDF for data frames structured data we created QVS for vector stores semantic data

unstructured data AI data these two platforms are going to be two of the most important platforms in the future super excited to see its adoption and throughout the network this complicated network of the world's data processing systems and the reason for that is because data processing has been around a long time and therefore so

many different companies and platforms and services it has taken us a long time to integrate deeply into this ecosystem I'm super proud of the work that we're doing here and then today we're announcing several IBM the inventor of sequel one of the most important domain-specific languages of all

time is accelerating Watson X data with QDF. Let's take a look at it. 60 years ago, IBM

it. 60 years ago, IBM introduced the System 360, the first modern platform for general-purpose computing, launching the computing era. Then SQL, a declarative language to query data without requiring the computer to be instructed step by step, and the data warehouse,

each the foundations of modern enterprise computing. Today, IBM and NVIDIA are reinventing data processing for the era of AI by accelerating IBM Watson X.DataSQL engines with NVIDIA GPU computing libraries. Data is the

X.DataSQL engines with NVIDIA GPU computing libraries. Data is the ground truth that gives AI context and meaning. AI needs rapid access to massive data sets, Today's CPU data processing systems can't keep up.

Nestle makes thousands of supply chain decisions every day. Their order-to-cache Datamart aggregates every supply, order, and delivery event across global operations in 185 countries. On CPUs, Nestle refreshed the Datamart a few times a day.

countries. On CPUs, Nestle refreshed the Datamart a few times a day.

With accelerated Watson X-DOT data running on NVIDIA GPUs, Nestle can run the same workload five times faster at 83% lower cost. The next

computing platform has arrived. Accelerated computing for the era of AI.

NVIDIA accelerates data processing in the cloud. We also accelerate data processing on-prem.

As you know, Dell is the world-leading computer systems maker, and they also are one of the world's leading storage providers. And they worked with us to create the Dell AI data platform that integrates QDF and QVS to create an accelerated data platform, well, for the era of AI. And this is

an example of what they did with NTT data. Huge speed-up. This is Google Cloud, and Google Cloud, as you know, We've been working with Google Cloud for a very long time. We accelerate Google's Vertex AI. We now accelerate

long time. We accelerate Google's Vertex AI. We now accelerate BigQuery, really important framework and really important platform. And this is an example of our work together with Snapchat where we reduced their cost of computing by nearly 80%.

When you accelerate data processing, when you accelerate computing, you get the benefit of speed, you get the benefit of scale. But most importantly, you also get the benefit of cost. And so all of those come together as one. It was originally called

of cost. And so all of those come together as one. It was originally called Moore's Law. Moore's Law was about getting performance doubling every couple of years. It's another

Moore's Law. Moore's Law was about getting performance doubling every couple of years. It's another

way of saying, so long as the price remains about the same, and most computers remained about the same, you're also getting twice the performance every year, or you're reducing the cost of computing every single year. Well, Moore's Law has run out of steam.

We need a new approach. Accelerated computing allows us to take these giant leaps forward.

And as you will see later, because we continue to optimize the algorithms, and NVIDIA is an algorithm company, as we continue to optimize the algorithms, and because our reach is so large and our install base is so large, we can reduce the computing cost, increasing the scale, increasing the speed for everybody continuously. This is Google Cloud.

You can see this pattern I just mentioned. I just wanted to show you three versions of it. NVIDIA built the accelerated computing platform. It

has a bunch of libraries on top. I gave you three examples. RTX is one of them. QDF is another. QVS, and we'll show you a few more. These libraries

of them. QDF is another. QVS, and we'll show you a few more. These libraries

sit on top of our platform. But ultimately, we integrate into the world's cloud services, into the world's OEMs, Together, and other platforms that I'll show you, together, we're able to reach the world. This pattern,

NVIDIA, Google Cloud, Snapchat, will repeat over and over again, and it kind of looks like this. And so this is one example, NVIDIA with Google Cloud. We accelerate Vertex

like this. And so this is one example, NVIDIA with Google Cloud. We accelerate Vertex AI. We accelerate BigQuery. I'm super proud of the work that we've done with Jaxx

AI. We accelerate BigQuery. I'm super proud of the work that we've done with Jaxx and XLA. We are incredible on PyTorch. We're the only accelerator in the world that's

and XLA. We are incredible on PyTorch. We're the only accelerator in the world that's incredible on PyTorch and incredible on Jackson XLA. And the customers that we support, the base tens, the CrowdStrikes, Puma, Salesforce, they're not our customers, but they're customers, developers of ours that we've integrated the NVIDIA technologies into that we

can then land on the clouds. Our relationship with cloud service providers are essentially us bringing customers to them. We integrate our libraries, we accelerate workloads, and we land those customers in the clouds. And so, as you could see, most of our cloud service providers love working with us, and they're always

asking us to land the next customer on their cloud. And I just want to let you know, there are a lot of customers. We're going to accelerate everybody.

And so there'll be lots and lots of customers will be able to land in your cloud. Just be patient with us. And so this is Google Cloud. This is

your cloud. Just be patient with us. And so this is Google Cloud. This is

AWS. We've been working with AWS a long time. And one of the areas, one of the things I'm super excited about this year is we're going to bring OpenAI to AWS. And so it's going to drive enormous consumption of cloud computing at AWS.

to AWS. And so it's going to drive enormous consumption of cloud computing at AWS.

It's going to expand the reach and expand the compute of OpenAI. And as you know, they are completely compute constrained. And so AWS, we accelerate EMR, we accelerate SageMaker, we accelerate BitRock. NVIDIA's integrated really deeply into AWS. They were our first cloud partner. Microsoft

AWS. They were our first cloud partner. Microsoft

Azure. NVIDIA's A100 supercomputer was the first one we built was for NVIDIA. The first one we installed was at Azure. And that led to the inter... the big successful partnership with open AI,

at Azure. And that led to the inter... the big successful partnership with open AI, but we've been working with Azure for quite a long time. We accelerate Azure cloud now it's their AI foundry we partner deeply with we accelerate being search we work with them on Azure regions. This is one of the areas that is

incredibly important as we continue to expand AI throughout the world. One of the capabilities that we offer is confidential computing. That

in confidential computing, you want to make sure that even the operator cannot see your data. Even the operator cannot touch or see your models.

data. Even the operator cannot touch or see your models.

Confidential computing, NVIDIA's GPUs is the first ones in the world to do that. It's

now able to support confidential computing and protected deployment of these very valuable OpenAI models and anthropic models. throughout clouds and different regions and all because of our account confidential computing. Confidential computing is super important. And here's an example where we have different customers that we work with. Synopsys, a great partner of

ours, we're accelerating all of their EDA and CAE workflows. And then we landed at Microsoft Azure. We were Oracle's first AI customer.

Microsoft Azure. We were Oracle's first AI customer.

Most people would have thought we were their first supplier. We were their first supplier also. But we were their first AI customer. I'm quite proud of the fact that

also. But we were their first AI customer. I'm quite proud of the fact that I explained AI clouds to Oracle for the first time. And we were their first customer. Since then, they've really taken off. We've landed a whole bunch of our partners

customer. Since then, they've really taken off. We've landed a whole bunch of our partners there. Cohere and Fireworks and of course, very famously, OpenAI.

there. Cohere and Fireworks and of course, very famously, OpenAI.

A great partnership with CoreWeave. They're the world's first AI native A company that was built with only one singular purpose, to provision to host GPUs as the era of accelerated computing showed up, and to host for AI clouds. They've got some fantastic customers, and they're growing incredibly. One of

the platforms that I'm quite excited about is Palantir and Dell. The three of our companies have made it possible to stand up a brand new type of AI platform, the Palantir Ontology platform, an AI platform, And we could stand up these platforms in any country, in any air-gapped region, completely on-prem, completely

on-site, completely in the field. AI could be deployed literally everywhere. Without our confidential computing capability, without our ability to build the end-to-end system, as well as offer the entire accelerated computing and AI stack from data processing, whether it's vectors or structures, all the way to AI, it wouldn't have been possible. I wanted

to show you these examples. This is our special working relationship with the world's cloud service providers. And many, well, all of them are here. And

I get the benefit of seeing them during boot tour, and it's just so incredibly exciting. I just want to thank all of you for the hard work. What NVIDIA

exciting. I just want to thank all of you for the hard work. What NVIDIA

has done is this. And you're going to see this theme over and over again.

NVIDIA is vertically integrated, the world's first vertically integrated but horizontally open company. And the reason that's necessary is very simple. Accelerated computing is not a chip problem.

simple. Accelerated computing is not a chip problem.

Accelerated computing is not a systems problem. Accelerated computing has a missing word.

We just never say it anymore. Application acceleration. If I

could make a computer run everything faster, that's called a CPU. But that's run out of steam. The only way for us to accelerate applications going forward and continue to

of steam. The only way for us to accelerate applications going forward and continue to bring tremendous speed up, tremendous cost reduction is through application or domain specific acceleration. I dropped that phrase in the front and therefore it just became accelerated computing. And that is the reason why NVIDIA has to be library after

library, domain after domain, vertical after vertical. we are a vertically integrated computing company. There is no other way. We have to understand the applications, we have

computing company. There is no other way. We have to understand the applications, we have to understand the domain, we have to understand fundamentally the algorithms, and we have to figure out how to deploy the algorithm in whatever scenario it wants to be deployed, whether it's a data center, cloud, on-prem, at the edge, or in a robotic system.

All of those computing systems are different. And finally, the systems and chips. We are

vertically integrated. What makes it incredibly powerful, and the reason why you saw all the slides, is because NVIDIA is horizontally open. We'll work and integrate NVIDIA's technology into whatever platform you would like us to integrate into. We offer you the software.

We offer you libraries. We integrate with your technology so that we can bring accelerated computing to everybody in the world. Well, this GTC...

is really a great demonstration of that. You know, most of the time, most of the time you'll see me talk about these verticals, and I'll use some examples. But

in every single case, whether it's automotive, by the way, financial services, the largest percentage of attendees at this GTC is from the financial services industry.

I know. I'm hoping it's developers, not traders. Guys.

Here's one thing I wanted to say. And so, in the audience represents NVIDIA's ecosystem upstream of our supply chain and downstream of our supply chain. And we think about our supply chain upstream and downstream. And it's just

supply chain. And we think about our supply chain upstream and downstream. And it's just so exciting that our entire upstream supply chain this last year irrespective of whether you're a 50-year-old company, we have 70-year-old companies, we have a 150-year-old company who are now part of NVIDIA supply chain and partnering with us, either

upstream or downstream. And last year, you had your record year.

Did you not? Congratulations.

We're on to something here. This is the beginning of something very, very big. And

so, If you look at accelerated computing, we've now set the computing platform, but in order for us to activate those computing platforms, we need to have domain-specific libraries that solve very important problems in each one of the verticals that we address. You see us addressing every single one of this. Autonomous vehicles,

our reach, our breadth, our impact, Incredible. We have a track on that.

Financial services I just mentioned. Algorithmic trading is going from classical machine learning with human feature engineering called quant, the quants did that, to now supercomputers, studying massive amounts of data, discovering insight and discovering patterns by itself. And so this is going through its deep learning and its transformer moment.

by itself. And so this is going through its deep learning and its transformer moment.

Healthcare is going through their CHAP-GPT moment, some really exciting work that were there. We

have a great keynote track here. We have a great keynote track. Kimberly Powell's done a great keynote track for healthcare. We're talking about AI physics or AI biology.

for drug discovery, AI agents for customer service and support of diagnosis, and of course, physical AI, robotic systems. All these different vectors of AI have different platforms that NVIDIA provides. Industrial, we

are completely resetting and starting the largest build-out of human history.

And most of the world's industries building AI factories, building chip plants, building computer plants are represented here today. Media and entertainment, gaming of course, real-time AI platform so that we could translation and broadcast support and live games and live video, enormous amount of

it will be augmented with AI. We have a platform called Holoscan. Quantum, there

are 35 different companies here. building with us the next generation of quantum GPU hybrid systems, retail and CPG, using NVIDIA for supply chain, creating agentic shopping systems, AI agents for customer support.

A lot of work being done here, $35 trillion industry, robotics $50 trillion industry and manufacturing. NVIDIA has been working in this area for a decade now, building three computers, the fundamental computers necessary to build robotic systems. We are integrated with working with literally every single company that we know of building robots. We have 110

robots here at the show. And then telecommunications. About as large as the world's IT industry, about $2 trillion, we see, of course, base stations everywhere. It's

one of the world's infrastructures. It was the infrastructure of the last generation of computing.

that infrastructure is going to get completely reinvented. And the reason for that is very simple. That base station, which is... It does one thing,

simple. That base station, which is... It does one thing, which is base station. It's going to be an AI infrastructure platform in the future.

AI will run at the edge. And so lots of great discussion there. And our platform there is called Arial or AI RAM. big partnership with Nokia,

there. And our platform there is called Arial or AI RAM. big partnership with Nokia, big partnership with T-Mobile and many others. At the core of our business, everything that I just mentioned, computing platforms, but very importantly, our CUDAX libraries. Our CUDAX libraries is the algorithm, the algorithms that NVIDIA

CUDAX libraries. Our CUDAX libraries is the algorithm, the algorithms that NVIDIA invents. We are an algorithm company. That's what makes us special. That's what makes it

invents. We are an algorithm company. That's what makes us special. That's what makes it possible for me to be able to go into every single one of these industries, imagine the future, and have the world's best computer scientists describe and solve problems, refactor it, re-express it, and turn it into a library. We have so many.

I think we have, at this show, we're announcing 100 libraries, 70 libraries, maybe 40 models. And that's just at the show. We're updating these all the time. We're updating them all the time. The libraries is the crown jewels of our

time. We're updating them all the time. The libraries is the crown jewels of our company. It is what makes it possible for that platform, the computing platform, to be

company. It is what makes it possible for that platform, the computing platform, to be activated in service of solving a problem, making impact. One of the biggest, one of the most important libraries that we ever created, CUDNN. Cuda. deep

neural networks. It completely revolutionized artificial intelligence, caused the big bang of modern AI. Let me show you a short video about CUDAX. 20

modern AI. Let me show you a short video about CUDAX. 20

years ago, we built CUDA, a single architecture for accelerated computing.

Today, we've reinvented computing. A thousand CUDAX libraries help developers make breakthroughs in every field of science and engineering. Qopt for decision optimization. Qletho for computational lithography.

optimization. Qletho for computational lithography.

QDSS for direct sparse solvers. Qequivariance

for geometry-aware neural networks. Arial for AI-RAM.

Warp for differentiable physics. Parabricks for genomics.

At their foundation are algorithms, and they are beautiful.

was a simulation. Some of it was principle solvers, fundamental physics solvers.

Some of it was AI surrogates, AI physical models. And some of it was physical AI robotics models. Everything was simulated. Nothing was animated.

Nothing was articulated. Everything was completely simulated. That is

what fundamentally NVIDIA does. It is through the connection of understanding of the algorithms with our computing platforms that we're able to open up to unlock these opportunities. NVIDIA is a vertically integrated computing company with open horizontal integration with the world.

So that's CUDAX. Well, just now you saw a whole bunch of companies. You saw

Walmart and L'Oreal and incredible companies. established companies, JP Morgan and Roche. These are companies that define society to today. Toyota is here. These are some of the largest companies in the world.

to today. Toyota is here. These are some of the largest companies in the world.

It is also true that there's a whole bunch of companies you've never heard of.

These are companies, we call them AI natives, a whole bunch of small companies. The

list is gigantic. This is just a little tiny bit of it. And

I couldn't decide whether to show you more or show you less. And so I made it so that you couldn't see any. And nobody's feelings are hurt. However, inside this list are a bunch of brand new companies. There are companies

hurt. However, inside this list are a bunch of brand new companies. There are companies like, for example, you might have heard a couple of them, OpenAI, Anthropic. But there's

a whole bunch of others. There's a whole bunch of others. And they serve different verticals. Something happened in the last two years, particularly this last year. We've been

verticals. Something happened in the last two years, particularly this last year. We've been

working with the AI natives for a long time, and this last year, it just skyrocketed. I'll explain to you why it happened. This industry has skyrocketed. $150

skyrocketed. I'll explain to you why it happened. This industry has skyrocketed. $150

billion of investment into venture investment, into startups, the largest in human history. This is also the first time that the scale of the

history. This is also the first time that the scale of the investments went from millions of dollars tens of millions of dollars, the hundreds of millions of dollars and billions of dollars. And the reason for that is this is the first time in history that every single one of these companies needs

compute and lots and lots of it. They need tokens, lots and lots of it.

They're either going to create and build and create tokens and generate tokens, or they're going to integrate, add value to, and the tokens that are available created by Anthropic and OpenAI and others. And so this industry is different in so many different ways, but the one thing that is very clear, the

impact that they're making, the incredible value that they're delivering already is quite tangible. AI natives. All because we reinvented

tangible. AI natives. All because we reinvented computing. Just like during the PC revolution, a whole bunch of new companies were created,

computing. Just like during the PC revolution, a whole bunch of new companies were created, Just as during the Internet revolution, a whole bunch of companies were created, and a mobile cloud, a whole bunch of companies were created. Each one of them had their own standards, and we're talking about one of the major standards that just happened, incredibly important. And this generation, we also have our own large number of

important. And this generation, we also have our own large number of very, very special companies. We reinvented computing. It stands to reason there's going to be a whole new crop of really important companies, consequential companies, for the future of the world. The Googles, the Amazons, the Metas, consequential companies that have

come as a result of the last computing platform shift. We are now at the beginning of a new platform shift. But what happened in the last couple of years?

Well, we've been watching, as you know, we've been working on deep learning and working on AI. The big bang of modern AI, we were right there at the spot,

on AI. The big bang of modern AI, we were right there at the spot, and we've been advancing this field for quite some time. But why the last two years. What happened in the last two years? Well, three things. ChatGPT, of course,

years. What happened in the last two years? Well, three things. ChatGPT, of course, started the generative AI era. It's able to not just understand, perceive and understand. It's able to also translate and generate, generation of unique content.

I showed you the fusion of generative AI with computer graphics, and it brought computer graphics to life. You guys are just Everybody in the world should be using ChatGPT.

I know I use it every single morning. I used it plenty this morning. And

so ChatGPT was the generative AI era. The second, by the way, generative computing versus the way we used to do computing. Generative

AI is a capability of software, but it has profoundly changed how computing is done.

Computing used to be retrieval-based. Now it's generative. Keep that thought in mind. when I

talk about certain things and you'll realize why it is that everything that we do is gonna change how computers are architected, how computers are provided, how computers are gonna be built out, and what is the meaning of computing altogether? Generative AI, 2023, end of 22, 2023. The next, reasoning AI, 01,

and then took off with 03. Reasoning allowed it to reflect, allows it to think to itself, allowed it to plan, break down problems and decompose a problem it couldn't understand into steps or parts that it could understand.

It could ground itself on research. O1 made generative AI trustworthy and grounded on truth. That caused ChatGPT to simply took off. And that was a very, very big moment. The amount of input tokens that was necessary in order to produce and the amount of output tokens it

generated in order to reason. The model was a little bit larger. Of course, you could have much larger models. The model 01 was a little bit larger, not much larger. But its input token usage for context and its

larger. But its input token usage for context and its output token for thinking increased the amount of computation tremendously. Then

came clock code, the first agentic model. It was able to read files, code, compile it, test it, evaluate it, go back and iterate on it.

Cloud Code has revolutionized software engineering, as all of you know. 100% of NVIDIA is using a combination of, or oftentimes all three of them, Cloud Code, Codex, and Cursor, all over NVIDIA. There's not one software engineer today who is not assisted by one or many AI agents helping them code. Cloud code completely

revolutionizes the new inflection. And for the first time, you don't ask AI what, where, when, how. You ask it create, do,

how. You ask it create, do, build. You ask it to use tools, take your context,

build. You ask it to use tools, take your context, read files, it's able to agentically break down a problem, reason about it, reflect on it, it's able to solve problems and actually perform tasks. An

AI that was able to perceive became an AI that could generate. An AI that could generate became an AI that could reason. An AI that could reason now became an AI that can actually do work, very productive work. The amount of computation in the last two years, we know that everybody in this room knows the computing

demand For NVIDIA, GPU is off the charts. Spot pricing is skyrocketing. You couldn't find a GPU if you tried, and yet, in the meantime, we're

skyrocketing. You couldn't find a GPU if you tried, and yet, in the meantime, we're shipping GPUs out. Incredible amounts of it, and demand just keeps on going up.

There's a reason for that. This fundamental inflection. Finally,

AI is able to do productive work, and therefore, the inflection point of inference has arrived. AI now has to think.

In order to think, it has to inference. AI now has to do. In order

to do, it has to inference. AI has to read. In order to do so, it has to inference. It has to reason. It has to inference. Every part of AI, every time it has to think, it has to reason, it has to do, it has to generate tokens, it has to inference. It's way past training now. It's

in the field of inference. So the inference inflection has arrived. At

the time when the amount of tokens, the amount of compute necessary increased by roughly 10,000 times. Now, when I combine these two, the fact that since in the last two years, the computing demand of the work has gone up by 10,000 times.

And the amount of usage, the amount of usage, has probably gone up by a hundred times. People have heard me say, I believe that computing demand has increased by one million times in the last two years. It is the feeling that we all have. It is the feeling every

two years. It is the feeling that we all have. It is the feeling every startup has. It's the feeling that OpenAI has. It's the feeling that Anthropic has. If

startup has. It's the feeling that OpenAI has. It's the feeling that Anthropic has. If

they could just get more capacity, they could generate more tokens. Their revenues would go up. More people could use it. The more advanced, the smarter the AI could become.

up. More people could use it. The more advanced, the smarter the AI could become.

We are now at that positive flywheel system. We have reached that moment. The inference inflection has arrived. Last year,

moment. The inference inflection has arrived. Last year,

at this time, I said that where I stood at that moment in time, we saw about $500 billion. We saw $500 billion of

billion. We saw $500 billion of very high confidence demand and purchase orders for Blackwell and Ruben and through 2026. I said that last year.

Now, I don't know if you guys feel the same way, but $500 billion is an enormous amount of revenue. Not one impressed.

I know why you're not impressed, because all of you had record years.

Well, I'm here to tell you that right now where I stand, a few short months after GTCDC, one year after last GTC, right here where I stand, I see through 2027 at least $1 trillion.

Now, does it make any sense? And that's what I'm going to spend the rest of the time talking about. In fact, we are going to be short. I am

certain computing demand will be much higher than that. And there's a reason for that.

So the first thing is, we did a lot of work in the last year.

Of course, as you know, 2025 was NVIDIA's year of inference. We wanted to make sure that not only were we good at training and post-training, that we were incredibly good at every single phase of AI, so that the investments that were made, investments made in our infrastructure, could scale out for as long as they would like to

use it. And the useful life of NVIDIA's infrastructure would be long, and therefore the

use it. And the useful life of NVIDIA's infrastructure would be long, and therefore the cost would be incredibly low. The longer you could use it, the lower the cost.

There's no question in my mind. Nvidia systems are the lowest cost infrastructure you could get for AI infrastructure in the world. And so the first part was last year was all about AI for inference and it drove this inflection point. Simultaneously,

we were very pleased last year that Anthropic has come to Nvidia, that MSL, Meta SL has chosen Nvidia. And meanwhile, meanwhile, and as a collection, as a group, This represents one third of the world's AI compute open source models. Open source models have reached near the frontier

and it is literally everywhere. And Nvidia as you know today we're the only platform in the world today that runs every single domain of AI across every single one of these AI models. in language and biology and computer graphics, computer vision and speech, proteins and chemicals,

robotics and otherwise, edge or cloud, any language, NVIDIA's architecture is fungible for all of that and we're incredible for all of that. That

allows us to be the lowest cost, the highest confidence platform because when you're building these systems, As I mentioned, a trillion dollars is an enormous amount of infrastructure.

You have to have complete confidence that the trillion dollars you're putting down will be utilized, would be performant, would be incredibly cost-effective, and have useful life for as long as you could see. That infrastructure investment you could make on NVIDIA, you could make with complete confidence. We have now proven that. It is the only

infrastructure in the world that you could go anywhere in the world and build with complete confidence. You want to put it in any of the clouds, we're delighted by

complete confidence. You want to put it in any of the clouds, we're delighted by that. You want to put it on prem, we're happy about that. You want to

that. You want to put it on prem, we're happy about that. You want to put it in any country anywhere, we're delighted to support you. We are now a computing platform that runs all of AI. Now, our business already starting to show that. 60% of our business, is hyper scalers

the top five hyper scalers. However even within that top five hyper scalers some of it is internal AI consumption the internal AI consumption really important work like Rex is moving from recommender systems of tables and collaborative filtering and content filtering it's moving towards deep learning and large language models search moving to deep learning large language

models. Almost all of these different hyperscale workloads are now moving, shifting

models. Almost all of these different hyperscale workloads are now moving, shifting towards a workload that NVIDIA GPUs are incredibly good at. But on top of that, because we work with every AI lab, because we work with every AI, we accelerate every AI model, and because we have a large ecosystem of AI natives that we

work with that we can bring to the clouds, that investment, no matter how large, no matter how quick, that compute will be consumed. And that represents 60% of our business. The other 40% is just everywhere. Regional clouds, sovereign clouds,

business. The other 40% is just everywhere. Regional clouds, sovereign clouds, enterprise, industrial, robotics, edge, big systems, supercomputing systems, small servers, enterprise servers. The number of systems, incredible. The diversity of AI is also

incredible. The diversity of AI is also its resilience. The span of AI. of reach of AI is its

its resilience. The span of AI. of reach of AI is its resilience. There is no question this is not a one app technology. This is now

resilience. There is no question this is not a one app technology. This is now fundamental. This is absolutely a new computing platform shift. Well,

fundamental. This is absolutely a new computing platform shift. Well,

our job is to continue to advance the technology. And one of the most important things that I mentioned last year was last year was our year of inference. We

dedicated everything. We took a giant chance and reinvented While Hopper was at its prime and it was just cooking, we decided that the Hopper architecture, the MVLink by 8, had to be taken to the next level. We

completely re-architected the system, disaggregated the computing system altogether, and created MVLink 72. The way that it's built, the way it's manufactured, the way it's programmed

MVLink 72. The way that it's built, the way it's manufactured, the way it's programmed completely changed. Grace Blackwell, MVLink 72, was a giant bet. And it

completely changed. Grace Blackwell, MVLink 72, was a giant bet. And it

wasn't easy for anybody and many of my partners here in the room. I want

to thank all of you for the hard work that you guys did. Thank you.

NVLink 72, NVFP4, not just FP4 precision. FP4

is a whole different type of tensor core and computational unit. We've demonstrated now that we can inference NVFP4 without loss of precision, but gigantic boost in performance and energy efficiency, we've also been able to use MVFP4 for training.

So, MVLink 72, MVFP4, the invention of Dynamo, TensorRTLM, a whole bunch of new algorithms. We even built a supercomputer to help us optimize kernels and help us optimize our complete stack, we call it DGX Cloud. We

invested billions of dollars of supercomputing capability help us create the kernels, the software that made inference possible. Well,

the results all came together and people used to tell me, but Jensen, inference is so easy. Inference is the ultimate hard. Inference is ultimate hard.

It is also ultimate important because it drives your revenues. And so this is the outcome. This is from semi-analysis, this is the largest, most comprehensive sweep

outcome. This is from semi-analysis, this is the largest, most comprehensive sweep of AI inference that has ever been done. And what you see here on the left, on this side, on this side, is tokens per watt.

Tokens per watt is important because every data center, every single factory, by definition, is power constrained. A one gigawatt factory will never become two. It's physically constrained. The

power constrained. A one gigawatt factory will never become two. It's physically constrained. The

laws of atoms, the laws of physicality. And so that one gigawatt of data center...

you want to drive the maximum number of tokens, which is the production, the product of that factory. So you want to be on top of that curve as high as you want. The x-axis is the interactivity, the speed of inference, the speed of each inference. The faster you can inference,

the faster you could, of course, respond. But very importantly, the faster you can inference, the larger the models, the more context you could process, the more tokens you can think through, this axis is the same as smartness of the AI.

And so this is the throughput of the AI. This is the smartness of the AI. Notice, the smarter the AI, the lower your throughput.

AI. Notice, the smarter the AI, the lower your throughput.

Makes sense. You're thinking longer. Okay? And so this axis is the speed, and I'm going to come back to this. This is important. This is where I torture all of you. But it's too important. Every CEO in the world, you watch, every

of you. But it's too important. Every CEO in the world, you watch, every CEO in the world will study their business from now on in the way I'm about to describe. Because this is your token factory. This is

your AI factory. This is your revenues. There's no question about that going forward. And

so this is the throughput. This is the intelligence. Better per watt for a given power of data center, the more throughput. the more tokens you could produce. On this

side is cost. Notice, NVIDIA is the highest performance in the world. Nobody

would be surprised by that. They would be surprised by the fact that in one generation, whereas Moore's law would have given us through transistors 50%, two times, Moore's law would probably give us one and a half times more performance, you would have expected from Hopper H200, one and a half times higher.

Nobody would have expected 35 times higher. I said last year, at this time, that NVIDIA's Grace Blackwell, NVLink 72, was 35 times perf per watt. Nobody believed me. And then, semi-analysis came out, and Dylan Patel had a quote. He accused me of sandbagging

He accused me of sandbagging. He says, Jensen sandbagged. It's actually 50 times. And he's

not wrong. He's not wrong. And so our cost per token.

Our cost per token is the lowest in the world. You can't beat it.

I've said before, if you have the wrong architecture, even if it's free, it's not cheap enough. And the reason for that is because no matter what happens, you still

cheap enough. And the reason for that is because no matter what happens, you still have to build a gigawatt data center. You still have to build a gigawatt factory.

And that gigawatt factory, for 15 years amortized across, that gigawatt factory is about $40 billion. Even when you put nothing on, it's $40 billion in. You better make for

billion. Even when you put nothing on, it's $40 billion in. You better make for darn sure you put the best computer system on that thing so that you could have the best token cost. NVIDIA's token cost is world class.

basically untouchable at the moment. And the reason that's true is because of extreme co-design.

And so I'm very happy that he named us...

There was a monkey king.

Well, we take all of our software, as I told you, we vertically integrate, but we horizontally open. We're vertical integration, horizontal open. We integrate all of our software and all of our technology, however we could package it up and integrate it into the world's inference service providers. And these companies are

growing so fast. They're growing so fast. Fireworks, Lynn is here together. They're just growing so incredibly fast. A hundred times in the last year.

together. They're just growing so incredibly fast. A hundred times in the last year.

They are token factories. And the effectiveness, the performance, and the token cost production capability for their factories is everything to them. And this is what happened.

This is, we updated their software, same system, and notice their token speeds. Incredible. The difference

before, before NVIDIA updated everything and all of our algorithms and software and all the technology that we bring to bear, about 700 tokens per second average went to nearly 5,000, seven times higher. And so this is the incredible power of extreme co-design. I mentioned earlier the importance

of factories. This is the importance of factory. Your data center, it used to be

of factories. This is the importance of factory. Your data center, it used to be a data center for files. It's now a factory to generate tokens. Your factory is limited no matter what. Everybody's looking for land, power, and shell. Once you build it, you are power limited. Within that power limited infrastructure, you better make for darn

sure that your inference, because you know inference is your workload and tokens is your new commodity, that compute is your revenues, You want to make sure that the architecture is as optimized as you can. In the future, every single CSP, every single computer company, every single cloud company, every single AI company, every single

company period are going to be thinking about their token factory effectiveness. This is

your factory in the future. And the reason why I know that is because everybody in this room is powered by intelligence. and in the future that intelligence will be augmented by tokens. So let me show you how we got here.

On April 6th, 2016, a decade ago, we introduced DGX1, the world's first computer designed for deep learning. Eight Pascal

GPUs connected with the first generation NVLink. 170 teraflops in one computer. The world's first computer designed for AI researchers.

computer. The world's first computer designed for AI researchers.

With Volta, we introduced NVLink Switch. 16 GPUs connected with full all-to-all bandwidth operating as one giant GPU. A giant step forward, but model sizes continued to grow. The data center needed to become a single unit of computing. So Mellanox joined NVIDIA.

In 2020, DGX-A100 Superpod became the first GPU supercomputer combining scale-up and scale-out architecture. NVLink 3 for scale-up, Connect X6, and Quantum InfiniBand for scale-out.

Then Hopper. The first GPU with the FP8 transformer engine that launched the generative AI era. NVLink 4. Connect X7. Bluefield 3 DPUs.

era. NVLink 4. Connect X7. Bluefield 3 DPUs.

Second generation Quantum InfiniBand. It revolutionized computing.

Blackwell redefined AI supercomputing system architecture with NVLink 72. 72

GPUs connected by NVLink's fund. 130 terabytes per second of all-to-all bandwidth.

Compute Trace integrate Blackwell GPUs, Grace CPUs, Connect X8, and Bluefield 3.

Scale-Out runs over Spectrum 4 Ethernet. With three scaling laws in full steam, pre-training, post-training, and inference, and now agentic systems, compute demand continues to grow exponentially.

Vera Rubin. Architected for every phase of agentic AI. Advancing

every pillar of computing, including CPU, storage, networking, and security.

Vera Rubin NVLink 72. 3.6 exaflops of compute. 260

terabytes per second of all-to-all NVLink bandwidth. The engine supercharging the era of agentic AI. The Vera CPU rack. designed for orchestration and agentic workflows. The STX rack, AI-native storage, built with Bluefield 4.

Scale-out with Spectrum X co-packaged optics, increasing energy efficiency and resiliency.

And an incredible new addition, the GroK3 LPX rack. Tightly connected to Vera Rubin, GroK's LPUs massive on-chip SRAM, a token accelerator to the already incredibly fast Vera Rubin. Together, 35 times more throughput per megawatt. The new Vera Rubin platform. Seven chips, five

megawatt. The new Vera Rubin platform. Seven chips, five rack-scale computers, one revolutionary AI supercomputer for agentic AI. 40

million times more compute in just 10 years.

In the good old days, when I would say, Hopper, I would hold up a chip. That's just adorable.

chip. That's just adorable.

This is Verirubin. When we think Ver...

Ver...

When we think Verirubin, we think the entire system. Vertically integrated,

completely with software. Extended end to end. optimized as one giant system. The reason why it's designed for agentic systems is very clear, because agents,

giant system. The reason why it's designed for agentic systems is very clear, because agents, of course, the most important workload is it's thinking the large language model. The large

language models are going to get larger and larger and larger. It's going to generate more and more tokens more quickly, so it could think more quickly, but it also has to access memory. It's going to pound on memory really hard. KV

cache, structured data, QDF, unstructured data, QVS, it's going to be pounding on the storage system really, really hard, which is the reason why we reinvented the storage system. It is also going to use tools. And

unlike humans that are more tolerant to slower computers, AI wants the tools to be as fast as possible. These tools, web browsers in the future, they could also be virtual PCs in the cloud. Those PCs have to be and those computers have to be as fast as possible. We created a brand new

CPU. A brand new CPU that's designed for extremely high

CPU. A brand new CPU that's designed for extremely high single-threaded performance, incredibly high data output, incredibly good at data processing, and extreme energy efficiency. It

is the only data center CPU in the world uses LPDDR5 and incredible single thread performance and performance per watt that is unrivaled and so that's we built that so that it could go along with the rest of these racks for agentic

processing and so here it is this is the Grace Blackwall no Verirubin where is it here it is okay so this is the Verirubin system notice since the last time I 100% liquid cooled, all of the cables gone.

What used to take two days to install now takes two hours.

Incredible. And so the manufacturing cycle time is gonna dramatically reduce. This is also a supercomputer that is cooled by hot water, 45 degrees, which takes the pressure off of the data center takes all of that cost and all of the energy that's used to cool the data center and makes it

available for the system. This is the secret sauce.

It is the only we're the only company in the world that has today built the sixth generation scale up switching system. This is not Ethernet.

This is not infinite band. This is MV link. This is the sixth generation MV link. This is insanely hard to do well. It is insanely hard to do, period.

link. This is insanely hard to do well. It is insanely hard to do, period.

And I'm just super proud of the team. MVLink. Completely cool. This is the brand new Grok system. And I'll show you a little bit more about it. This system,

eight Grok chips. This is the LP30. The world's never seen it. Anything that the world's ever seen is V1. This is third generation. And we're in volume production now. And I'll show you more about that in just a second. The world's

production now. And I'll show you more about that in just a second. The world's

first... CPO Spectrum X switch.

This is also in full production. Co-packaged optics. Optics

comes directly onto this chip, interfaces directly to silicon, electrons get translated to photons, and it gets directly connected to this chip. We

invented the process technology with TSMC. We're the only one in production with it today.

It's called Coupe. It's completely revolutionary. NVIDIA is in full production with Spectrum X.

This is the Vera system, twice the performance per watt of any CPUs in the world today. It is also in production. Well, you know, we never

world today. It is also in production. Well, you know, we never thought we would be selling CPUs standalone. We are selling a lot of CPUs standalone. This is already... sure going to be a multi-billion dollar business for us so

standalone. This is already... sure going to be a multi-billion dollar business for us so I'm very very pleased with our CPU architects we designed a revolutionary CPU and this is the CX 9 powered with Vera CPU the blue field for STX our new storage platform okay so these are the four these are the

the racks and it's connected each one of these racks the MV link rack This is actually the guys this before it's a super heavy seems to get heavier every year. Because I think there's just more cables in there every year and so

every year. Because I think there's just more cables in there every year and so so this is the MV link rack we've also taken this technology because it is so. Efficient to create a data center with these cabling

so. Efficient to create a data center with these cabling systems structured cables so we decided to do that for Ethernet so this is.

Ethernet 256 liquid-cooled nodes in one rack, and it is also connected with these incredible connectors.

You guys want to see Ruben Ultra?

So this is the Rubin Ultra compute node. Unlike Rubin that slides in horizontally, Rubin Ultra goes into a whole new rack, it's called Kyber, that enables us to connect 144 GPUs in one MVLink domain. And so the Kyber rack, this,

I could lift it, I'm sure, but I won't. It's quite heavy.

This is one compute node and it slides into the Kyber rack vertically.

This is where it connects into. This is the mid-plane. The Kyber racks, those four top MV-Link connectors slide in and connect into this and this becomes one of the nodes. And each one of these racks is a different compute node. And this is the amazing part. This is the mid-plane.

And the back of the midplane, instead of the cabling system, which has its limits in terms of how far we could drive cables, copper cables, we now have this system to connect 144 GPUs. This is the new MVLink.

This sits also vertically, and it connects into the midplanes on the back. Compute in the front, MVLink switches in the back, one giant computer, okay? So that is Ruben Ultra.

As I mentioned, how about we take this back down?

I need the rest of my slides. Oh, it's coming down? Okay, thank you, Janine.

This is what happens when you don't practice.

Okay, all right. So, you saw... Take your time, just don't get hurt. You saw this slide.

Only on NVIDIA's keynote will you see last year's slide presented again. And the reason for that is I just want to let you know that last year I told you something very, very important. And it's so important, it's worthwhile to tell you again.

This is probably the single most important chart for the future of AI factories. And

every CEO in the world will be tracking it, will be studying it very deeply.

It's much, much more complicated than this, it's multi-dimensional. But you will be studying the throughput and the token speed of your AI factories, the throughput, token speed at ISO power, because that's all the power you have, throughput and token speed for your factories forever. And that

analysis is gonna lead directly to your revenues. What you do this year will show up precisely next year as your revenues. And this chart is what it's all about.

And I said on the vertical axis, on the vertical axis, thank you guys, On the vertical axis is throughput on the horizontal axis is token rate. Today I'm going to show you this. Because we're able because we're now able to increase the token speed and because model sizes are increasing because the token

length the context length depending on the different grades of different application use case continues to grow from maybe a hundred thousand tokens input length maybe millions. The token input length is growing and also the output

millions. The token input length is growing and also the output token length is growing. And so all of these play into ultimately the marketing and the pricing of future tokens. Tokens are the new commodity and like all commodities, once it reaches an inflection, once it becomes mature or becomes

maturing, it will segment into different parts. The High throughput, low speed could be used for the free tier. The next tier could be the medium tier. Larger model maybe, higher speed for sure, larger input

medium tier. Larger model maybe, higher speed for sure, larger input context length. That translates to a different price point. You could see

context length. That translates to a different price point. You could see from all the different services, this one is free, it's a free tier. The first

tier could be $3 per million tokens. The next tier could be $6 per million tokens. you would like to be able to keep pushing this boundary because

tokens. you would like to be able to keep pushing this boundary because the larger the model, smarter, the more input token context length, more relevant, the higher the speed, the more you can think and iterate smarter AI models. So this is about smarter AI models. And when you have smarter

AI models, each one of these clicks allows you to increase the price. So this

is $45. And maybe one day, there'll be a premium model that allows you, a premium service that allows you to generate token speeds that are incredibly high because you're in a critical path or maybe you're doing really long research. And $150

per million tokens is just not a thing. So let's translate that. Suppose you were to use 50 million tokens per day as a researcher, $150 per million tokens.

As it turns out, as a research team, that's not even a thing. So we

believe that this is the future. This is where AI wants to go. This is

where it is today. It had to start here to establish the value and establish its usefulness and get better and better and better. In the future, you're going to see most services encompass all of that. This is Hopper. Hopper

started and I moved the chart. This is 50, this is 100. Hopper looks like this. And you would have expected Hopper, the next generation, to be higher, but nobody

this. And you would have expected Hopper, the next generation, to be higher, but nobody would have expected it to be that much higher. This is Grace Blackwell. What Grace

Blackwell did is, at your free tier, increase your throughput tremendously. However,

where you mostly monetize your service, it increased your throughput by 35 times.

This is no different than any product that every company makes. The higher the tier, the higher the quality, the higher the performance, the lower the volume, the lower the capacity. And so it is no different than any other business in the world. And

capacity. And so it is no different than any other business in the world. And

so now we're able to increase this tier by 35x.

And we introduced a whole new tier. This is the benefit of Grace Blackwell, a huge jump over Hopper. Well this is what we're doing with...

Okay, so this is Grace Blackwell. Okay, let me just reset this.

And this is Vera Rubin. Okay?

Now just think what just happened. At every single tier, at every single tier, at every single tier we increase the throughput, And at the tier that where your highest ASP and your most valuable segment, we increased it by 10x.

That is the hard work. This is incredibly hard to do out here. This is

the benefit of HeavyLink 72. This is the benefit of extremely low latency. This is

the benefit of extreme co-design that we can shift the entire area up. Now, what

does it mean from a customer perspective in the end? Suppose I were to take all of that and I just, you know, multiply it against... Suppose I took 25% of my power, used it in a free tier, 25% of my power in the medium tier, 25% of my power in the high tier, and 25% of my power in the premium tier. My data center only has a gigawatt. And so I get

to decide how I want to distribute. The free tier allows me to attract more customers. This allows me to serve my most valuable customers. And the

customers. This allows me to serve my most valuable customers. And the

combination, the product of all that, allows you basically your revenues. The

revenues you can generate, assuming this simplistic example, allows Blackwell to generate five times more revenues. Vera Rubin to generate five times. Yeah.

So Vera Rubin, you should get there as soon as you can. And the reason for that is because your cost of tokens goes down and your throughput goes up.

But we want even more. We want even more. And so let me just show you back to this. This is, as I told you, this throughput requires a ton of flops. This latency, this interactivity requires enormous amount of bandwidth. Computers don't like extreme amount of flops, extreme amount of bandwidth,

because there's only so much surface area for chips that any systems has. And so

optimizing for high throughput, and optimizing for low latency are in fact enemies of each other. And so this is what happened when we combined with Grok. Okay, and so

other. And so this is what happened when we combined with Grok. Okay, and so we acquired the team that worked on the Grok chips and licensed the technology and we've been working together now to integrate the system. This is what that looks like.

So at the most valuable tier, at the most valuable tier, we're now going to increase performance by 35x. Now this very simple chart reveal to you exactly the reason why NVIDIA is so strong in the vast majority of the workloads so far. And the reason for that is because up

in this area, throughput matters so much. NvLing 72 is so game-changing. It is exactly the right architecture, and it's even hard to beat even as

game-changing. It is exactly the right architecture, and it's even hard to beat even as you add Grok to it. However, if you extend it, chart way out here and you said you wanted to have services that delivers not 400 tokens per second but a thousand tokens per second all of a sudden MVLink 72 runs out of steam and simply can't get there we just don't have enough bandwidth

and so this is where Grok comes in and this is what happens when we push that out so it goes out beyond thank you It goes out beyond even the limits of what MVLink 72 can do. And if

you were to do that, translate that into revenues. Relative to

Blackwell, Veraruban is 5x. If most of your workload is high throughput, I would stick with just 100% Veraruban. If a lot of your workload wants to be coding and very high valued engineering, token generation, I would add Grok to it. I would add Grok to maybe 25% of my

total data center. The rest of my data center is all 100% Vera Rubin. And

so that gives you a sense of how you would add Grok to Vera Rubin and extend its performance and extend its value even more. This is what happens.

This is a contrast. The reason why Grok was so attractive to me is because their computing system, a deterministic data flow processor, It is statically compiled. It is compiler scheduled, meaning the compiler figures out

compiled. It is compiler scheduled, meaning the compiler figures out when the data, when to do the compute, the computing data arrives at the same time. All of that is done statically in advance and scheduled

time. All of that is done statically in advance and scheduled completely in software. There's no dynamic scheduling. The architecture is designed with massive amounts of SRAM. It is designed just for inference.

one workload now this one workload as it turns out is the workload of AI factories and as the world continues to increase the amount of high-speed tokens it wants to generate with super smart tokens it wants to generate the value of this integration is going to get even higher and so these are two extreme processors you could

see one chip 500 megabytes one Vera Rubin chip one Rubin chip 288 gigabytes it would take a lot of Grok chips to be able to hold the parameter size of Rubin as well as all of the context that has to go, the KV cache that has to go

along with it. So that limited Grok's ability to really reach the mainstream, to really take off until we had a great idea. What if we disaggregated inference altogether with a piece of software called Dynamo? What if we re-architected the way that inference is done in the pipeline? so that we could put the work that makes perfect sense

on Verirubin and then offload the decode generation, the low latency, the bandwidth limited, challenged part of the workload for Grok. And so we united, unified two processors of extreme differences, one for high throughput, one for low latency. It still doesn't change the fact that we need a lot

of memory. And so Grok, we're just going to... whole bunch of grok chips which

of memory. And so Grok, we're just going to... whole bunch of grok chips which expands the amount of memory it has and so if you could just imagine out of a trillion parameter model we have to store all of that in grok chips however it sits next to nvidia vera rubin where we could we could hold the

massive amounts of kvcash that's necessary in processing all of these agentic AI systems it's based upon this idea of this aggregated inference. We do the pre-fill, that's the easy part, but we also tightly integrate the decode.

So the attention part of decode is done on NVIDIA's Verirubin, which needs a lot of math, and the feed forward network part of it, the decode part is done, the token generation part is done on Verirubin, on the Grok chip. The two

of them working tightly coupled together over today, Ethernet, with a special mode to reduce its latency by about half. And so that capability allows us to integrate these two systems. We run Dynamo, this incredible operating system for AI factories on top of it, and you get 35 times increase. 35 times

increase, not to mention additional new tiers of inference performance for token generation the world's never seen. So this is it. This is Grok.

Verirubin systems including Grok. I want to thank Samsung who manufactures the Grok LP30 chip for us and they're cranking as hard as they can. I really

appreciate you guys. We're in production with the Grok chip and we'll ship it in the second half probably about Q3 timeframe. Grok LPX.

Vera Rubin, it's kind of hard to imagine any more customers. The really great thing is

customers. The really great thing is Grace Blackwell's early sampling of it was really complicated because of coming together in MULink 72. But the sampling of Vera Rubin is just going incredibly well. In fact, Satya

72. But the sampling of Vera Rubin is just going incredibly well. In fact, Satya I think texted already that the first Vera Rubin rack is already up and running at Microsoft Azure. And so I'm super excited for them. We're going to keep cranking these things out. We have now set up a supply chain that can manufacture thousands

a week of these systems, essentially multi gigawatts of AI factories per month inside our supply chain. And so we're going to crank out these these Vera Rubin racks while we're cranking out the GB300 racks. We are in full production. The Vera CPUs, incredibly successful. And the reason for that is because

production. The Vera CPUs, incredibly successful. And the reason for that is because AI needs CPUs for tool use. And VeriCPU was designed just perfectly for that sweet spot. Incredible for the next generation of data processing. VeriCPU is

ideal. The VeriCPU plus CX9 connected into the Bluefield 4 stack. 100%

of the world's storage industry is joining us on this system. And

the reason for that is because they see exactly the same thing. The storage system is going to get pounded. It's going to get pounded because we used to have humans using the storage systems. We used to have humans using SQL. Now we're going to have AI's using these storage systems and it's going to store QDF accelerated storage,

QVS accelerated storage as well as very importantly KV caching. Okay, so this is the Vera Rubin system. Now what's amazing is this. In just two years time, in a one gigawatt factory, in just two years time, in one gigawatt factory, using the mathematics that I showed you earlier, whereas Moore's law would have given us

a couple of steps. We would have, you know, X-factored the number of transistors. We

would have X-factored the number of flops. We would have X-factored the number of amount of bandwidth. But with this architecture, We're going to take our token generation speed, token

of bandwidth. But with this architecture, We're going to take our token generation speed, token generation rate from 2 million to 700 million, 350 times increase.

This is the power of extreme co-design. This is what I mean when we integrate and optimize vertically, but then we open it horizontally for everybody to enjoy. This is

our roadmap, very quickly. Blackwell is here, the Oberon system. In the case of Rubin, we have the Oberon system. We're always backwards compatible

system. In the case of Rubin, we have the Oberon system. We're always backwards compatible so that if you wanted to not change anything and just keep on moving through with the new architecture, you could do so. The old system, the standard rack system, Oberon, still available. Oberon is copper scale up and with Oberon,

We could also use optical scale out, or excuse me, optical scale up to expand to MVLink 576. Okay? And so there's a lot of conversation about is NVIDIA going to copper scale up or optical scale up? We're going to do both. So we're going to have MVLink 144 with Kyber,

both. So we're going to have MVLink 144 with Kyber, and then with Oberon, we're going to MVLink 72, plus optical to get to MVLink 576. The next

generation of Rubin with Rubin Ultra, we have the Rubin Ultra chip, which is taping out, and we have a brand new chip, LP35. LP35

will, for the first time, incorporate NVIDIA's MVFP4 computing structure, give you another few X-factor speed up. Okay, and so this is... Oberon, NVLink 72, optical scale-up, and it uses Spectrum 6, the world's first co-packaged optical, and all of this is in production. The

next generation from here is Feynman. Feynman has a new GPU, of course. It also has a new LPU, LP40,

course. It also has a new LPU, LP40, big step up. Incredible, incredible new technology. Now,

uniting the scale of NVIDIA and the GroK team, building together LP40, it's gonna be incredible. A brand new CPU called Rosa, short for Rosalind.

Bluefield 5, which connects the next CPU with the next SuperNIC CX10. We will have Kyber, which is

CX10. We will have Kyber, which is copper scale up, we will also have Kyber CPO scale up. So for the first time, we will scale up with both copper and co-package optics. Okay?

And so a lot of people have been asking, you know, Jensen, is copper going to still be important? The answer is yes. Jensen, are you going to scale up Optical, yes. Are you gonna scale out optical?

Optical, yes. Are you gonna scale out optical?

Yes. And so for everybody who is in our ecosystem, we need a lot more capacity. And that's really the key. We need a lot more capacity for copper, we need a lot more capacity for optics, we need a lot more capacity for CPO, and that's the reason why we've been working with all of you to

lay the foundation for this level of growth. And so Feynman will have all of that. Let me see if I missed everything. That's it. Every single year, brand new

that. Let me see if I missed everything. That's it. Every single year, brand new architecture.

Very quickly, NVIDIA went from a chip company to an AI infrastructure company, AI computing company, these systems. And now we're building entire AI factories. There's so much power that is squandered in these AI factories. We want to make sure that these AI

factories come together, designed in the best possible way. Most of these components never meet each other. Most of us technology vendors now, we all know each other. But

in the past, we never met each other until the data center. That can't happen.

We're building super complex systems. And so we have to meet each other virtually somewhere else. And so we created Omniverse and the Omniverse DSX

else. And so we created Omniverse and the Omniverse DSX world, a platform where all of us can meet and design these gigafactories, gigawatt AI factories virtually in system. We have simulation systems for the racks for mechanical, thermal, electrical, networking, those

simulation systems integrated into all of our ecosystem partners of incredible tools companies. We also

operated, connected to the grid so that we could interact with each other, send each other information so that we could adjust grid power and data center power accordingly, saving energy. And then inside the data center using Max-Q so that we could adjust the system dynamically across power and cooling and all of

the different technologies we all work on together so that we leave no power squandered so that we run at the most optimal rate to deliver enormous amount of token throughput. There's no question in my mind there's a factor of two in here. And

throughput. There's no question in my mind there's a factor of two in here. And

the factor of two at the scale we're talking about is gigantic. We call this the NVIDIA DSX platform. And just as all of our platforms, there's the hardware layer, there's the library layer, and there's the ecosystem layer. It's exactly the same way. Let's

show it to you. the greatest infrastructure build-out in history is underway. The world is racing to build chip, system, and AI factories. And every month of delay costs billions in lost revenues.

AI factories. And every month of delay costs billions in lost revenues.

AI factory revenues are equal to tokens per watt. So with power constraints, every unused watt is revenue lost. NVIDIA DSX is an Omniverse digital twin blueprint designing and operating AI factories for maximum token throughput, resilience and energy efficiency. Developers connect through several

APIs. DSX-SIM for physical, electrical, thermal and network

APIs. DSX-SIM for physical, electrical, thermal and network simulation. DSX-XXX for AI factory operational data.

simulation. DSX-XXX for AI factory operational data.

DSX-FLEX for secure dynamic power management between the grid. And DSX-MAX-Q.

to dynamically maximize token throughput. It starts with SIM-ready assets from NVIDIA and equipment manufacturers, managed by PTC Windchill PLM.

Then, model-based systems engineering is done in Dasso Systems 3D experience.

Jacobs brings the data into their custom Omniverse app to finalize design.

It's tested with leading simulation tools, using Siemens Star CCM Plus for external thermals, Cadence Reality for internal, ETAP for electrical, and NVIDIA's network simulator, DSX Air, and virtually commissioned through Procore to ensure accelerated construction time.

When the site goes live, the digital twin becomes the operator. AI agents

work with DSX Max-Q dynamically orchestrate infrastructure.

Phaedra's agent oversees cooling and electrical systems, sending signals to Max-Q, which continuously optimizes compute throughput and energy efficiency. Emerald AI agents interpret live grid demand and stress signals and adjust power dynamically.

With DSX, NVIDIA and our ecosystem of partners, are racing to build AI infrastructure around the world, ensuring extreme resiliency, efficiency, and throughput.

It's incredible, right? Well,

Omniverse was designed to hold the world's digital twin, starting from the Earth. And

it's going to hold digital twins of all sizes. And so we have just such a great ecosystem of partners. I want to thank all of you. All of these companies are brand new to our world. We didn't know many of you just a couple of years ago. And now we're working so close together to work on and build together the largest computer the world's ever seen and also to do it at

planetary scale. So NVIDIA DSX is our new AI factory platform.

planetary scale. So NVIDIA DSX is our new AI factory platform.

I'll spend very little time on this this time. However, we're going to space. We've

already been out in space. Thor is radiation approved and we're in satellites. You do

imaging from satellites in the future. We'll also build data centers in space. Obviously, very complicated to do so. We're working with our partners on a

space. Obviously, very complicated to do so. We're working with our partners on a new computer called Vera Rubin Space One and it's going to go out to space and start data centers out in space. Now, of course, In space, there's no conduction, there's no convection, there's just radiation. And so we have to figure out how

to cool these systems out in space. But we've got lots of great engineers working on it. Let me talk to you about something new.

on it. Let me talk to you about something new.

So Peter Steinberger is here. He wrote a piece of software. It's called OpenClaw. And I don't know if he realized how successful it was gonna be, but the importance is profound. OpenClaw is the number one, it's the most popular open source

profound. OpenClaw is the number one, it's the most popular open source project in the history of humanity, and it did so in just a few weeks.

It exceeded, It exceeded what Linux did in 30 years. And it's that important. It

is that important. It will do well. This is

all you do. Okay? We're announcing our support of it. Let me just quickly go through this. I want to show you a couple things. You simply type this. You

through this. I want to show you a couple things. You simply type this. You

type this into a console. And it goes out. It finds OpenClaw.

It downloads it. It builds you an AI agent. Okay? then you could tell it whatever else you need to do. Okay, so let's take a look.

Andre Carpathie has just launched something called Research is a huge deal. You give an AI agent a test, go to sleep, it runs 100 experiments overnight, keeping what works and killing what doesn't.

I really love what my stuff enables that person to do. And we had like one guy, he told me like he installed it as a 60 year old dad and like they made deer, connected the machine via Bluetooth to OpenClaw. And then we automated everything, including the whole website for people to order.

Hundreds of people are queuing up for lobsters in Saint-Jean. Open Claw. Open Claw. Open Claw.

Open Claw. We want to build Open Claw with Open Claw. Everyone is talking about Open Claw, but what the f*** is Open Claw? Believe it or not, there's already a Claw con.

Incredible. Incredible. Now, I illustrated... effectively what open clause in this way so that all of you can understand it but let's just think what happened what is open clock it connects its an agent its agentic system it calls and connects to large language models so the first thing it has it has

resources that it manages it man it could access tools it could access file systems that could access large language models it is able to do scheduling it's able to do cron jobs is able to decompose a problem that a prompt that you gave it into step by step by step it could spawn off and call upon

other sub-agents it has I.O. you could talk to it in any modality you want you could wave at it and it understands you you could talk to any modality you want it sends you messages it texts you send you email so it's got I.O. what else does it have Well,

got I.O. what else does it have Well, based on that, you could say, in fact, it's an operating system.

I've just used the same syntax that I would describe an operating system.

OpenClaw has open sourced, essentially, the operating system of agentic computers. It is no different than how Windows made it possible for us to create

computers. It is no different than how Windows made it possible for us to create personal computers. Now, OpenClaw has made it possible for us to create computers.

personal computers. Now, OpenClaw has made it possible for us to create computers.

agents. The implication is incredible. The implication is incredible. First of all, the adoption says something in all in itself. However,

incredible. First of all, the adoption says something in all in itself. However,

the most important thing is this. Every single company now realized, every single company, every single software company, every single technology company, for the CEOs, the question is, what's your open clause strategy? Just as we need it all, have a Linux strategy. We all

needed to have a HTTP HTML strategy, which started the internet. We all needed to have a Kubernetes strategy, which made it possible for mobile cloud to happen. Every company

in the world today needs to have an open clause strategy and agentic system strategy.

This is the new computer. Now, this is just the exciting part. This is

enterprise IT before open claw. And I mentioned earlier, the way enterprise IT works And the reason why it's called data centers is because these large rooms, these large buildings held data, held the files of people, the structured data of business.

It would pass through software that has tools and systems of records and all kinds of workflow that's codified into it. And that turns into tools that humans would use, digital workers would use. That is the old IT industry, software companies creating tools, Saving files, and of course, GSI's consultants that help companies figure out how to use

these tools and integrate these tools. These tools are incredibly valuable for governance and security and privacy and compliance and all of that continues to be true. It's just

that post open clock, post agentic, this is what it's going to look like. This

is the extraordinary part. Every single IT company, every single company, every SAS company, Every SaaS company will become a gas company. No question about it. Every single SaaS company will become a gas company, an agentic as a service company. And what's amazing is this. You

now, OpenClaw gave us, gave the industry exactly what it needed at exactly the time. Just as... Linux gave the industry exactly what it

time. Just as... Linux gave the industry exactly what it needed at exactly the time, just as Kubernetes showed up at exactly the right time, just as HTML showed up. It made it possible for the entire industry to grab onto this open source stack and go do something with it. There's just one catch.

Agentic systems in the corporate network can have access to sensitive information, it can execute code, and it can communicate externally.

Just say that out loud, okay? Think about it. Access sensitive information, execute code, communicate externally. You could, of course, access employee information,

communicate externally. You could, of course, access employee information, access supply chain, access finance information, sensitive information, and send it out, communicate externally.

Obviously, this can't possibly be allowed. And so what we did was we worked with Peter, we took some of the world's best security and computing experts, and we worked with Peter open claw enterprise secure and enterprise private

capable and we call that this is our Nvidia open claw reference for open Nemo claw which is a reference for open claw and it has all these agentic AI toolkits and the first part of it is technology we call open shell that has now been integrated into OpenClaw. Now it's

enterprise ready. This stack, this stack with a reference design we call NemoClaw, okay, with a reference stack we call NemoClaw, you could download it, play with it, and you could connect to it the policy engine of all of the SaaS companies in the world. And your policy engines are super

important, super valuable. So the policy engines could be connected, Nemo Claw or Open Claw with Open Shell would be able to execute that policy engine. It has

a network guardrail. It has a privacy router. And as a result, we could protect and keep the Claws from executing inside our company and do it safely. We also added several things to the agentic system. And

one of the most important things you want to do with your own business custom clause is so that you can have your custom models. And this is NVIDIA's open model initiative. We are now at the frontier of every single domain

model initiative. We are now at the frontier of every single domain of AI models. Whether it's NemoTron, Cosmos World Foundation model, Groot, artificial general robotics, human robotics models, Alpamayo for autonomous vehicle, BioNemo for digital biology, Number two for AI

physics, we are at the frontier on every single one. Take a look.

The world is diverse. No single model can serve every industry. Open

Models is one of the largest and most diverse AI ecosystems in the world. Nearly

three million Open Models across language, vision, biology, physics, and autonomous systems enable AI builds for specialized domains. NVIDIA is one of the largest contributors to open source AI. We build and release six families of Open Frontier models, plus the training data, recipes, and frameworks to help developers customize and adopt. New

leaderboard topping models are launching for every family. At the core, NemoTron, reasoning models for language, visual understanding, RAG, safety, Speech. Can you hear me now? Hello? Yes, I can hear you now. Cosmos.

Speech. Can you hear me now? Hello? Yes, I can hear you now. Cosmos.

Frontier models for physical AI world generation and understanding.

Alpha Maya. The world's first thinking and reasoning autonomous vehicle AI.

Group. Foundation models for general purpose robots. Bionemo.

Open models for biology, chemistry, and molecular design. Our

models are... Thank you.

Our models are valuable to all of you because number one, it's on the top of the leaderboard. It's world-class.

Most importantly, it's because we are not going to give up working on it. We're

going to keep on working on it every single day. NemoTron 3 is going to be followed by NemoTron 4. Cosmos 1 was followed by Cosmos 2. Groot,

Generation 2. Each and one of these, we're going to continue to advance these models.

Vertical integration, horizontal openness. So that we can enable everybody to join the AI revolution. Number one on leaderboard across research and voice and world models and artificial general robotics and self-driving cars and reasoning. And of

course, one of the most important one, this is Nemo Tron three in open claw. This is Nemo Tron three and open claw. And look at the top

open claw. This is Nemo Tron three and open claw. And look at the top three. There are the three best models in the world. Okay, so we are at

three. There are the three best models in the world. Okay, so we are at the frontier.

It is also true that we want to create the foundation model so that all of you can fine tune it, post train it into exactly the intelligence you need.

This is NemoTron 3 Ultra. It is going to be the best base model the world's ever created. This allows us to help every country build their sovereign AI. And we're working with so many different companies out there. And

one of the most exciting things that we're doing today, I'm announcing today, is a Nemo Tron coalition. We are so dedicated to this. We have

invested billions of dollars of AI infrastructure so that we could develop the core engines for AI that's necessary for all the libraries of inference and so on, but also to create the AI models to activate every single industry in the world. Large language models is really important. Of course it's important. How

could human intelligence not be? However, In different industries around the world, in different countries around the world, you need to have the ability to customize your own models and the domain of the models is radically different from biology to physics to self-driving cars to general robotics to, of course, human language. And we have

the ability to work with every single region to create their domain-specific, their sovereign AI.

Today, we're announcing a coalition. Partner with us to make NemoTron 4 even more amazing. And that coalition has some amazing companies in it. Black

Forest Labs, imaging company, Cursor, the famous coding company, we use lots of it.

Langchain, billion downloads. creating custom agents, Mistral, Arthur mentioned, I think he's here, incredible, incredible company, Perplexity, Perplexity's computer, absolutely use it, everybody use it, it is so good, a multi-modal agentic system, reflection, Sarvam from India, Thinking Machine, Miramirati's lab,

incredible companies joining us, thank you.

I said, I said that every single enterprise company, every single software company in the world needs an agentic systems, need an agent strategy. You need to have an open claw strategy. And they all agree. And they're all partnering with us to integrate Nemo, the Nemo claw reference design, the NVIDIA

agentic AI toolkit, and of course, all of our open models. One company after another, there's so many. We're partnering with all of you. I'm really grateful for that.

And this is our moment. This is a reinvention. This is a renaissance, a renaissance of the enterprise IT from what would be a $2 trillion industry. This is going to become a multi-trillion dollar industry offering

industry. This is going to become a multi-trillion dollar industry offering not just tools for people to use, but agents, are specialized in very special domains that you're expert in that we could rent. I could totally imagine in the future every single engineer in our company will need an annual token budget. They're

going to make a few hundred thousand dollars a year of their base pay. I'm

going to give them probably half of that on top of it as tokens so that they could be amplified 10x. Of course we would. It is now one of the recruiting tools in Silicon Valley. How many tokens comes along with my job?

And the reason for that is very clear. Because every engineer that has access to tokens will be more productive. And those tokens, as you know, will be produced by AI factories that all of you and us, we partner to build. Okay? So

every single enterprise company in today on top of file systems and data centers every single software company of the future will be agentic and they will be token manufacturers there'll be token users for their engineers and they'll be token manufacturers for all of their customers the open clause in event the open claw event

cannot be understated this is as big of a deal as html this is as big of a deal as linux we have now a world-class open agentic framework that all of us could use to build our open clause strategy.

And we've created a reference design we call NemoClaw that all of you could use that is optimized, it's performant, it is safe and secure.

Speaking of agents, Agents as you know, perceive, reason, and act.

Most of the agents in the world today that I've spoken about are digital agents.

They act in the digital world. They reason, they write software. It's all digital.

But we also have been working on physically embodied agents for a long time. We

call them robots. And the AIs that they need are physical AIs. We have some big announcements here. I'm gonna just walk through a few of them. 110 robots here, almost every single company in the world, I can't think of one, that are building robots is working with NVIDIA. We have three computers, the training computer, the synthetic data

generation and simulation computer, and of course the robotics computer that sits inside the robot itself. We have all the software stacks necessary to do so, the AI models to

itself. We have all the software stacks necessary to do so, the AI models to help you. And all of this is integrated into ecosystems around the world,

help you. And all of this is integrated into ecosystems around the world, and all of our partners from Siemens to Cadence, incredible partners everywhere. And

today we're announcing a whole bunch of new partners. As you know, we've been working on self-driving cars for a long time. The ChatGPT moment of self-driving cars has arrived.

We now know we could successfully, autonomously drive cars. And today we are announcing four new partners for NVIDIA's robo-taxi-ready platform.

BYD Hyundai Nissan Geely all together, 18 million cars built each year. Joining our

partners from before, Mercedes, Toyota, GM, the number of robo-taxi-ready cars in the future are going to be incredible. And we're

announcing also a big partnership with Uber. Multiple cities, going to be deploying and connecting these robo-taxi-ready vehicles into their network. And so

a whole bunch of new cars. We have ABB, Universal Robotics, KUKA, so many robotics companies here. And we're working with them to implement our physical AI models integrated into simulation systems so that we could deploy these robots into manufacturing lines all over. We have Caterpillar here. We even have mobile

here and the reason for that is in the future that radio radio tower used to be a radio tower is going to be an NVIDIA aerial AI RAM and so this is going to be a robotics radio tower meaning it can reason about the traffic figures out how to adjust its beam forming so that they could save

as much energy as possible and increase the amount of fidelity as possible there's so many humanoid robots here but one of my favorites One of my favorites is a Disney robot. You know what? Tell you what, let me just show you some of the videos. Let's look at that first.

The first global rollout of physical AI at scale is here. Autonomous vehicles.

And with NVIDIA AlpaMaio, vehicles now have reasoning, helping them operate safely intelligently across scenarios. We ask the car to narrate its actions.

I'm changing lanes to the right to follow my route. Explain its thinking as it makes decisions. There's a double parked vehicle in my lane. I'm going around it.

makes decisions. There's a double parked vehicle in my lane. I'm going around it.

And follow instructions. Hey Mercedes, can you speed up? Sure, I'll speed up.

This is the age of physical AI and robotics. Around the world, developers are building robots of every kind. But the real world is massively diverse, unpredictable, full of edge cases. Real world data will never be enough to train for every scenario. We need data generated from AI and simulation. For

every scenario. We need data generated from AI and simulation. For

robots, compute is data. developers pre-trained World Foundation models on internet-scale video and human demonstrations and evaluate the model's performance to prepare them for post-training. Using classical and neural simulation, they generate massive amounts of synthetic data and train policies at

scale. To accelerate developers, NVIDIA built open-source Isaac

scale. To accelerate developers, NVIDIA built open-source Isaac Lab for robot training and evaluation and simulation. for extensible and GPU accelerated differentiable physics simulation, Cosmos World Models for neural simulation, and Groot Open Robotics Foundation Models for robot reasoning and

action generation. With enough compute, developers everywhere

action generation. With enough compute, developers everywhere are closing the physical AI data gap. Paritas AI trains their operating room assistant robot in NVIDIA Isaac Lab. multiplying their data with NVIDIA Cosmos world models. Skilled AI uses Isaac Lab and Cosmos to generate

models. Skilled AI uses Isaac Lab and Cosmos to generate post-training data for their skilled AI brain. They use reinforcement learning to harden the model across thousands of variations. Humanoid uses Isaac Lab to train whole body control and manipulation policies. Hexagon Robotics

uses Isaac Lab for training and data generation. Foxconn fine-tunes group models in Isaac Lab, as does Noble Machines. Disney Research uses their Kamino physics simulator in Newton and Isaac Lab to train policies across their character robots in every universe.

Ladies and gentlemen, Olaf. Woo

hoo! Snowman coming through. Newton works.

Wow. Omniverse works. Olaf, how are you?

I'm so happy now that I'm leaving you. I know. Because I gave you your computer. Jetson. What's that? Well, it's in your

computer. Jetson. What's that? Well, it's in your tummy. That's going to be amazing. And you learn how to walk

tummy. That's going to be amazing. And you learn how to walk inside Omniverse. I love walking. This is so

inside Omniverse. I love walking. This is so much better than riding on a reindeer gazing up at a beautiful sky.

And it was because of physics using this Newton solver.

that runs on top of Nvidia Warp that we jointly developed with Disney and with DeepMind that made it possible for you to be able to adapt to the physical world. Check that out. I was about to say that. That's how smart you are.

world. Check that out. I was about to say that. That's how smart you are.

I'm a snowman, not a Snoke-lopedia.

Could you imagine this? The future of Disneyland? All these... All these robots, all these characters wandering around. You know, I have to admit though, I thought you were going to be taller. I've never seen such a short snowman, to be honest.

Nope. Hey, tell you what, you want to help me out? Hooray!

Okay. Usually, usually I close the keynote by telling you what I told you.

We talked about inference inflection, we talked about the AI factory, we talked about the open claw agent revolution that's happening, and of course we talked about physical AI and robotics. But tell you what, why don't we get some friends to help us

and robotics. But tell you what, why don't we get some friends to help us close it out? Of course! All right, play it.

Terminating simulation.

Anybody here?

The keynote's over, always says and map the road ahead AI factories coming alive agents learning how to drive from open models to robots too now we'll break it all down for you compute exploded what we saw from CNNs to open cloth agents working across the land

but they need the power to meet demand so we solved the problem it was brilliant we multiplied compute by 40 million The models, how the inference runs the whole world now. Veris shows us who's the boss at 35 times less the cost. Blackwell makes the tokens sing NVIDIA, the inference king. Yeah, our

factories once took years. Vendors pulling racks and gears. Built up slowly piece by piece. No clear way to scale this piece. DSX and Dynamo know what to do.

piece. No clear way to scale this piece. DSX and Dynamo know what to do.

to guard the course. And yes, my friends, it's open source.

Cars that think and droids that run, this ain't the movies, it's all begun. Alchemyo

calls the shots, it's a GPT moment for the bots from sim to streets, now watch them.

For physical AI.

Nostril age built what came before. Now we build for AI even more. Vera Rubin

plus Grok make the inference splash. Put them together, now it's raining cash. We build

new architecture every year. Cause claws keep yelling more tokens here.

Welcome all to GTC.