Jensen Huang GTC 2026 Keynote

Full keynote by NVIDIA founder and CEO Jensen Huang (Korean translation)

GTC 2026 · San Jose, California

Part 1: GTC Opening - Welcome to a Technology Conference

Opening of the GTC 2026 Keynote and Introduction to NVIDIA's Three Platforms

Welcome to GTC. I want to remind you of one thing. This is a technology conference. To everyone who stood in line and waited from this early morning, it is truly good to have you here.

At GTC, we will talk about technology. We will also talk about platforms. NVIDIA has three platforms. You may think we mainly talk about one of them, the one related to CUDA X. Systems is another platform, and now there is a new platform called AI Factories. Today we will talk about all three. And most important, we will talk about the ecosystem.

Before we begin in earnest, I want to thank the people who hosted the pregame show. It was truly excellent. Sarah from Conviction, Alfred Lim from Sequoia Capital, and Gavin Baker, NVIDIA's first venture capitalist and NVIDIA's first major institutional investor. These three people are deeply fluent in technology and have remarkable insight across the technology ecosystem.

Thank you as well to every company attending today. As you know, NVIDIA is a platform company. We have technology, platforms, and a rich ecosystem. Nearly 100% of a $100 trillion industry is represented here today. Four hundred fifty companies sponsored this event, with 1,000 technical sessions and 2,000 speakers.

This conference will cover every layer of artificial intelligence's five-layer cake, from infrastructure such as land, power, and buildings, to chips, platforms, models, and ultimately the applications that will make this industry grow explosively.

Part 2: CUDA at 20 - The Foundation of Accelerated Computing

CUDA Platform's 20-Year Journey and Flywheel Effect

Everything began right here. This year is CUDA's 20th anniversary. We have been building CUDA for 20 years. For 20 years we have been committed to this architecture, this revolutionary invention, SIMT (Single Instruction, Multiple Threads). It is a structure in which scalar code can branch into a multithreaded application, far easier to program than SIMD. Recently we added tiles so tensor cores can be programmed, supporting the mathematical structures that form the basis of today's artificial intelligence.

Thousands of tools, compilers, frameworks, and libraries are available as open source. There are hundreds of thousands of public projects, and CUDA is literally integrated into every ecosystem. This chart basically explains 100% of NVIDIA's strategy.

The hardest thing to achieve in the end is what sits at the bottom: the installed base. After 20 years, hundreds of millions of GPUs and computing systems around the world now run CUDA. We are in every cloud, every computer company, and we serve nearly every industry.

CUDA's installed base is what accelerates the flywheel. The installed base attracts developers, and when developers create new algorithms and achieve breakthroughs, such as deep learning, those breakthroughs open entirely new markets, build new ecosystems, and create an even larger installed base. This flywheel is now accelerating. Downloads of NVIDIA libraries are growing at an enormous pace.

This flywheel is what allows this computing platform to support so many applications and new breakthroughs. Most important, it gives these infrastructures an astonishingly long life. That is also why the cloud price of Ampere, shipped six years ago, is actually rising.

Accelerated computing dramatically speeds up applications, and as we keep updating the software, users gain not only the performance improvement from the first adoption but continuing cost reductions over time. Because the installed base is so large, when we release new optimizations, millions benefit. That is the dynamic through which the NVIDIA architecture expands reach, accelerates growth, and lowers computing costs at the same time.

Part 3: From GeForce to RTX, and Then to Neural Rendering

A 25-Year Journey from Programmable Shaders to DLSS 5

Our journey actually began 25 years ago. GeForce. I know how many of you grew up with GeForce. GeForce is NVIDIA's best marketing campaign. We acquire future customers long before you can pay for it yourselves. Your parents paid. Year after year after year, they paid, and one day you became excellent computer scientists and became real customers.

Twenty-five years ago, we invented the programmable shader. It was a completely counterintuitive invention that made accelerators programmable: the world's first programmable accelerator, the pixel shader. Five years later, CUDA was invented. It was one of the biggest investments we ever made, and at the time it was almost too much to bear. We spent most of the company's profits to put CUDA, carried by GeForce, into every computer.

The pixel shader led the GeForce revolution, and GeForce brought CUDA to the world. That is how Alex Krizhevsky, Ilya Sutskever, Geoffrey Hinton, Andrew Ng, and many others discovered that GPUs were the best tool for accelerating deep learning. The Big Bang of AI began.

About 10 years ago, we decided to combine programmable shading with two new ideas: hardware ray tracing, and what was then a new idea, the vision that AI would revolutionize computer graphics. Just as GeForce brought AI to the world, AI is now about to completely transform computer graphics itself.

Today I will show you what the future looks like. Our next-generation graphics technology is neural rendering. It is the fusion of 3D graphics and artificial intelligence. DLSS 5. We combined controllable 3D graphics, the structured data of virtual worlds, with generative AI and probabilistic computing. One is fully predictive; the other is probabilistic yet extremely realistic. The result is beautiful and surprising while still controllable. This concept of fusing structured information with generative AI will repeat from industry to industry. Structured data is the foundation of trustworthy AI.

Part 4: Structured Data and Unstructured Data - cuDF & cuVS

Data Processing Revolution: IBM, Dell, and Google Cloud Partnerships

This is structured data. Major platforms you know, such as SQL, Spark, Pandas, Velox, Snowflake, Databricks, Amazon EMR, Azure Fabric, and Google Cloud BigQuery, process dataframes. These dataframes are enormous spreadsheets, and they contain all the information of life. They are the backbone of business and the source of truth for enterprise computing.

Now AI will use structured data. So we must accelerate it tremendously. In the future, AI will be much faster than we are, and future agents will also use structured databases.

Next are unstructured databases. Vector databases, PDFs, video, audio, and similar data account for about 90% of the world's information. Until now, this data was effectively useless in the world. You read it, put it in a file system, and that was it. Querying and searching were difficult because there was no easy indexing. You have to understand meaning and intent. Now AI does that work.

NVIDIA created two foundational libraries. Just as we created RTX for 3D graphics, we created cuDF for dataframes and structured data, and cuVS for vector stores and unstructured data. These two platforms will become the most important platforms in the future.

Today we are announcing several partnerships. IBM, the inventor of SQL, is accelerating Watson X Data with cuDF. Nestle runs supply-chain decisions across global operations in 185 countries on Watsonx.data accelerated by NVIDIA GPUs, completing the same work 5 times faster at 83% lower cost. Dell created the Dell AI Data Platform, integrating cuDF and cuVS. In our collaboration with Google Cloud BigQuery, we reduced Snapchat's computing costs by nearly 80%.

Part 5: Accelerated Computing Platform - Vertically Integrated, Horizontally Open

NVIDIA's Business Model and Global Cloud Partnerships

It was originally called Moore's Law. Performance doubled every two years, meaning computing costs fell every year. But Moore's Law has reached its limit. A new approach is needed, and accelerated computing gives us the leap. NVIDIA is an algorithms company. Because we keep optimizing algorithms and because our reach and installed base are large, we can keep reducing everyone's computing cost while increasing scale and speed.

What NVIDIA did is this. This theme will keep repeating. NVIDIA is vertically integrated, the world's first vertically integrated and horizontally open company. The reason is simple. Accelerated computing is not a chip problem. It is not a systems problem either. A word is missing: application acceleration. If you can make everything faster, that is a CPU. But the CPU has reached its limit.

To speed up applications and reduce costs from now on, domain-specific acceleration is needed. That is why NVIDIA must stack library on library, domain on domain, vertical on vertical. We need to understand the application, understand the domain, understand the algorithms at a fundamental level, and then find ways to deploy them anywhere, whether in data centers, clouds, the edge, or robots.

We are deeply integrated with the world's cloud service providers, including Google Cloud, AWS, Microsoft Azure, Oracle, and CoreWeave. We plan to bring OpenAI to AWS, work deeply with Azure in the AI foundry, accelerate Bing search, and support confidential computing. In confidential computing, even the operator cannot see your data or access the model. At Oracle, we were both the first AI customer and the first supplier.

With Palantir and Dell, we built a new type of AI platform that can deploy AI on-premises and in the field in any country and any air-gapped environment. AI can literally be deployed anywhere.

Part 6: Industry AI Applications & CUDA X Libraries

Comprehensive AI Libraries for Automotive, Healthcare, Robotics, Quantum Computing, and More

The largest share of attendees at this GTC came from the financial services industry. Algorithmic trading is shifting from classical machine learning and human feature engineering to deep learning and large language models. Healthcare is having its ChatGPT moment, with interesting work underway in AI biology for drug discovery, customer service AI agents, diagnostic support, and more.

Physical AI, robot systems, and the industrial sector are seeing the largest buildout in human history. AI factories, chip fabs, and computer factories are being built around the world. Robotics is a $5 trillion industry and manufacturing field, and NVIDIA has worked in this field for more than 10 years. We have built the three computers needed to make robots: the training computer, the synthetic data generation and simulation computer, and the robotics computer inside the robot. There are 110 robots on display at this show.

Telecommunications is about a $2 trillion market, as large as the global IT industry. Base stations will be completely reinvented as AI infrastructure platforms in the future because AI will run at the edge. We have large-scale partnerships with Nokia, T-Mobile, and others.

At the core of our business are the CUDA X libraries. The algorithms NVIDIA invents are what make us special. At this show, we are announcing 100 libraries, 70 libraries, and about 40 models. Libraries are our company's core assets. They enable a computing platform to solve problems and make an impact. One of the most important libraries we created, cuDNN, CUDA Deep Neural Networks, triggered the Big Bang of modern AI.

Part 7: AI-Native Companies and the Arrival of the Inference Inflection

ChatGPT, Reasoning AI, Claude Code - A 1-Million-Fold Increase in Computing Demand

There are countless small companies. The list is vast. OpenAI and Anthropic, of course, and many other companies serving many vertical markets. Over the past two years, and especially over the past year, there has been explosive growth. This industry recorded $150 billion in venture investment, the largest amount in human history.

For the first time, investment amounts jumped from millions and tens of millions of dollars to hundreds of millions and billions. The reason is that, for the first time in history, each of these companies needs computing, and needs a lot of it. They need tokens, a lot of tokens. They will generate and create tokens, or add value to tokens created by Anthropic, OpenAI, and others.

Because we reinvented computing, an entirely new class of companies is being born, as happened in the PC revolution, the internet revolution, and the mobile cloud era. We are now at the starting point of a new platform transition.

What happened over the past two years? Three things happened. First, ChatGPT opened the era of generative AI. It is the ability not only to understand and perceive, but to translate and generate, to create unique content. Generative computing is a capability of software, but it fundamentally changed the way computing itself works. Computing used to be retrieval-based. Now it is generative.

Second, reasoning AI, which began in earnest with o1 and then o3. Reasoning allowed AI to reflect on itself, think, plan, and break problems it could not understand into understandable steps. Because it could be grounded in research, o1 made generative AI trustworthy and grounded in truth. That was a major turning point that made ChatGPT grow explosively.

Third, Claude Code, the first agentic model. It can read files, code, compile, test, evaluate, go back, and iterate. Claude Code revolutionized software engineering. One hundred percent of NVIDIA uses Claude Code, Codex, and Cursor. Today there is not a single software engineer coding without the help of AI agents.

For the first time in history, we do not ask AI 'what, when, and how.' We say, 'Make it, do it, build it.' We tell it to use tools, read context, and read files. It breaks down problems agentically, reasons, reflects, solves problems, and actually performs work. AI that could perceive became AI that could generate, then AI that could reason, and now AI that can actually work.

Over the past two years, computing demand increased about 10,000-fold, and usage probably increased 100-fold. I believe computing demand increased 1 million-fold over the past two years. We all feel it, every startup feels it, OpenAI feels it, and Anthropic feels it. If there is more capacity, more tokens can be generated, revenue rises, more people use it, and AI becomes smarter. We have now reached that positive flywheel. The inference inflection has arrived.

Part 8: $1 Trillion in Demand - The Future of AI Infrastructure

At Least $1 Trillion in Computing Demand Expected by 2027

Around this time last year, from the very spot where I was standing, we identified about $500 billion in very high-confidence demand and purchase orders for Blackwell and Rubin through 2026. I told you that last year. $500 billion is an enormous amount. But none of you were impressed. I know why you were not impressed: because all of you had record years.

Today, one year after the last GTC, I see at least $1 trillion by 2027. Does that make sense? That is what we will talk about for the rest of the time. In fact, we will be short. Computing demand will certainly be much higher than that.

Sixty percent of our business is the top five hyperscalers. Within that, part is internal AI consumption: recommendation systems are moving from tables and collaborative filtering to deep learning and large language models, and search is doing the same. The remaining 40% is literally everywhere. Regional clouds, sovereign clouds, enterprises, industrial use, robotics, the edge, large systems, small servers. The diversity of AI is its resilience. This is not a single-app technology. This is clearly a transition to a new computing platform.

Part 9: The King of Inference Performance - The Cost-per-Token Revolution

Blackwell's 50x Performance Gain, Token King, and the AI Factory Concept

Last year was NVIDIA's year of inference. When Hopper was at its peak, we boldly redesigned the system completely. Grace Blackwell and NVLink 72 were a huge bet. It was not easy. I thank all of our partners.

NVLink 72, NVFP4: not plain FP4, but an entirely new kind of tensor core and compute unit. We proved that with NVFP4 we can perform inference without precision loss while dramatically improving performance and energy efficiency. NVFP4 can now also be used for training. We invented new algorithms such as Dynamo and TensorRT-LLM, and even built multibillion-dollar supercomputers for kernel optimization.

This is the largest and most comprehensive AI inference benchmark result from SemiAnalysis. The vertical axis is tokens per watt. Every data center is power-limited, so it must extract as many tokens as possible. The horizontal axis is inference speed. The faster it is, the larger the models and contexts it can handle, and the more it can think. This axis is AI intelligence.

No one should be surprised that NVIDIA has the world's best performance. What is surprising is that in one generation, where Moore's Law would have suggested 50%, perhaps 1.5x at best, we achieved 35x. Dylan Patel of SemiAnalysis accused me of sandbagging. In reality, it was 50x.

The cost per token is the lowest in the world and effectively impossible to catch. If the architecture is wrong, even free is not cheap enough. You have to build a gigawatt-scale data center anyway, and depreciated over 15 years, that is about $40 billion. Even if you put nothing inside, it costs $4 billion. You need to put in the best computing system.

Your data center is no longer a data center for files. It is now a factory that generates tokens. Factories are power-limited. Everyone is looking for land, power, and buildings. Once it is built, power becomes the limit. Inference is your work, tokens are the new raw material, and computing is revenue. Architecture must be optimized with that clearly in mind. In the future, every CSP, every computer company, and every AI company will think about token factory efficiency.

Part 10: Vera Rubin Architecture - Designed for Agentic AI

From DGX-1 to Vera Rubin: Seven Chips, Five Racks, One Supercomputer

On April 6, 2016, 10 years ago, we introduced DGX-1, the world's first computer designed for deep learning. It was a 170-teraflop computer with eight Pascal GPUs connected by first-generation NVLink. With Volta, we introduced the NVLink switch to connect 16 GPUs at full bandwidth. Hopper was the first GPU with an FP8 transformer engine and opened the era of generative AI. Blackwell redefined the AI supercomputing system architecture with NVLink 72.

And now, Vera Rubin. It is designed for every stage of agentic AI and advances every pillar of computing, including CPU, storage, networking, and security. NVLink 72, 3.6 exaflops of computing, and 260 TB/s of full-bandwidth NVLink bandwidth. It is the engine that drives the agentic AI era at ultra-high speed. It includes the new Vera CPU, the STX rack for AI-native storage, and BlueField 4, and scales out with Spectrum X co-packaged optics.

There is a remarkable new addition: the Groq 3 LPX wrapper. Groq's LPU, tightly connected to Vera Rubin, uses large on-chip SRAM and acts as a token accelerator for the already ultra-fast Vera Rubin. Together, they improve throughput per megawatt by 35x. Seven chips, a five-rack-scale computer, one revolutionary AI supercomputer. In only 10 years, 40 million times more computing.

This is the Vera Rubin system. It is 100% liquid-cooled, and all cables are gone. Installation used to take two days; now it takes two hours. It cools with 45-degree hot water, using the energy and cost normally spent on data-center cooling inside the system. The secret weapon is the sixth-generation NVLink scale-up switching system: not Ethernet, not InfiniBand, but sixth-generation NVLink. This is extremely difficult.

Rubin Ultra slides vertically into a new rack called Kyber, connecting 144 GPUs into one NVLink domain. Compute in the front, NVLink switches in the back, one gigantic computer.

Part 11: Token Factory Economics - A New Formula for Revenue

Token Tiering, Groq Integration, and Disaggregated Inference for a 350x Performance Gain

This is the most important chart for the future of the AI factory. Every CEO in the world will track it and study it closely. The vertical axis is throughput, and the horizontal axis is token speed. Tokens are the new raw material. Once the market matures, tokens will be divided into tiers like every other raw material.

High throughput and low speed can be used for the free tier. The middle tier is $3 per million tokens, and the next tier is $6. Larger models, faster speed, longer context: higher price. A premium model may be $150 per million tokens. If, as a researcher, you spend 50 million tokens a day for $150, that is not much.

The move from Hopper to Grace Blackwell dramatically increased throughput in the free tier, while increasing throughput by 35x in the most heavily monetized segment. Vera Rubin raises throughput across all tiers and increases it 10x in the highest-ASP, most valuable segment.

If you assume that 25% of the data center is allocated to the free tier, 25% to the middle tier, 25% to the advanced tier, and 25% to the premium tier, Blackwell generates 5 times more revenue, and Vera Rubin generates another 5 times from there. You need to transition to Vera Rubin as quickly as possible.

This is why we combined Groq. Groq's deterministic dataflow processor is statically compiled and scheduled by the compiler. It is designed with large SRAM and specialized for one workload only: inference. With software called Dynamo, we fully disaggregated inference. Work suited to high throughput is handled by Vera Rubin, while low-latency, bandwidth-limited decode generation is handled by Groq.

In the region above 1,000 tokens per second, where NVLink 72 reaches its limit, Groq moves beyond that limit. If the workload is centered on high throughput, keep it 100% Vera Rubin. If there is a lot of coding or high-value engineering token generation, add Groq to about 25% of the data center. Samsung is producing the Groq LP 30 chip, and it is scheduled to ship in the third quarter. In two years, a 1-gigawatt factory goes from 2 million to 700 million in token generation rate, a 350x increase.

Part 12: Roadmap - Feynman, Rosa, and Even Space

Vera Rubin to Rubin Ultra to Feynman, DSX Factory Platform, and Space Data Centers

Blackwell is the current generation. Rubin has the Oberon system, and it will always be backward compatible. Oberon is copper scale-up, and it can expand to NVLink 576 with optical scale-up. NVIDIA does both copper and optical. Kyber's NVLink 144 and Oberon's NVLink 72 plus optics implement NVLink 576.

Rubin Ultra has a new chip and LP 35. LP 35 integrates NVIDIA's NVFP4 arithmetic structure for the first time and provides severalfold speed improvements. The next generation is Feynman. It includes a new GPU, a new LPU called LP 40, a new CPU called Rosa (short for Rosalind), BlueField 5, and CX 10. Feynman scales up with both copper and co-packaged optics. A completely new architecture every year.

NVIDIA has transformed from a chip company into an AI factory company, an AI infrastructure and computing company. We now build the entire AI factory. Too much power is wasted in this factory. We created Omniverse, a platform where every component meets virtually to design gigawatt-scale AI factories.

The NVIDIA DSX platform: DSX-M for mechanical, thermal, electrical, and network simulation; DSX Exchange for AI factory operations data; DSX Flex for dynamic power management between the grid and data centers; and DSX Max Q for dynamically maximizing token throughput. We work with ecosystem partners including Siemens, Cadence, Dassault, Jacobs, and PTC. I am convinced that 2x efficiency is hidden here.

And we are going to space too. Thor has been radiation-certified and is installed in satellites. In the future, we will build data centers in space as well. A new computer called Vera Rubin Space One will go to space. In space there is no conduction or convection, only radiation, so cooling is difficult, but excellent engineers are solving it.

Part 13: The Open Claw Revolution - The Operating System for the Agentic Era

Open Claw, Nemo Claw, the Nemotron Federation, and the Enterprise AI Renaissance

Peter Steinberger created software called Open Claw. Open Claw has become the most popular open-source project in human history, surpassing in only a few weeks what Linux achieved over 30 years. That is how important it is. If you enter a command in the console, it finds and downloads Open Claw and creates an AI agent. From then on, you can make it do anything.

Let me explain what Open Claw is. It is an agentic system. It connects to and calls large language models. It manages resources, accesses tools, accesses the file system, schedules tasks, runs cron jobs, breaks prompts into steps, and can call subagents. It also has IO, so it can communicate in any modality, send messages, and send email. In fact, it is an operating system. Open Claw open-sourced the operating system for agentic computers.

Just as Windows made personal computers possible, Open Claw makes personal agents possible. The question for every enterprise, every software company, and every technology-company CEO is this: what is your Open Claw strategy? Just as you needed a Linux strategy, just as you needed an HTTP/HTML strategy, just as you needed a Kubernetes strategy, every company now needs an agentic systems strategy.

But there is one caution. Agentic systems inside corporate networks can access sensitive information, execute code, and communicate externally. They can access employee information and financial information and send it outside. Of course that cannot be allowed. So, with Peter, we gathered the world's best security experts and made Open Claw into a form that guarantees enterprise security and privacy. NVIDIA's Nemo Claw reference design and Open Shell are integrated, with a policy engine, network guardrails, and a privacy router.

This is NVIDIA's open model initiative. We are now at the frontier in every domain of AI models: Nemotron for language, Cosmos for world foundation models, Groot for general-purpose robotics, Alpamayo for autonomous driving, BioNemo for digital biology, and Earth 2 for AI physics. Every model ranks first on the leaderboard, and Nemotron 3, in particular, is among the top three models in Open Claw.

Today we announce the Nemotron Alliance. Incredible companies are joining us, including Black Forest Labs, Cursor, LangChain, Mistral, Perplexity, Reflection, Sarvam, and Thinking Machines. Every enterprise software company will transition to agentic. Every SaaS company will become a GaaS company, Generative as a Service. This is a renaissance in enterprise IT. A $2 trillion industry will become a multi-trillion-dollar industry, and it will not only provide tools, but rent agents for specialized domains.

In the future, every engineer at our company will need an annual token budget. If base compensation is hundreds of thousands of dollars, we will add about half of that in tokens to give them 10x productivity. In Silicon Valley, this has already become a new recruiting tool: 'How many tokens come with my role?' The Open Claw event is as big as HTML, as big as Linux.

Part 14: Physical AI and Robotics, from Autonomous Driving to Olaf

Robotaxi-ready platforms, humanoid robots, Disney Olaf

Agents perceive, reason, and act. Most of the agents we have discussed so far are digital agents operating in the digital world. But for a long time, we have also been working on physically embodied agents, robots. The AI they need is Physical AI.

There are 110 robots on display at this show, and almost every company building robots is working with NVIDIA. The training computer, the computer for synthetic data generation and simulation, and the robotics computer inside the robot, three computers and the full software stack, are ready.

The ChatGPT moment for autonomous driving has arrived. We now know that we can successfully drive cars autonomously. Today we announce four new partners: BYD, Hyundai, Nissan, and Geely. Together they produce 18 million vehicles annually. They join existing partners Mercedes, Toyota, and GM, and we have also entered into a large partnership with Uber to connect robotaxi-ready vehicles to the network in multiple cities.

With robotics companies such as ABB, Universal Robotics, and KUKA, we are integrating Physical AI models into simulation systems and deploying them on manufacturing lines. Caterpillar is with us, and so is T-Mobile. The base station of the future will be NVIDIA Aerial AI RAN, a robotic radio tower that reasons about beamforming and saves energy.

And one of my favorites: Disney's robot. It was co-developed with Disney and DeepMind using NVIDIA Warp on top of the Newton physics solver. Olaf learned to walk inside Omniverse and became able to adapt to the physical world. Imagine the Disneyland of the future, with character robots walking around.

Part 15: Closing, the Future of GTC

A wrap-up of the inference inflection, AI factories, the Open Claw revolution, and Physical AI

Usually, I end by summarizing what we talked about today. We talked about the inference inflection, AI factories, the agent revolution called Open Claw, and Physical AI and robotics.

Computing exploded. From CNN to Open Claw, agents operate around the world, but to handle that power, you need computing. So we solved the problem. We increased computing by 40 million times. If training was the old paradigm, inference now runs the world. Vera shows who is boss by reducing costs 35x, and Blackwell makes tokens sing. It is the king of inference.

DSX and Dynamo turn power into revenue, and Nemo Claw keeps agents from going off course by saying, 'Absolutely not.' And yes, this is open source. Thinking cars and running robots. This is not a movie. It has already begun. We create a new architecture every year because the agents keep shouting, 'More tokens!'

Have a great GTC, everyone! Thank you!

Jensen Huang GTC 2026 Keynote

Full Text of NVIDIA Founder & CEO Jensen Huang's Keynote

GTC 2026 · San Jose, California

Part 1: Opening & Welcome to GTC

GTC 2026 Keynote Begins

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 gonna talk about technology.

We're gonna talk about platforms.

MVD has 3 platforms.

You think that we mostly talk about one of them.

It's related to Cuda X.

Our systems is another platform, and now we have a new platform called AI factories.

We're gonna talk about all of them.

And most importantly, we're going to talk about ecosystems.

But before I start, let me thank our pregame show hosts.

I thought they did a great job.

Sarah go of conviction.

Alfred Lim, Sequoia capital, NVIDia's 1st venture capitalist, Gavin Baker.

Nvidia's 1st major institutional investor.

These 3 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 wanna 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 rich ecosystem.

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

I want to thank you, a 1000 Technical sessions.

2000 speakers.

This is, this conference is gonna cover every single layer of the 5 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 gonna take, get this industry taken off is all of the applications.

Part 2: 20 Years of CUDA, the Foundation of Accelerated Computing

CUDA platform's 20-year journey and flywheel effect

What it all began, it all began here.

This is the 20th anniversary of Kuda.

We've been working on Kuda for 20 years.

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

We recently added tiles so that we could help people program, tensor cores, and these 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 100,000 public projects. Kuda literally is integrated into every single ecosystem.

This chart basically describes 100% of invidious 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 100s of 1000000s of GPUs and computing systems around the world that run Kuda.

We are in every cloud, where in every computer company, We serve just about every single industry.

The install base of Kuda 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 build new ecosystems around them, with other companies that join, which creates a larger install base.

This flywheel, this flywheel, is now accelerating, the number of downloads of NVIDA 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 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 Kuda.

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 Ampire that we shipped them some 6 years ago, the pricing of Ampire 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 cost, which ultimately encourages new growth.

So, Kuda is at the center of it.

Part 3: From GeForce to RTX and Neural Rendering

From programmable shaders to DLSS 5

But our journey that could actually started 25 years ago.

G Force.

I know how many of you grew up with G-force.

G Force is Invidia's greatest marketing campaign.

We attract future customers.

starting long before you could afford to pay for it yourself.

Your parents paid.

Your parents paid, your parents paid for you to be NVIDA 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, this is the house that G Force made.

25 years ago, we started our journey, which led to Kuda.

25 years ago we invented the programmable shader.

A perfectly unobvious invention to make an accelerator programmable, the world's 1st programmable accelerator, the pixel shader.

25 years ago, that led us to explore further and further, 20 years later, 5 years later, the invention of Kuda.

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 Kuda on the backs of G-Force to every single computer.

We dedicated ourselves to create this platform because we felt so much, 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 Kuda installed everywhere.

The pixel shader, led to, of course, the revolution of G-force.

And then 10 years ago, we 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.

G-force brought Kuda to the world.

G-force,

Therefore, enabled Alex Khrushchevsky and Ilias Suscover and Jeff Hinton, Andrew Ang, and so many others, to discover that the GPU could be their friend in accelerating deep learning.

It started the bink bang of 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 10 years ago, we thought that AI would revolutionize computer graphics.

Just as G-force 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 DLSS 5.

Take a look at it.

Is that incredible?

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 combine 3D graphics, structure data with generative AI, probabilistic computing.

One of them is completely predictive, the other one probabilistic, yet highly realistic.

We combine these two ideas.

Combine these two ideas.

control through structure data, control 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.

Part 4: Structured and Unstructured Data, cuDF & cuVS

Data processing revolution and cloud partnerships

Well, this is gonna scare you a little bit.

I'm going to flip the slide, and don't gasp.

So we're gonna go through the schematic for the rest of the time.

This is my best light.

Every time I asked my, I asked the team, what's my best light?

Repeatedly, this was 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 this is structured data.

You've heard of it, sequel, spark, pandas, Velox, some of these really, really important, very large platforms, snow, snowflake, data bricks, EMR, Amazon, EMR, um, Azure, fabric, Google Cloud, Big Query.

All of these platforms are processing data frames.

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

This is the structure data, the ground truth of 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 structure 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 gonna 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 is 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 multimodality, Perception.

You can and understanding.

You can use that same technology, multimodality, perception, and understanding, to go read a PDF, to understand its meaning.

And from that meaning, embedded into a larger structure that we can search into.

We can query into.

NVIDA created 2 foundational libraries, just like we created RTX for 3D graphics.

We created QDF for data frames, structure data.

We create a 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 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 of them.

IBM, the inventor of sequel, one of the most important domain specific languages of all of all time, is accelerating Watson X data with QDF.

Let's take a look at it.

60 years ago, IBM introduced the system 360.

The first modern platform for general purpose computing, launching the computing era, then sequel, 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 Watsonx.datasequel 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.

They're ordered to cash data market, aggregates every supply, order, and delivery event, across global operations in 185 countries.

On CPUs, Nestle refreshed the data mart a few times a day.

With accelerated Watsonx.data, running on NVIDA 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.

NVID accelerates data processing on the cloud.

We also accelerate data processing on prim.

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 speedup.

This is cloud, 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 big query, really important uh, framework and really important platform, and this is an example of our work together with Snapchat where we reduce 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.

Part 5: Accelerated Computing Platform, Vertical Integration and Horizontal Openness

NVIDIA's business model and cloud partnerships

It was originally called Moore's law.

Moore's law was about getting performance doubling every couple of years.

That'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 NVID 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 could see this pattern I just mentioned.

I just wanted to show you 3 versions of it.

Nvidia built the accelerated computing platform.

Has a bunch of libraries on top.

I gave you 3 examples.

RTX is one 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 and 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, and video with Google Cloud.

We accelerate vertex AI, we excel a big query.

We accelerate, I'm super proud of the work that we've done with Jackson, XLA.

We are incredible on PyTorch.

We're the only accelerator in the world that's incredible on Pie Torch and incredible on Jackson XLA.

And the customers that we support.

The base tens, the crowd strikes, puma, sales force.

They're not our customers.

But they're customers, developers of ours that we've integrated the NVIDA 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 gonna 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 AWS.

We've been working with AWS a long time.

And one of the areas, one of the, one of the things I'm super excited about this year, is we're gonna bring open AI to AWS.

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

It's going to expand the reach, expand the compute of open AI.

And as you know, they are completely compute constrained.

And so AWS, we accelerate EMR, we accelerate sage maker, we accelerate bedrock, NVIDI is integrated really deeply into AWS.

They were our 1st cloud partner.

Microsoft Azure.

Nvidia's A100 supercomputer, um, was the, the 1st one we built was for Nvidia, the 1st one we installed was at Azure, and that led to the, the, uh, 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 bing 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.

Confidential computing and VS GPUs.

the 1st ones in the world to do that.

It's now able to support confidential computing and protected deployment of these very valuable open AI models and anthropic models throughout clouds and different regions and all because of our confidential computing.

Confidential computing super important.

And here's an example, where we have different customers that we work with.

Synopsis, a great partner of ours, who are accelerating all of their EDA and CAU workflows.

And then we land it at Microsoft Azure.

We were Oracles, first AI customer.

Most people would have thought we were their 1st supplier.

We were their 1st supplier also, but we were their 1st AI customer.

I'm quite proud of the fact that I explained AI clouds to Oracle for the first time.

And we were their 1st customer.

Since then, they've really taken off.

We've landed a whole bunch of our partners there.

coherent fireworks, and of course, very famously open AI.

A great partnership with court, court, court weave.

They're the world's 1st AI native cloud, a company that was built with only one singular purpose, to provision, to host GPUs, the era of accelerated computing showed up, and the 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 Palantier and Dell.

The 3 of our companies have made it possible to stand up a brand new type of AI platform, the Palanteer 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 at 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 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.

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 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 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, unprim 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 NVIDIO is horizontally open.

We'll work and integrate ambidious 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.

Part 6: Industry-Specific AI Applications & CUDA X Libraries

All-direction AI for automobiles, healthcare, robotics, quantum computing, and more

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 traitors.

Guys.

Here's, here's, 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 work, 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 onto 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 those, autonomous vehicles, our reach, our breath, 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, 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.

Healthcare is going through their chat GPT moment, some really exciting work that we're there.

We have a great keynote track here.

We have a great keynote track, Kimberly Powell's doing the 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, diagnosis.

And of course, physical AI, robotic systems, all these different vectors of AI have different platforms that NVIDI provides, industrial.

We are completely resetting and starting the largest buildout 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, live games and live video, enormous amount of it will be augmented with AI.

We have a, we have a platform called holoscan.

Quantum.

There are 35 different companies here, building with us the next generation of quantum GPO hybrid systems, retail and CPG, using NVIDO for supply chain, using creating a gentic shopping systems.

AI agents for customer support.

A lot of work being done here.

$35 trillion industry, robotics, $5000000000000 industry and manufacturing.

NVIDA has been working in this area for a decade now, building 3 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, which is base station.

is going to be an AI infrastructure platform in the future.

AI will run at the edge.

And so lots of, lots of great, um, uh, great, uh, discussion there in our platform there is called aerial, our A, Iran, 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, are Cuda X libraries, is the algorithm, the algorithms that NVIDA invents, we are an algorithm company.

That's what makes us special.

That what 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 are announcing 100, 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 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, coup DNNN, 'Couda deep neural networks.

It completely revolutionized artificial intelligence caused a big bang of modern AI.

Let me show you a short video about 'Couda X.

20 years ago, we built 'Cuda.

A single architecture for accelerated computing.

Today, we've reinvented computing.

A thousand coup de X libraries helped developers make breakthroughs in every field of science and engineering.

Co opt for decision optimization.

Culetho for computational lithography.

coup DSS for direct sparse solvers.

coup equivariance, for geometry aware neural networks.

Aerial for AI RAM.

Warp for differentiable physics.

Pair of bricks for genomics.

At their foundation are algorithms, and they are beautiful.

Everything you saw was a simulation.

Some of it was principled 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 MVIDIA does.

It is through the... connection of understanding of the algorithms. With our computing platforms that were 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 coup to X.

Part 7: AI-Native Companies and the Arrival of the Inference Inflection

ChatGPT, inference AI, Claude Code, a 1-million-fold increase in computing demand

Well, just now you saw a whole bunch of companies.

You saw Walmart, and, you know, there's L'Oreal, and incredible companies, established companies, JP Morgan, and Roche, and these are companies, in companies that define society 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.

I couldn't, this is just a little tiny, tiny bit of it.

And, um, I couldn't decide whether to show you more, 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 like, for example, you might have heard a couple of them, open AI, 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 2 years.

particularly this last year.

We've been working with the AI natives for a long time.

And this last year, it just skyrocketed.

Now 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 investments went from millions of dollars, 10s of millions of dollars, to 100s of millions of dollars, and 1000000s of dollars.

And the reason for that is this is the 1st 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 tokens that are available, created by anthropic and open AI, 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 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 in 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 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 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, and we've been advancing this field for quite some time.

But why the last 2 years?

What happened in the last 2 years?

Well, 3 things.

Chat GPT, 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, everybody in the world should be using ChatGPT.

I know I use that every single morning, use the planning this morning.

And so ChatGPT was the generative AI, the era.

The second, by the way, generative, generative computing, versus the way we used to do computing, it's not, it's 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 going to change how computers are architected, how computers are provided, how computers are going to be built out, and what is the meaning of computing altogether.

Generative AI.

2023.

End of 22, 2023.

The next reasoning AI, 01.

Which, and then took off with 03.

Reasoning allowed it to reflect, allows it to think to itself, allowed it to plan, break down, 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, 01 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.

You know, of course, you could have much larger models, the model, 01 was a little bit larger, not much larger, but it's input token usage for context.

And its output token for thinking.

Increase the amount of computation tremendously.

Then came clock code.

The 1st 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 3 of them, clyde 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.

Claude code completely revolutionizes the new inflection.

And for the 1st time, you don't ask it, AI, what, when, how?

You ask it, create, do, 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.

And AI, that was able to perceive, became 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 2 years, we know that everybody in this room knows, the computing demand for NVIDA 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 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.

its 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 last 2 years, the computing demand, 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 100 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 startup has, it's the feeling that open AI has, is the feeling that anthropic has.

If they could just get more capacity, they could generate more tokens, the revenues would go 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 we have reached that moment.

The inflection, the inference inflection has arrived.

Part 8: $1 Trillion in Demand, the Future of AI Infrastructure

At least $1 trillion in computing demand projected by 2027

Last year, at this time, I said, that, where I stood, at that moment in time, we saw about 500 billion dollars.

We saw $500 billion.

of very high confidence demand and purchase orders.

For Blackwell and Ruben 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 one trillion dollars.

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 gonna be short.

I am certain, computing demand will be much higher than that.

And there's a reason for that.

So the 1st thing is, um, we did a lot of work in the last year.

Of course, as you know, 2025 was MVIDIO'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 NVIDious 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.

And video systems are the lowest cost infrastructure you could get for AI infrastructure in the world.

And so the 1st 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, 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 NVIDA, 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, in computer graphics, computer vision, and speech, proteins and chemicals, robotics, and otherwise, edge or cloud, any language,

Invidious 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 $100000000 is an enormous amount of infrastructure.

You have to have complete confidence, that the $100000000000 you're putting down, will be used, would perform well, would be incredibly cost effective, and have useful life for as long as you could see.

That infrastructure investment you could make on NVIDA, 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 conference.

You want to put it in any of the clouds.

We're delighted by that.

You want to put it on prim.

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.

Part 9: The King of Inference Performance, the Cost-per-Token Revolution

Blackwell's 35x performance improvement, Token King, and the AI factory concept

60% of our business is hyper scalers, the top 5 hyper scalers.

However, even within that top 5 hyper scalars, some of it is internal AI consumption.

The internal AI consumption really important work, like Rexus 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 hyper scale workloads are now moving, shifting towards a workload that NVIDA 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, enterprise, industrial, robotics, edge, big systems, supercomputing systems, small servers, enterprise servers, the number of systems, incredible.

The diversity of AI is also its resilience.

The span of reach of AI is its 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, 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 envy linked by 8, had to be taken to the next level.

We completely rearchitected 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, completely changed.

Grace Blackwell, MVLing 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.

Envy link 72.

MVFP4, not just FP4, red 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 and performance and energy efficiency.

We've also been able to use MVFP for training.

So, MVLink 72, MVFP4, the invention of dynamo, tensor RTLLM, 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 1000000000s of dollars of supercomputing capability, help us create the kernels, the software that made inference possible.

Well, the results all came together.

And people told, 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.

of AI that has 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 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 that, you want to be on top of that curve as high as you want.

This, the X axis, is the introactivity, 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.

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.

where I torture all 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%, 2 times, Moore's law would probably give us one.5 times more performance.

You would have expected from Hopper H200, one.5 times higher.

Nobody would have expected 35 times higher.

I said last year.

At this time, that Invidious, Grace Blackwell, Embling 72 was 35 times per 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.

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 have to build a gigawatt data center.

You still have to big up, build a gigawatt factory, and that gigawatt factory for 15 years, amortize the cross.

That gigawatt factory is about $40 billion.

Even when you put nothing on, it's $4000000000 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.

NVDS token cost is world-class.

Basically untouchable at the moment.

And the reason that's true is because of extreme code design.

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

There was a monkey king, Tolken 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 100 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 5000, 7 times higher.

And so this is the incredible power of extreme codesign.

I mentioned earlier the importance 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 is 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, that 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 gonna be thinking about their token factory effectiveness.

This is your factory in the future.

Part 10: Vera Rubin Architecture, Designed for Agentic AI

From DGX-1 to Vera Rubin, MVLink 72, liquid cooling, Rubin Ultra

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 NV link, 170 terra flops in one computer, the world's first computer designed for AI researchers.

With Volta, we introduced MV Link 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 Melonox joined NVIDA.

In 2020, DGXA 100 SuperPod became the first GPS supercomputer, combining scale up and scale out architecture.

NV Link 3 for scale up, Connect X6, and Quantum Infiniban for scale out.

Then Hopper, the first GPU with the FP8 transformer engine that launched the generative AI era.

MVLink 4, Connect X 7, Bluefield 3, DPUs, Second generation Quantum Infiniban.

It revolutionized computing.

Blackwell redefined AI supercomputing system architecture with MV Links 72, 72 GPUs connected by MV Links 5, 130 terabytes per second of all 12 bandwidth.

Compute trace integrate Blackwell GPUs, Grace CPUs, Connect X 8, and Bluefield 3.

Scaleout 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.

And now, Vera Rubin.

Architected for every phase of agentic AI, advancing every pillar of computing, including CPU, storage, networking, and security.

Vera Rubin, MV Link 72.

3.6 exa flops of compute, 260 terabytes per second of all to all NV link bandwidth,

The engine supercharging the era of agentic AI, the Vera CPU rapid.

Designed for orchestration and agentic workblues.

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 GROC 3 LPX wrapper.

Tightly connected to Vera Rubin, Grock's LPU's 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 ships, five rack scale computers, one revolutionary AI supercomputer, for agentic AI, 40 million times more compute, and just 10 years.

Now, in the good old days, when I would say, Hopper, I would hold up a chip.

That's just adorable.

This is Vera Reuben.

When we think ver...

When we, when we think Vera Rubin, we think the entire system, vertically integrated, completely with software, extend it end to end, optimized as one 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 cash, structured data, QDF, unstructured data, CVS,

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 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 that uses LPDDR 5, LPDDR 5, and incredible single thread performance, and performance per want 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.

Oh, no, Vero Rubin, where is it?

Here it is.

Okay?

So this is the Vera Rubin system.

Notice, since the last time, 100% liquid cooled, all of the cable's gone.

What used to take, what used to take, 2 days to install now takes 2 hours.

Incredible.

And so the manufacturing cycle time is going to dramatically reduce.

This is also a supercomputer that is cooled by, it's cooled by hot water, 45 degrees, which takes the pressure off of the data center, takes all of that cost and all of that 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 6th, 6th generation scale up switching system.

This is not ethernet.

This is not Infiniban.

This is MVLink.

This is the 6th generation MV link.

This is insanely hard to do well.

It is insanely hard to do, period, and I'm just super proud of the team.

Envy Link, completely cool.

This is the brand new Grock system.

And I'll show you a little bit more about it.

This system, 8 guac chips, this is the LP 30.

The world's never seen it.

Anything that the world's ever seen, is V1.

This is 3rd generation.

And we're in volume production now.

And I'll show you more about that in just a second.

The world's 1st CPO, Spectrum X Switch.

This is also in full production.

co-packaged optics.

Optics comes directly onto this chip.

Interfaces directly to silicon.

Electrons gets translated to photons, and it gets directly connected to this chip.

We invented the process technology with TSMC.

where the only one in production with it today.

It's called Coop.

It's completely revolutionary.

NVIDA is in full production.

with Spectral Max.

This is the VERA system.

Twice the performance per watt of any, any CPUs in the world today.

It is also in production.

Well, you know, we never, we never thought we would be selling CPUs, standalone.

Um, we are selling a lot of CPU standalone.

This is already for 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 CX9 powered with Vera CPU, the Blue Field 4 STX, our new storage platform.

Okay?

So these are the four, these are the racks.

And it's connected.

Each one of these racks, the envy link rack, this is, I've shown you guys this before.

It's just super heavy.

It seems to get heavier every year.

Because I think there's just more cables in there every year.

And so this is the MVLink rack.

We've also taken this technology because it is 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, um, Ruben Ultra?

So this is the Ruben Ultra Compute node.

Unlike Ruben, that slides in horizontally, Ruben Ultra goes into a whole new rack, it's called kiber that enables us to connect 144 GPUs in one MVLink domain.

And so the kyber rack, this, I, I could lift it, I'm sure, but I won't.

It's quite heavy.

This, this is one compute node, and it slides into the kyber rack vertically.

This is where it connects into.

This is the midplane.

The kyberax, those 4 top envy link connectors slide in and connect into this, and this becomes one of the nodes.

And each one of these whacks is a different compute node.

And this is the amazing part.

This is the midplane.

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 MV link.

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, 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, this is what happens when you don't practice.

Okay, all right, so, um, you saw, you, take your time.

Just don't get hurt.

You saw, you saw this slide.

Part 11: Token Factory Economics, the New Formula for Revenue

Token layering, Grock integration, disaggregated inference, 350x performance improvement

You know, only on video'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 is worthwhile to tell you again.

This is probably the single most important chart for the future of AI factories, and every CEO, every CEO in the world will be tracking it, will be studying it very deeply.

It's much, much more complicated than this.

is multidimensional, 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 going to 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 100,000 tokens, input length to maybe 10000s, 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 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 1st tier could be $3 per 1000000 tokens.

The next tier could be $6 per million 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 50000000 tokens per day as a researcher at $150 per 1000000 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 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 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, reset this.

And this is Vera Rubin.

Okay?

Now just think, 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 10 X.

That is the hard work.

This is incredibly hard to do out here.

This is the benefit of Eddie Link 72.

This is the benefit of extremely low latency.

This is the benefit of extreme codesign 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 into 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 track more 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.

Now, 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 rock.

Okay?

And so we acquired the team that worked on the grock 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, envealing 72 is so game changing, it is exactly the right architecture, and it's even hard to beat, even as you add groc to it.

However, if you extend it, this chart, way out here, and you said you wanted to have services that delivers not 400 tokens per second, but a 1000 tokens per 2nd, All of a sudden, envy link 72 runs out of steam and simply can't get there.

We just don't have enough bandwidth.

And so this is where Grock comes in.

And this is what happens when we push that out.

So, it goes out beyond, thank you.

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, Vera Rubin is 5X.

If most of your workload is high throughput, I would stick, which is 100% vera Ruben. If a lot of your workload wants to be coding and very high valued engineering, token generation, I would add groc to it.

I would add grock 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 Grock to Vera Rubin and extend its performance and extend its value even more.

This is what happens.

This is a contrast.

The reason why 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 when the date, when to do the compute, the computer and data arrives at the same 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, this 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 disintegration is going to get even higher.

And so these are two extreme processors you could see.

One chip, 500 megabytes.

One vera Ruben chip, one Ruben chip, 288 gigabytes.

It would take a lot of rock chips to be able to hold the parameter size of Reuben as well as all of the context that has to go, the KV cache that has to go along with it.

So that limited Grock'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 Vera Rubin, and then offload the decodeneration, the low latency, the bandwidth limited challenged part of the workload for Grock.

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, Grock, we're just gonna add a whole bunch of Grock 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 grock chips. However, it sits next to Mvidia Vera Rubin, where we could we could hold the massive amounts of KV cash that's necessary in processing all of these agentic AI systems.

It's based upon this idea of this aggregated inference.

We do the prefill.

That's the easy part, but we also tightly integrate the decode.

So, the attention part of decode is done on NVIDious Verrubin, 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 Vera Rubin, on the ground ship.

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 grog.

The Vera Rubin systems, including rock.

I want to thank Samsung, who manufactures the Grock LP 30 Chip for us, and they're cranking as hard as they can.

I really appreciate, appreciate you guys.

We're in production with the Grock chip, and, uh, you know, we'll ship it in the second half, probably about Q3 time frame.

Okay?

Grock LPX.

Part 12: Roadmap, Feynman, Rosa, and Beyond

Vera Rubin → Rubin Ultra → Feynman, AI factory platform DSX, space

Vera Reuben?

You know, it's kind of hard.

It's kind of hard to imagine any more customers.

You know, and, and to, the, the really great thing is, is, um, Grace Blackwell, early sampling of it was really complicated because of coming together, EmmyLink 72, but the sampling of Vero Rubin is just going incredibly well.

And in fact, Satya, I think, texted out already, that the 1st Vera Ruben Rack is already up and running at Microsoft Azure.

And so I'm super excited for them.

We're going to just keep cranking these things out.

We have now set up a supply chain that could 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 Vera Rubin racks while we're cranking out the GB 300 racks.

We are in full production.

The various CPUs.

Incredibly successful.

And the reason for that is because AI needs CPUs for tool use, and VERA CPU was designed just perfectly for that sweet spot.

Incredible for the next generation of data processing.

Vera CPU is ideal.

The Vera CPU plus blue plus CX9 connected into the blue field force stack.

100%.

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 just have humans using SQL.

Now we're going to have AIs using these storage systems, and it's going to store QDF accelerated storage, QVS accelerated storage, as well as very importantly, KV cashing.

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 X factored, the number of flops, we would X factored, the number of amount of bandwidth, but with this architecture, we're going to take our token generation rate from 2 million.

to 700 million, 350 times increase.

This is, this is the power of extreme code 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 Ruben, we have the Oberon system, we're always backwards combatible 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, opticals 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 gonna do both.

So, we're gonna have MBLink 144 with kiber.

And then with Opturon, Opturon.

Oberon, we're going to EnvyLink 72 plus optical to get to EnvyLink 576.

The next generation of Ruben, with Ruben Ultra, we have the Ruben Ultra Chip, which is which is taping out, and we have a brand new chip, LP 35.

LP 35 will, for the 1st time, incorporate invidious MVFP4 computing structure.

Give you another few X factor speed up.

Okay?

And so this is Oberon, MVLink 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 Feyman.

Feyman has a new GPU, of course.

It also has a new LPU.

LP 40.

Big step up, incredible, incredible new technology.

Now, uniting the scale of NVIDIA and the GROC team, building together, LP 40,

It's going to be incredible.

A brand new CPU called Rosa.

Short for Rosalind.

Bluefield 5, which connects the next CPU with the next Superneck, CX 10.

We will have kiber, which is copper scale up, we will also have kiber, CPO scale up.

So for the 1st 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 going to scale out optical?

Yes.

And so for everybody who's 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 Feyman will have all of that.

Let me see if I missed everything.

That's it.

Every single year, brand new architecture.

Very quick.

Very quickly.

Nvidia went from a chip company to a AI factory company or 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 AF factories come together, design 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 omnivorse.

And the omnivorous DSX world, a platform where all of us can meet and design these giga factories, giga, you know, 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 2 in here.

And a factor of 2 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.

is exactly the same way.

Let's show it to you.

The greatest infrastructure buildout in history is underway.

The world is racing to build chip, system, and AI factories, and every month of delay cost 1000000000s 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 omnivorse digital twin blueprint for designing and operating AI factories for maximum token throughput, resilience and energy efficiency.

Developers connect through several APIs.

DSXM for physical, electrical, thermal, and network simulation, DSX exchange, for AI factory operational data.

DSXFlex, for secure, dynamic power management between the grid, and DSX Max Cube to dynamically maximize token throughput.

It starts with sim ready assets from MVIDIA and equipment manufacturers.

Managed by PTC Windshill PLM.

Then, model based systems engineering is done in Daso 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+ for external thermals.

Cadence Reality for Internal, E tap for Electrical, an 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 to 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?

Omnivorse, omnivorse was designed to hold the world's digital twin, starting from the Earth, and it's gonna hold digital twins of all sizes.

And so we have just such a great ecosystem of partners.

I wanna thank all of you.

All of these companies are brand new to our world.

We didn't know many of you, just a couple 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.

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 have, 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.

Part 13: The Open Claw Revolution, the Operating System of the Agentic Era

Open Claw, Nemo Claw, enterprise AI, Nemotron Alliance

Let me talk to you about something new.

So, so, um, Peter Steinberger's here, and, um, he wrote a piece of software.

It's called Open Claw.

And, and, um, I don't know if he realized, uh, how successful it was gonna be, um, but the importance is profound.

Open claw is the number one, is 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,

Uh, 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 type it, this into a into a console.

And um, it goes out, it finds open claw, it downloads it, it builds you an AI agent.

And then you could tell it, whatever else you need to do.

Okay?

So let's take a look.

An open source project just dropped.

Andre Carpathy has just launched something called... No research is a huge deal.

You give an AI agent a task.

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 beer, connected the machine via Bluetooth to open claw.

And then we automated everything, including the whole website for people to order.

The lobster lag of here.

Hundreds of people are queuing up for lobsters in San Jed.

Open claw.

Open cloud, open cloud.

You want to build open claw with open claw.

Everyone is talking about open clock, but what is open clock?

Believe it or not, there's already a claw con.

Incredible.

Incredible.

Now, I illustrated effectively what open claw is in this way.

And so all of you can understand it, but let's just think what happened.

What is open claw?

It connects, it's an agentic system.

It calls and connects to large language models.

So the first thing it has, it has resources that it manages.

It could access tools, it could access follow systems, it could access large language models.

It's able to do scheduling.

It's able to do crime jobs, it's able to decompose a problem that a prompt that you gave it into step by step by step.

It could spun off and call upon other subagents.

It has IO.

You could talk to it in any modality you want.

You could wave at it and understand you.

You could talk to any modella you want.

sends you messages, it texts you, sends you email.

So it's got IO.

Um, what else does it have?

Well, based on that, you could, you could say, in fact, it's an operating system.

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

Open claw has open sourced.

essentially, the operating system of agentic computers.

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

Now, open claw has made it possible for us to create personal agents.

The implication is incredible.

The implication is incredible.

First of all, the adoption says something, you know, 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 need it to have HTTP HTML strategy, which started the internet.

We all needed to have a Kubernetti strategy, which made it possible for mobile cloud to happen.

Every company in the world today needs to have an open clause strategy, an agentic system strategy.

This is the new computer.

Now, this is just the exciting part.

This is Enterprise IT before open claw.

You know, 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, you know, 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 gonna look like.

This is the extraordinary part.

Every single IT company.

Every single company, every SAS company,

Every SAS company will become a, A gas company.

No question about it.

Every single SAS company would becoming a gas company, and agentic as a service company.

And what's amazing is this, you now open claw gave us, gave the industry exactly what it needed, at exactly the time.

Just as Linux gave the industry, exactly what it needed, exactly the time, just as Kubernini 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, access, apply access, finance, information, access, 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 to make open claw.

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 tool kits, and the 1st part of it is technology we call open shell that has now been integrated into open claw.

Now, it's enterprise ready.

This stack, this stack, with a reference design we call Nemo Klaw, Nemo Klaw, okay, with a reference stack we call Nemo Klaw, you could download it, play with it.

And you could connect to it, the policy engine of all of the SAS 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 policy, it has a network guardrail.

It has a privacy router.

And as a result, we could protect and keep the clause 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 claw, custom claws, is so that you can have your custom models.

And this is invidious open 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, Bio Nemo, for digital biology, Earth 2, 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 3 million open models across language, vision, biology, physics, and autonomous systems, enable AI bills 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, and speech.

Can you hear me now?

Hello?

Yes, I can hear you now.

Cosmos.

Frontier models, for physical AI world generation and understanding.

Alpamayo, the world's first thinking and reasoning autonomous vehicle AI.

Group. Foundation models for general purpose robots. Bionema. Open models for biology, chemistry, and molecular design.

Earth 2. Models for weather and climate forecasting, rooted in AI physics.

NVIDA open models give researchers and developers, the foundation to build and deploy AI for their own specialized domains. 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.

But most importantly, it's because we are not gonna give up working on it.

We're gonna keep on working on it every single day.

Nemotron 3 is going to be followed by Nemotron 4.

Cosmos one was followed by Cosmos 2.

Groot, Groot, Generation 2.

Each and one of these will 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 Nemotron 3, in Open Claw.

This is Nemotron 3 and Open Claw, and look at the top three.

There are the 3 best models in the world.

Okay?

So we are at the frontier.

It is also true.

It is also true that we want to create the foundation model, so that all of you could fine-tune it, post-trained 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 Nemotron Coalition.

We are so dedicated to this.

We have invested 1000000000s 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 domains, the domain of the domain of the models is radically different, from biology, the physics, the self-driving cars, the 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.

To 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, Lang Chang, 1000000000 downloads for creating custom agents, mistral, the Arthur, Arthur mentioned.

I think he's here.

Incredible, incredible company, perplexity, perplexes, computer.

Absolutely use it.

Everybody use it.

It is so good.

A multimodal, agentic system, reflection, Sarvum from India, thinking machine, Mirror Marati'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 class 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.

And 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 $20000000000 industry.

This is going to become a multi-trillion dollar industry, offering not just tools for people to use, but agents that 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 gonna make a few $100,000 a year, their base pay.

I'm gonna give them, probably half of that, on top of it, as tokens so that they could be amplify 10 X.

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, Sit 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 there'll be token manufacturers for all of their customers.

The open class 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 a gentic framework that all of us could use to build our open clause strategy.

And we've created a reference design we call Nemo Cloud, Nemo Cloud, 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.

Part 14: Physical AI and Robotics, from Autonomous Driving to Olaf

Robotaxis, humanoid robots, Disney Olaf, Physical AI

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 3 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 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 chat GPT moment of self-driving cars has arrived.

We now know we could successfully autonomously drive cars.

And today we are announcing 4 new partners for invidious robotaxi ready platform.

BYD, Hyundai, Nissan, Gili, all together, 18 million cars built each year.

joining our partners from before, Mercedes, Toyota, GM, the number of robotaxi ready cars in the future are going to be incredible, and we're announcing also a big partnership with Uber.

Multiple cities, we're going to be deploying and connecting these robotaxi ready vehicles into their network.

And so a whole bunch of new cars.

We have, uh, ABB, Universal Robotics, uh, 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 T-Mobile here.

And the reason for that is in the future, that 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 it could save as much energy as possible, and increase the amount of fidelity as possible.

There are 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 Alpamayo, vehicles now have reasoning, helping them operate safely and 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.

And follow instructions.

Hey, Mercedes.

Can we 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 robots, compute is data.

Developers pre train 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, MVIDA build open source Isaac Lab for robot training and evaluation and simulation.

Newton 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 are closing the physical AI data gap.

Peritas AI trains their operating room assistant robot in Amvidia Isaac Lab, multiplying their data with Nvidia Cosmos World 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.

Foxcon fine tunes group models in Isaac Lab.

As does Noble machines.

Disney Research uses their Camino Physics Simulator in Newton and Isaac Lab to train policies across their character robots in every universe.

Ta, da, da, da, da, da, da, da, da, da, da, da, da, da, da, da, da, da, da.

Ladies and gentlemen, Olaf?

Does it? Soon coming through.

Noon works.

Wow. Omnivorse 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 tummy.

That's gonna be amazing.

And you learn how to walk inside omnivorse.

I got to walk.

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 develop with Disney, and with Deep Mind, that made it possible for you to be able to adapt to the physical world.

Check that out.

to say that.

That's how smart you are.

I miss No May.

Not a Snoclopedia.

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 gonna be taller.

I've never seen such a short snowman, to be honest.

Nope.

Hey, tell you what.

You wanna 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 close it out?

Part 15: Closing, the Future of GTC

Closing rap and farewell

But tell you what, why don't we get some friends to help us close it out?

All right, play it.

Terminating simulation.

Hello?

Anybody here?

The keynote's overall, we said, gents and map the road ahead.

AI factory's coming alive.

Agents learning how to drive from open models to robots, too.

Now we'll break it all down.

Compute exploded what we saw from CNNs to open cloth.

Agents work and cross the land.

But they need the power to meet the man.

So we solve the problem.

It was brilliant.

We multiply, compute by 40 million.

Well, once upon an AI time train was paradigm.

Sure, it talking models how, but in friends runs the whole world now.

Very shows us who's the bars at 35 times less the cost.

Blackwell makes the token singing video.

The inference king.

Yeah, factories once took years, vendors pulling racks and gears.

Built up slowly, piece by piece,

No clear way to scale this bee's, DSX and dynamo.

Know what to do.

Turning power into revenue.

Agents used to wait and see.

Now, act autonomously, but if they ever try to stray safe course, block and say, no way, Nemo claws there to guard the course.

And yes, my friends, it's open sore.

Cars that think enjoys that run, this ain't the movies.

It's all begun.

How come I old call the shots?

It's a GPT moment for the bots from Sim the streets.

Now watch them drive.

Blow your hands up.

For physical, yeah.

Not to your age, but what came before, now we built for AI.

Even more of a Reuben plus Grock, 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.

The AI stacks for all to make, so let us all eat 5, lay a cake, the moment bright.

The path is clear

'Cause open models let us hear when day does missing.

There's no dispute.

We just generate more with compute.

Robots learning without flaw, fueling the 4 scaling laws.

The future's here, won't you come and see?

Welcome all to GTC.

All right, have a great GTC!

Wave.

Thank you, everybody.

Thank you. I just met... Okay