Welcome to GTC. It is great to be here. Before we start, let us give it up for our pre-game show hosts. Smart and adorable. They spoke about AI infrastructure, sovereign AI , safety, and physical AI. They covered an enormous amount of ground this morning, and we are going to take it one step further. It is really interesting. What do L'Oréal, Revolut, Spotify, Merck, Vodafone, Zara, Porsche, Nestlé, and HSBC have in common? They are all European companies and they are all working with NVIDIA on AI. Everybody here is all activated on the AI ecosystem and working towards the AI economy. We have been working here in Europe now for 33 years. You probably do not realize that NV1, NVIDIA's first chip , NV2, and NV3, which became RIVA 128, the chip that saved our company, really, and launched our company, were all built in Europe. Our first partner was SGS-Thomson.
We manufactured a chip in Crolles, France, assembled it in Malta and shipped it around the world. Our first large customer, the largest customer of GeForce days in the beginning, was a company called MediaMarkt. Do you know them? MediaMarkt had a spectacular idea about building PCs, as you remember, and they were our largest customer. It was ELSA, a German company, that brought NVIDIA into the workstation industry in the year 2000. NVIDIA is in every single workstation all over Europe, designing cars. Everything from styling, design, engineering, simulation, all the way through to even inside the car, which a European company brought us into, Audi, in the year 2005. Then in year 2020, we started working on autonomous vehicles with Mercedes. As you could see, NVIDIA's life journey, the entire 33 years, I have spent a lot of time in Germany.
I am delighted to be back. Thank you. I am delighted to do GTC here. This last year, we started the new journey to building a new industry here in Europe, the beginnings of the AI economy. In the last year alone, we have invested nearly USD 10 billion in Europe across startups, big partnerships, small partnerships, all over Europe. This last week alone, as I journeyed from France and U.K. to Sweden, and now here in Berlin, we announced the investments of 14 startup companies. Overall, since the beginning, we now have 12,000+ partnerships across Europe, and we are all working towards one goal now. After decades of advancement in the computer industry, we are now at the beginnings of a new industrial revolution. This image was generated by a computer. It was prompted by me. Nice, simple prompt about AI powering a new industrial revolution generated this.
I gave it a few partners that I wanted to highlight, companies that we are working with, and this is an extraordinary thing. In the mind of the AI, this is what it means to build a new industry, generation of tokens, the production of intelligence, which integrates into every single industry powering a new industrial revolution. A new industrial revolution happens when the new technology affects how we build something and what we can build. How we build something and what we can build. I think in every single way, artificial intelligence will affect everything we build and, in fact, enable us to build new things. In this way, because everyone will use it, every company will be powered by it, because it is digital intelligence, the foundation of everything.
Every single person will use it, every company will be powered by it, every industry will be transformed, and new ones will be created. In this way, like the electricity, it is a new industrial revolution, and we are at the very beginnings of that. The question is, what is AI? This is a big deal. If you want to be part of creating this future, understanding what is AI is vitally important. This is really a simple framework. We were early to observe that, in fact, AI is a manufacturing industry. AI is not just software technology. It is software technology, but not just. AI, of course, runs on a computer, but the way that the computer operates is fundamentally different.
Rather than a tool that is used by us, it is now part of a machinery. An infrastructure, factories that are producing intelligence that are integrated into every single industry and application. A simple model is to think of AI as a five-layer cake. Energy powers advanced computers that are part of an AI factory that generates tokens, digital intelligence from the models that run on it, that are integrated into every application. A five-layer cake. To be part of this AI economy, you do not want to just be consuming the AI, you want to be producing the AI. We could do all of that here in Europe, and in fact, we have already started. Europe is building its AI economy and its five-layer cake represented here.
We have great partnerships, and when you look across this five-layer cake, you can recognize that in fact it is a new industry because there is growth at every single layer. We are creating jobs, creating growth at extraordinary rate. Companies here in Europe, Schneider Electric and Siemens Energy, doing incredibly well around the world. We are the computing layer. We integrate into AI factories that are now flourishing all over Europe. Startup companies like Nebius is doing incredibly well. A new company called Firebird, which is setting up a new cloud in the Central Asia region. Even Cassava in Africa. Every single telco in the future, and I predict this, every single telco in the future, with 100,000 sites representing some 100 GW of distributed telco base stations and data centers, will in the future evolve into a distributed AI network. So the AI infrastructure layer is being built out.
Which is then above that, the AI models. It is really important to recognize that long before the AI models emerge, the infrastructure has to exist. Just as before you can have an internet, you have to have the networking. Just as before you could really enjoy the capabilities of electricity, you have to have electrified houses, electrified power plants. So the models are the next layer. It is also interesting to observe that the model layer is not just language models. It represents language, biology, chemicals, physics, robotic actions, animation, and it represents models of all sizes. These different industries are going to be powered by different modalities of AI models, which then integrate into the AI natives, the startups. There are so many here in Europe. So Europe's AI economy is starting and it is really accelerating. It is great to see.
At the foundation of it, where we contribute, most people think is GPUs. It is true. We started in 1993 building a graphics chip which emerged into the GPU, the programmable shading GPU. Over time, we added CUDA acceleration to that, reinvented computing as we know it, which then brought forth artificial intelligence. NVIDIA today is really a full stack, end-to-end, complete AI factory platform. The breadth of the technology of our company over the years has really expanded. Everything from GPU computing, Vera Rubin is our latest generation, to the NVLink switches that connect them into one giant GPU. The Vera BlueField-4 compute that we recently introduced, and I will say a few words about that later, to Spectrum-X, which is our Ethernet solution, making NVIDIA the largest networking company in the world today, to Groq 3, a decode accelerator for token generation.
To Vera BlueField-4 storage acceleration, which I will say a few words in that in just a second, and Vera RTX compute, and I will mention that in just a second. This now represents the breadth of technology necessary to create the AI factory. Nothing works unless you have software that activates it all. The first part is the system software that runs the AI factory. These systems are gigantic. A 1 GW data center, 1 GW AI factory costs about USD 50 billion. Land, power, shell, all of the compute networking storage, power cooling all connected together. Multiple acres of AI factory. Just turning it on and operating is a marvel in itself. The magnificent amount of software necessary to energize this factory, to put it to use and produce tokens is an extraordinary feat. NVIDIA offers the entire full stack software.
On top of that are what we call the CUDA-X libraries, the algorithms of our company. What NVIDIA truly is an algorithm company that designs silicon to accelerate them. Of course, one of them is cuDNN, what many researchers recognize as the library that brought forth, that started the big bang of modern AI, cuDNN. cuDNN is to AI what SQL is to the world's database industry. It is a domain-specific language, a domain-specific library. Just as OpenGL and DirectX enabled real-time computer graphics, cuDNN enabled the AI computing industry, 350 other libraries. Those libraries are, in fact, the crown jewels of our company. Everything from computational lithography to signal processing, image processing, data processing, particles, fluids, sparse solvers. Incredible breadth of libraries that many of you in the audience have connected into your company. This is how we open new markets.
This is how we connect NVIDIA into algorithms that enable you to do computation at incredible speeds. Finally, on the right-hand side, on that side, the models. This is the new software, the new engine of new applications. It sits on top of and it collaborates with the CUDA-X libraries. These models NVIDIA had been working on across multiple domains. With these models, it opens up new opportunities, and I have lots to say about that later on. Every single generation, NVIDIA started with Pascal. The AI era started with the Pascal new, what is called DGX-1 system. DGX-1 brought to the world the world's first AI supercomputer. I introduced it 10 years ago. Delivered the first one to OpenAI. Started their computation journey.
From Pascal to Volta to Ampere, then to many of you who heard about us for the very first time, Hopper, the current generation, Blackwell, and then now Vera Rubin in full production. Notice six generations of architectures. With each generation, we advanced the technology. In the course of that last 11 years, we have improved AI performance by 1 million times. If you would take a step back and think about what would Moore's law have done, in 10 years' time, the first five years would have improved by a factor of 10. The second five years by another factor of 10. 100x improvement in 10 years' time, in the same period of time. Because of the way NVIDIA extreme co-designs, we are innovating across every single computer, across every single stack of the software, optimizing new algorithms, creating new algorithms while creating architectures to accelerate it.
Working across that entire domain, what we call extreme co-design, we were able to accelerate AI by 1 million times, and we are going to continue to do so. This is what NVIDIA builds today, and this is the foundation by which we can enable Europe to be part of the AI ecosystem, the AI economy. Each generation, in the beginning, Pascal was GPUs, Volta was GPUs, Ampere was GPUs, and Hopper was GPUs. We invented the new DGX form factor. Then it was Blackwell that introduced to the world the first rack -scale computing, and I will show you that in just a moment. Rack -scale computing turned one entire system into one giant computer, one GPU. Now with Vera Rubin, we are extending it to processing beyond models, beyond large language models, to now processing the entire agent. We will refer back to this slide later, but remember this.
The agent has to think and that is where the model comes in. Perception, reasoning, planning, and using tools. All of that runs on the large language model, and the models are designed to do that. The agent also has what is called a harness. Basically, this exoskeleton of the digital agent. Just as we have the brain, the rest of our body represents our exoskeleton. The agent is the exoskeleton that orchestrates the work that allows the agent to actually engage memory, active memory, working memory, as well as long-term memory and activating tools. Then the third part is a computer that processes knowledge designed for data processing. Lastly, a computer dedicated to CUDA-X libraries, and its job is to run tools. These computers now represent the entire agentic system. Vera Rubin is in full production, and it is nice to see it.
This is the first computer in the world that we have created. No cables, no wires, completely 100% liquid cooled, and it is just really delightful. Whoops. Hang on. Really delightful to be able to pull it out. This is one Vera Rubin computer. Okay, so this has two GPUs. Yep. What used to take, in the case of Grace Blackwell, two hours to assemble one node, and when you see it, you will know what I mean. Cables and wires everywhere, 80% liquid cooled. Now, Vera Rubin is 100% liquid cooled, and it takes less than five minutes to assemble it. So incredible achievement. Of course, this translates to higher cycle times in manufacturing, much better reliability, and of course, a lot better performance. This is the NVLink switch tray, and what this does is it connects 72 of these GPUs. There are four of them in here right now.
It connects 72 GPUs into one gigantic GPU. Each GPU is 20 petaflops, connecting 72 of them into one gigantic system. Thank you. Thank you. This is our new Vera computer.
What are you doing about that? What are you doing about it?
It is okay. Go easy. Give her a chance. Everybody has a chance to say something. Today, let me. No, no. I will talk to you later. I will talk to you guys later. Thank you. All important issues. Let me get through my keynote. This auditorium is packed. We have 100 other watch parties around the world. 100 watch parties. 15,000 people are watching us right now. I will get through the keynote. There are some really fun things I want to show you in the end. I have to torture you through some education first, and then the entertainment is coming. This is our brand-new Vera computer. Vera CPU is an extraordinary CPU beast. Eight instructions per clock. It has the world's first LPDDR memory system, so that we can have high performance, very high bandwidth, as well as extremely excellent energy efficiency.
The on-chip fabric is the fastest of any chip in the world. As a result, data processing is off the charts. Agentic sessions starting and processing is off the charts, and this is designed for doing exactly that. This is our Vera computer. This computer here is the Spectrum-X. This is the world's first co-packaged optics. You can see it here, I will show you. Some of this area falls into the ground, and some of this area you can walk on. I am just not sure which. If I accidentally disappear, you guys know it is not a stunt. It is an accident. You could see here, these are the lasers, and these lasers come up through optics, and it directly connects to our chip. It connects using a process technology we invented with TSMC called COUPE.
Silicon photonics are directly attached right onto our chip. Otherwise, what you would see is all of this would be PCB, and PCB's transmission capability is much worse than optical cables. We now have extremely high data rate, extremely high-quality data that comes directly onto the chip, and this goes directly into otherwise transceivers without going through a PCB, and a transceiver goes right into the optics. This is the world's first CPO. We are in complete volume production, and 512 port at 200 Gb per second. The world's most advanced Ethernet switch, and so we are really proud of it. Thank you. What that gives us is the ability to process the thoughts, the large language model, process the agent harness, run data processing for its knowledge, and then run accelerated computing for all of the tools.
That represents essentially the agent workload, and I will show you more in a second. Vera Rubin is in full production, and the performance is off the charts. Every single generation, as I mentioned, through extreme co-design, we are able to achieve performances that are well beyond reason, well beyond what Moore's law would have dictated. As you know, Moore's law has really largely ended. We need these extreme co-design ideas, working from chips to systems to system software, to algorithm, to the entire AI factory. Vera Rubin is another giant step on top of Blackwell. Blackwell was about 30x faster than Hopper in doing large language model processing. Now, Vera Rubin is another 30x greater than Blackwell in just two years' time. Let us take a look at how Vera Rubin turns into what is a DSX full stack AI factory.
AI is creating the largest infrastructure build-out in history. The factories are massive. The investments are significant. We have to build it right. Great chips alone do not make a great AI factory. NVIDIA DSX is a full-stack AI factory reference architecture, co-innovated with the world's leading infrastructure companies. Compute, networking, storage, software, power, and cooling, co-designed as one system. Optimized from design to operation, DSX starts in a digital twin. Performance, power, water, and scale, validated virtually before construction begins. PTC turns equipment designs into simulation-ready assets. Dassault Systèmes connects rack and facility design through model-based systems engineering. Schneider Electric simulates power distribution and liquid cooling. Jacobs brings it all together in the Omniverse digital twin. Siemens evaluates thermal, acoustic, and grid impacts. Procore supports construction phase planning. Power limits how much an AI factory can produce.
DSX MaxLPS is an end-to-end control loop that dynamically steers power to match workload demand. It uses 30% less power for the same output. DSX rethinks cooling. NVIDIA Vera Rubin systems are designed for closed-loop liquid cooling, with coolant temperatures up to 45 degrees Celsius. In suitable climates, outdoor dry coolers release heat into the air without evaporating water. At 2 GWs, DSX can save 54 million liters of water a day. That is the daily water use of 180,000 households. An AI factory can take several months to build. Until it is operational, it produces no tokens. Today, we are announcing NVIDIA DSX Pod, bringing this full-stack platform into a factory-built, integrated system. It starts with Vera Rubin NVL 72. Seven liquid-cooled compute racks connected with 17 km of Spectrum-X cables form a Vera Rubin DSX Pod.
Vera Rubin DSX Pods are integrated, fully cabled, and factory tested, then delivered in transportable containers. Dry coolers replace traditional evaporative cooling. 800 volts DC connects to the racks for efficient power delivery. Once site utilities are ready, estimated on-site deployment goes from several months to days. NVIDIA DSX AI Factory is responsible by design. With DSX Pod, AI factories can be built at the speed of light. More intelligence per watt, less time to first token.
What do you think? In 2016, we introduced the world's first AI supercomputer. It was in one node, one GPU node. Then three years ago, we introduced the Grace Blackwell NVL72 rack scale computer. Both times, we had to reinvent not just the computer, the software, and how it works, but also the supply chain.
In the case of NVL72, we took the entire rack, which would have otherwise been assembled on-site, and we assembled it inside the supply chain. We essentially manufactured supercomputers at scale. It is the reason why we are able to move so fast and be able to provide for literally several hundred data center build-outs around the world at the same time. Every single computer manufacturer in the world now manufactures NVL72. The scale at which we are now producing computing is unheard of ever. Unimaginable. Even then, when you bring these systems onto your sites, there are still months and months of assembly, and you just heard 17,000 km of wiring to connect just Spectrum-X for the scale-out computing. 17,000 km. It is a crazy number, 17,000 km incredible numbers.
Now, suppose we were to do that all inside the supply chain, run everything up, build everything up, make sure that it runs completely, get it qualified, burnt in, and by the time we ship it is fully functional. The amount of cabling and on-site assembly is going to be greatly reduced. But there is something much, much more. In fact, what is going to happen here is that by moving this into the supply chain, the amount of idle computing that is going to be sitting inside at your data centers will reduce dramatically. Remember this, each gigawatt data center is USD 50 billion. The revenue generation you expect is tens of billion dollars per year.
If we just said it is USD 40 billion per year of revenue generation that comes out of that infrastructure. USD 40 billion, if delayed by just three months, that is USD 5 billion. Excuse me, what is that? USD 10 billion worth of revenues. USD 10 billion worth of revenues will make anybody anxious. By moving this into the supply chain, the speed by which we can turn on, go from installation, arrival of computing to production of tokens will go from many months, we think something along the lines of four to five months down to about a month. This is a complete transformation of the world's supply chain, and this will change the way that people install and build data centers, and hopefully, as a result, velocity goes up. The wasted idle compute, the wasted idle CapEx will reduce, and everybody becomes more profitable.
This is NVL72 now into a large cabinet system. We call it DSX POD. This is the next click of NVIDIA computing. What you make and how you make it makes you special. Just as here in Europe, the work you know makes you special. You design machines, you operate factories, you discover medicine, you have extraordinary craft. That craft, that specialty, makes you special. What makes NVIDIA special are really two things. The first thing is that our architecture is the only accelerated computing architecture that is general purpose. We do not specialize on just one algorithm, for example, transformers. We specialize across anything that could be parallelized. It could be fluids and particles. It could be quantum. It could be quantum chemistry. It could be image processing. It could be data processing, structured vector data processing of all kinds.
Because we have been working at it for so long, and because NVIDIA's architecture has remained consistent for literally two and a half decades, the number of algorithms, the number of domains, the number of industries we serve, we accelerate, has grown tremendously. Our install base, our general purpose architecture, our dedication to this architecture, and our collaboration with all of the world's industries has made NVIDIA's architecture literally everywhere. The second thing is how we do it. Of course, we have the architecture that we developed, but we also build it now in a full stack, complete AI factory. There are many companies in the world who want to design their own chips, but most of the companies in the world just wants to deploy the AI infrastructure so that they could run their business on top of it.
The ability to design a chip and build a supercomputer, write all the software, is really completely out of mind for most of the companies around the world. We now have the combination of these two ideas, CUDA, full-stack AI factories, and it exposes us to markets that otherwise is impossible. We are able to partner with so many companies here for this very reason. If I were to go around this, starting with CSPs, this is where most people think the vast majority of AI is today. As you can see in the beginning of our keynote, I mentioned a whole bunch of companies who are not those. CSPs remain very large businesses for the world's computing. There are AI natives, companies that are starting all over the world. There are so many that are here.
Neoclouds, because every region wants to participate in the AI economy. Every region wants to ensure that the valuable resources that they have, the energy that they have, is dedicated to companies in their region. For example, Mistral here in Europe, Mistral in France, Nebius here in Europe. So many companies around Europe that are building up neoclouds. OEMs and ODMs who are building computers for the world's enterprise. AI labs. Of course, we know about last year, all we spoke about was OpenAI. Today, we speak about OpenAI, Anthropic, but we also talk about the incredible Meta's Llama Muse, Grok, and of course, Gemini. So many different AI labs are now participating at the frontier. Then very importantly, and this is ultimately the most important.
For AI to succeed, for Europe to benefit from AI and to have really created an AI ecosystem and AI economy, you need all of the end markets, the end industries, to adopt AI and to succeed with AI. Whether it is in the automotive industry because we are building self-driving cars, or the way you manufacture with robotics, the way you discover new drugs in healthcare, your financial services industry. All of these different industries have to become AI companies, every single one of them. Well, we have a unique ability through what I just mentioned about CUDA and the way that we build computers. We have a unique ability to serve literally every single segment of AI. So one architecture, many markets.
Because these infrastructure are so significant and so expensive, we need to make sure that they are as productive as possible, as soon as possible, that they are fungible, that you can use it for every workload from the beginning of your AI journey, data processing to training to post-training, inference, and agentic processing, every single step of the AI journey to every type of AI, chemicals and physics, to languages, to every single industry and the algorithms that they use. So NVIDIA's architecture is fungible, and as a result of that, continuous innovation, continuous updating of software. Because the install base is so great, we have such an incredibly rich ecosystem of software, then this architecture is also durable. Fungible, durable, productive. Well, that lays the foundation. That is the infrastructure. Now what do we do with it? This last year, we saw a transition in AI.
AI is now productive, and one of the most important times was in the beginning of the year when we spoke about OpenClaw, which introduced the concept of an open source agent, and it really started what we all see across the world today. Agents think, reason, iterate, use tools, and as a result, consume an enormous amount of tokens. But as a result, they become useful. So more agents are being developed, they are more useful, and therefore they are used by more people. And because they have to use a lot of tokens to think, the amount of token consumption has gone through the roof. GPU demand has really gone through the roof. Now, what is processing it is this. This is an agent. These are the five elements of an agent. The first part are the language models.
These are the tokens that are running on top of the GPUs, and they are generating tokens that are consumed by the agent, what I call the exoskeleton. That OpenClaw harness sits inside a sandbox, and that sandbox isolates it from the rest of the system. This is a very important concept that we are going to come back to. There are five things. There is the model, there is the harness, the sandbox that the harness sits in, and two other things, knowledge and tools. Models, sandbox, harness, knowledge, and tools. These are the five elements of an agent. Now, before I talk about how to make the agents useful, the most important thing is we have to make sure that the agent is secure. The agent is autonomous.
In this autonomous system that has access to files, has access to resources, networks, access to tools, we need to make sure that the privileges that we provide this autonomous system is minimal and that we constantly monitor it. We introduced a fantastic new product, a new platform, and it is called OpenShell. OpenShell came just in time. This is a secure runtime that provides for the things that I just mentioned and has an additional hardware-based monitor. Before I explain it to you, let us watch a video.
Enterprises are preparing to deploy billions of AI agents. To be useful, they will reason, plan, use tools, access data, and work together. Today, the only thing stopping an agent from misusing all that is asking it nicely. Please do not delete anything. Please do not push to main. Please do not touch my SSH keys. That is the please don't approach. But hope is not security. Safety is not an aspiration. It is technology and engineering. NVIDIA OpenShell and Sentry put that philosophy into practice. You write a policy for what each agent can touch, and OpenShell enforces it at the granularity of system calls, file access, and network connections. Formal verification checks every policy change against your permission ceiling, so agents never gain more access than intended. It is like the early internet. Viruses were everywhere until browsers enforced strict limits on what a webpage could do. OpenShell does the same for agents.
Enforcement happens outside the agent, not just in LLM guardrails, bringing agent-native security into your infrastructure. Every agent runs inside its own isolated sandbox with no direct route to the network. Every outbound network request goes through the supervisor outside the sandbox. It checks the action against your policy. If it is not permitted, it is denied. Keys stay outside with the supervisor and are added only on the way to an approved endpoint. A compromised agent cannot leak a key it never had. The gateway is the control plane. The supervisor makes the decisions. The sandbox holds the agent, and the policy prover checks proposed policies against your permission ceilings. OpenShell combines Linux kernel isolation with runtime policy enforcement. NVIDIA Sentry adds an optional layer of protection outside the host itself, away from the agent.
Running on NVIDIA BlueField-4, Sentry monitors agent activity and enforces security policies in a hardware-isolated trust domain. Because agent reasoning traffic passes through Sentry, if the agent is planning to do something unintended, it will update policies immediately and cut the connection to the model, quarantining it in milliseconds. It keeps tamper-evident records that operators can verify for detection, investigation, and response.
OpenShell is open source under Apache 2.0, runs everywhere your agents do, edge to cloud, and takes two commands to adopt. Install the CLI, create a sandbox, and run your favorite agent. Agents are only as useful as the access you give them. OpenShell and Sentry put that access under your control. That's how we advance AI capabilities and safety together.
Agents are autonomous systems. We have to create extremely secure technology to make sure that we could develop these agents and deploy these agents safely. Now, it's incredible. After announcing OpenShell, just about every major company in the world, from the frontier labs to the operating system companies, cybersecurity companies, platform companies, have come out to adopt OpenShell. It's really fantastic to see.
The reason for that is because this technology has arrived at exactly the right time, at a time when we want to deploy agents all over the world. Now remember, this isn't just about the services using OpenShell as they deploy agents. This is really about all of the world's companies also adopting OpenShell so that you could receive these agents and have them run autonomously inside your company. Every single level of capability, every single task that you give the agent, for every person that uses the agent, every one of those combinations requires a different level of privilege that you would provide to the agent. Every single time an agent is launched, the privileges are different. Privileges, the policy enforcement, the monitoring, all has to be done dynamically. OpenShell is really revolutionary.
I'm delighted to say that Anthropic and NVIDIA are partnering to create Claude OpenShell agents. You can go to the website. There's a really great write-up about that. The first part is how do you use these harnesses, OpenClaw, Hermes, OpenHands. You could create your own harnesses from LangChain, Deep Agents, or just write it from Python. There's a whole bunch of different ways you could create agent harnesses that sits inside now an OpenShell secure harness. That's the sandbox that it sits in, the secure sandbox. Next, let's talk about models. You should use models of all kinds. In the future, it won't just be one model that we run everything on. We're going to use the best models, whether they're closed, open, or custom. We're going to use all the models to do your work.
Sometimes the work that you do is fairly generic. Artificial general intelligence is going to do an incredibly good job. However, sometimes the work that you want to do captures too much of your proprietary knowledge, too much of your specialty, and you want to continuously enhance that intelligence. Every company in the future will use both closed models as well as open models you customize. By the end of the session, you will see that in fact, building your own AIs is not that hard. It starts with, of course, the model. We've been dedicated to advancing open models to open up new opportunities and enable people to build their own custom AIs. Nemotron is for languages. Cosmos is our world foundation model. We are the frontier. GR00T for human or robotics, we are the frontier. Alpamayo for autonomous navigation, we are the frontier.
Ising for quantum -classical computing, we are the frontier. Earth-2, physics AI, we are the frontier. In all of these different areas, we offer you all of the models, and we are continuously working on it. We have been working on it for a decade, and we will continue to work on this forever. We offer you the model completely open, the data that goes along with it so that you know the provenance of the model that we created, as well as the scripts necessary for you to reproduce it. Not only do you have the open weights, you have from us the entire recipe and the data to recreate the open models so that you could know where it came from and have complete trust into it. These models are not necessarily the frontier of frontiers, especially for the language model.
However, with customization and with partners that the ecosystem that we work with, you could turn these models into frontier, and as a result, turn your proprietary data into sovereign intelligence. Here is an example of AI21 helping Alice turn Nemotron into a model that exceeds frontier capabilities. Here is Baseten helping CodeRabbit, turning a Nemotron model after fine-tuning on your data, turned into frontier capability. Baseten and CodeRabbit. Here is Trajectory and Harvey. Again, using Nemotron, post-trained it, and turned and created their own frontier level capability. Here is Scale working with Skydance, again, doing the same thing. It goes on and on and on. The point being that you could start with an open model, something that is incredibly good like Nemotron, and with your own data, post-train it for your use case and achieve, of course, super intelligence.
That is a very sensible thing to do, and all the technology exists today. Here is an example of Palantir with Tampa General Hospital. The list goes on and on. We have a rich ecosystem of partners that can help you, and they are incredible companies, and they can work with you from around the world. Anyways, I will mention one and I will offend everybody else. I will have to sit here and mention all of them. I love them all. First, Agent Harness running in a secure sandbox, OpenShell. Second, Nemotron large language models, the first two components of what you need. Ultimately, what turns these agents into super agents is that you give them access to your company's knowledge. This is really special. This is called the Agent Knowledge Foundation that we put together.
Libraries and models that are completely world-class and allows you to turn your company's data. The assets could be in unstructured format in documents, so that you need an AI to understand how to read these PDFs and read all these unstructured documents, or it could be in structured data inside databases. We have the libraries and the tools necessary to help you extract meaning and understanding from your company's data. One at a time. Nemotron Parse, completely world-class, top of leaderboard. Nemotron Embed, taking a query and associating with the knowledge that is inside the vector database. Top of leaderboard. Nemotron Rerank. It might find many different sources where the query is associated with different pieces of data. It brings it together, ranks them based on which one is the most relevant to you. Kumo, to be able to predict from your structured database what is likely to happen.
This is, for example, if you were to receive a notice that one of your suppliers will be short on supply or delay on supply, that piece of information is likely unstructured data as a document. The information about the orders in your company are likely inside a structured database, your SQL database, for example. In order to predict what is going to happen to all of these orders, who are going to be short, the amount of delay, you use Kumo to make that prediction. Then in order to figure out what to replace it with, you might use vector database search and go find an engineering document of a replacement component. All of those different libraries and data processing capabilities now makes for you your knowledge foundation. You have now armed your AI with proprietary data, proprietary knowledge.
Now, the last part that I will talk about is ultimately tools of the trade. NVIDIA has been working on CUDA-X, and for most of you, before AI, all of our conversations have been around how do we accelerate simulators and solvers, data processing engines, domain-specific libraries of all kinds. They are used in scientific discovery in all the different fields of science. They are used in engineering. They are used in research. They are used in designing, building, creative arts. At this core are CUDA-X libraries, and these CUDA-X libraries are algorithms. Algorithms, as you know, are beautiful. Let us take a look at them.
Math is beautiful. Math is beautiful. CUDA-X libraries, these are the crown jewels of our company, and the reason for that is this.
When you are building an AI assistant, it could be a lab assistant, it could be a research assistant, it could be a co-designer, somebody who is co-creator with you, building something with you. Ultimately, what makes those agents useful, what makes them able to do real work, are the tools. So we now have models that allow these agents to reason, understand information, reason, to be able to plan and iteratively act and use tools until it can get the job done. But it still comes down to the tools. These tools we have been working on for so many years, and there was one particular computer I showed you on the right-hand side that runs every single one of these tools. It could be fluid dynamics and particle physics. It could be quantum chemistry. It could be data processing. It could be ray tracing.
All of these tools run on that one computer, and that one computer speeds up the processing of it, compared to just running it natively on a CPU, by 10x, 50x, 100x. CUDA-X tools is what give the agents the tools of the trade. It is so cool to see how other people are using it. Let us take a look.
So this is the magic. Magic behind the agents. Ultimately, the agents have to use tools. Of course, in one of the first fields of agentic use is software programming, and therefore, a compiler or a Python is all really it needs. But in the future, as we have these tools do more and more and more, and more useful work, we are going to need to give them more tools. Here are some examples. So here is Lila Sciences.
They are creating a lab assistant, and they are using NVIDIA's BioNeMo models that are designed for understanding proteins and understanding cells and generating proteins and chemicals in drug discovery. They are using BioNeMo and CUDA-X tools. This is Siemens Factory, building a factory agent that uses NVIDIA Cosmos, our world foundation model that understands the physical world, and CUDA-X tools. Here is an example of Dassault creating a virtual companion that uses NVIDIA's Nemotron, our language model, and CUDA-X tools. Let's see them in use.
The most intelligent scientist still needs a laboratory. The best engineer still needs simulators and solvers. An AI agent is no different. An agent brings together five building blocks. The model perceives reasons and plans. The harness coordinates the work. Knowledge foundation gives the agent context from your company's structured and unstructured data. Tools let the agent simulate, experiment, and act. NVIDIA OpenShell controls what agents can access and execute. Start with a frontier closed model or customize an open model. With NVIDIA's Agent Toolkit, any company can turn its knowledge and expertise into a customized super agent. Let's look at three examples. For Lila Sciences, the goal is to design a therapy for B-cell lymphoma. The Lila Iris agent, built on NVIDIA Nemotron, turns that goal into an experimental plan.
In the computational dry lab, Lila agents use NVIDIA BioNeMo Agent Toolkit to call models for protein design and RNA optimization, running millions of virtual experiments. The most promising candidates move into Lila's AI science factory, where autonomous robots and instruments, developed with NVIDIA Omniverse and Cosmos, make and test them. The results return to Iris, shaping the next designs and experiments. The loop continues. At Siemens Electronics Factory, Erlangen, the goal is to bring new products into production. Process Preparation X agents use NVIDIA Cosmos to generate virtual prototypes and synthetic data, then train and validate inspection models before production starts. Intelligence Center X agents, built with NVIDIA's factory operations blueprint, use orders, supplies, and line setups to recommend production plans. Digital Twin Composer uses NVIDIA Omniverse libraries to validate those plans with physics simulation and real-time factory data before changes reach the factory floor.
With the Dassault Systèmes platform, an engineer needs to adapt a Volkswagen Golf door design for the Tiguan. Aura and Leo use NVIDIA Nemotron to reason over engineering requirements. The harness coordinates the specialized sub-agents and preserves context across planning, design, and simulation. Aura uses ENOVIA to plan the work around the vehicle's dimensions, weight limits, and other engineering requirements. Leo-design agent selects a compatible Golf door design and uses CATIA to reshape it for the Tiguan. Leo-simulation agent uses SIMULIA, accelerated by NVIDIA CUDA-X libraries, to test the variants, revealing a vibration problem. The results return to the design agent for refinement. The engineer reviews the refined designs and selects the final one. Different goals, different expertise, the same building blocks. Open models, CUDA-X accelerated tools, and the OpenShell secure runtime, brought together with the NVIDIA Agent Toolkit.
Isn't it great? Some people said that AI is here, it's going to be the end of software. It's going to be end of tools company. It's exactly the opposite. AI is here. There'll be more users of these tools that we've created over time than ever before. Every single tools company will now become an AI company. They'll be an agents company. Not only will they offer tools, but they'll offer you specially optimized agents that are incredibly good at using their tools. Cadence and CrowdStrike and Dassault, IQVIA, and just about every enterprise company out there are all building agents, and we're just delighted to be working with them. Building agents is not that hard. It's got five components. It's got the model, the harness, both of them have closed and open versions. The OpenShell secure runtime, and then most importantly, the knowledge and tools.
Those knowledge and tools belong to you. That's your expertise. All of this is now possible to do, and I'll show you more in just a second. Before I do that, let's talk about physical AI. That's really the next wave of AI. AI that understands the physical world, can reason about it, because the physical world is diverse, it is complicated, it's got extremely long tails, and to reason about it and use appropriate tools to interact with the physical world. Whenever people think about physical worlds, they think robotics. In fact, one of my favorite physical world applications is the radio cellphone cell towers. The reason for that is this. It uses, instead of cameras, instead of speakers and microphones, it uses radio waves to sense the world.
It reasons about it processes information, reasons about it, and interacts with the world using beamforming. In fact, this is an application, a technology that is just perfectly suited for AI. AI will revolutionize the way we do telecommunications. It will enhance our spectrum efficiency tremendously, just as we've done so with ray tracing, all of the graphics that you've seen completely 100% ray traced. The amount of computation we thought necessary in the past, and now that we apply AI to, is a million to one difference. We're going to bring to the telecommunications network an enormous step up in spectrum efficiency. Beyond that, we're also going to revolutionize all of these cell towers. There's 100,000 sites around the world, around the Western world, about 100 GW of distributed data centers that will, in the future, become a distributed AI infrastructure.
We're delighted to partner with many companies here in Europe. Just this last week, after working on this for nearly eight years, NVIDIA made the first commercial phone call on the T-Mobile network with Nokia. Now CUDA makes phone calls the traditional way. Then once it can do that, then we can layer AI on top of it. The revolution is going to begin. This is really exciting opportunity for us. The other physical AI application that I'm super excited about is essentially robotic radiotherapy. The tumor is changing in shape. The physical body is changing while you're in therapy. The robot, this adaptive radiotherapy system, has to perceive the changing environment, understand where the tumor is, segment it just perfectly, and precisely direct the radiotherapy accordingly. Essentially, robotic. That's right. Go ahead. This is a form of robotic surgery.
Now, of course, the highest volume, the most near-term robotics application or physical AI application that everybody recognized has been something we have been working on for over a decade. Now we recognize that every single car company will want to make their cars part of the ride-hailing fleet. Today, we have ride hailing, and as part of Uber and here in Europe, we have Bolt and we have Grab and Lyft and so on and so forth, and all these different ride-hailing services. In the future, there will be robotaxis that will be connected to those ride-hailing services as well. But it will not be from just one brand. We recognized that building these systems are incredibly expensive, particularly the software that goes on top of it. We decided to create one standard architecture, 14 different cameras, nine radar systems, three lidars, umpteen ultrasonics.
This cocoon of a shell of sensors powered by dual NVIDIA GPU systems for the AV computing system. We turned this into an industry standard, and now it is being designed into one car fleet after another. On top of it are AV software companies, and we are, in fact, one of them as well, to build an autonomous robotaxi capability, which then is connected into the ride-hailing services. This ecosystem is being built out. The world travels about 1 trillion miles per year around the world. Many of those miles in the future, USD 1 or USD 2 per mile, are going to be on robotaxis. This is one giant opportunity, but it is incredibly fragmented. Creating an industry standard, Hyperion, and working with an entire ecosystem, we could create essentially a giant virtual fleet that everybody can connect into.
Today, I am super proud to announce that four more brands, four more car lines are going to come in. The VW van, I just cannot imagine anything more iconic. The JLR brand new car. The Volvo fleet, as well as Mercedes next -generation, are going to be also Hyperion compatible and be part of this network, this ride-hailing network, robotaxi fleet. Welcome to Hyperion. The next major industry, the next great industry, and this one is likely to be extremely large, is humanoid robotics. There is a reason why robotics is hard to do. Because all the different work is rather diverse. Building a special configuration for each one of the diverse tasks segments the robotics industry into all of these different configurations, which prevents the technology flywheel to happen.
The reason why the cellphone has advanced so quickly, the reason why personal computers have advanced so quickly, the reason why NVIDIA's NVL72 has advanced so quickly, is because those standardized configurations allows many customers to use it, the volume to therefore be derived. That volume drives the technology flywheel, and it gets faster, better, incredibly fast. We need a robotics configuration that is as standard as possible. Humanoid robotics obviously has the ability. It probably is not perfect for everything, but it is likely to work for many things. As a result, this is likely going to be the largest volume robotics configuration. If we could just create the AI that allows it to learn very quickly, then for small, medium businesses who do not have an entire robotics team to teach and program the robot, can now show the robot what needs to be done.
The robot, because it has general capabilities and general intelligence, would be able to learn and adapt quickly. This is likely to be the next major platform, technology platform. We are working with so many different companies. You will recognize many of them here in Europe. This is your great opportunity. No country, no region has dedicated itself to making mechatronics, electronics, mechanical systems at the level of precision and craft as the European region. So many companies here are incredibly good at manufacturing. This is going to be the next high-volume industry, and you could see there are so many companies that are now part of it, and we are delighted to be partnered with you. In order to create a physical AI system, you need three computers.
The first computer is the one we have been talking about, and everybody knows quite well, which is training the AI model. The second computer is where we have the AI learn how to do its skills. You cannot just have the AI program the AI and have the AI come out into the physical world and try to do whatever it wants to do. You do it inside a digital twin. We create a digital twin called Omniverse, and you have seen us talk about that repeatedly. In fact, just now, when you saw all the CUDA-X libraries, they are all being done inside a digital twin world. That digital twin world is called Omniverse. Omniverse is a library that is connected into all kinds of platforms and tools, Siemens and Dassault and Cadence and so on and so forth. You could see these digital twin systems being created.
The second computer. The third computer is the robotics computer. That computer integrates, in the case of the self-driving car, the robotaxi, we call it Hyperion. Hyperion consists of the computer itself, the functional safety system, the active safety system, an operating system designed for physical AI safety. What we need to do exactly the same for robotic systems. So that every single time you start to develop research and start to work on your own robotic systems, you do not have to start from scratch to cobble up all of the software and computing systems and the sensors and all the mechanical systems. That alone would take several years. We decided to build a reference robot. This is the NVIDIA Agile ONE in partnership with Agile Robots.
This robot is going- This reference robot will be available in the first quarter of next year, and it will come preassembled with computer inside, software integrated, and it should be able to perform quite well right out of the bat. This is a great starting point, and it will be the flywheel by which we can accelerate development of robotics. Let me end today's speech from where we started. NVIDIA started in the PC revolution. It was in 1993, and the PC revolution was just about to start. Remember, Windows 95 kicked it into turbocharge. We came since then, invented the GPU, programmable shading, real-time ray tracing. We added CUDA to the computing model and helped ignite the modern AI revolution.
Four years ago, we started to think, what can we do to reimagine the personal computer for this new era, where the personal computer is not just your tool, your personal computer tool, but your personal agent or your personal assistant. The way you interact with this computer would be essentially a companion, somebody who helps you do work. This computer needs to have exactly the five things that I mentioned today. It needs to have model. It could be running locally or it could be running in the cloud. It needs a harness. It needs a secure runtime, OpenShell, and it needs access, because it's available on your PC, it needs access to knowledge and tools.
The good news is, because NVIDIA has been accelerating tools for so long, if we could just create a computer that can accelerate OpenGL and DirectX and everything CUDA, everything NVIDIA has ever worked on would run on this computer. This is it. After four years of partnership with Microsoft, we have created the RTX Spark, and this is a brand-new line of computers. Literally thousands of engineering years went into this. We've been secretly working on it for four years, created a chip called the N1X, and then we just recently announced it with the entire PC industry joining forces. We created desktops, laptops, and workstations. This is 1 petaflops. 1 petaflops. Just to put that in perspective, in the year 2016, I delivered 1 petaflops and 128 GB of memory, 1 petaflops and 128 GB to a small company called OpenAI.
This is 1 petaflops and 128 GB The only difference, of course, is that I can hold this like this, and DGX-1 took two people to carry upstairs. This is the brand-new RTX Spark PC, and if you would like to have a companion workstation that runs CATIA and everything Dassault and everything Autodesk, Adobe, Cadence , and Synopsys, you can always get yourself a DGX station that runs Windows. This is 20 petaflops and 754 GB of memory. Incredible. Of course, there's also the desktop version. All of these are now available and I think the DGX station with RTX Spark, DGX station with Windows will be available at the end of this year. Now the question is, so far I just talked about the five components of an agent, and now the question is, how do you build it?
How do you build an agent that runs, let's say, on one of those computers, or runs in a cloud, or runs on-prem somewhere? The answer is really easy, and you do it this way. The way you build an agent is you ask an agent to build it for you. This is the prompt that is going to run the next application. What you're about to see is this. You're about to see a robotics application. While you're watching this demo, just remember, there's an agent running on this personal computer. This demo runs across three computers. The first computer is to train, the second is a digital twin, and the third is to put that AI into the computer itself. Okay? So two ideas.
The first idea is agents, how do you build it and run it, and what you can use it to do? The second is physical AI computing model, which consists of three computers. Let's take a look.
Meet Microduck, Hugging Face's robot. Let's teach it to see and walk with NVIDIA RTX Spark. I use Hermes as my agent harness, Nemotron and OpenAI's GPT-6 Astra as my models, and CUDA-X accelerated tools FreeCAD, Blender, and Isaac Sim, Newton Physics engine, and Isaac Lab. I will run everything on my RTX Spark, except when I need to burst larger workloads to cloud. First, I describe the course and what my Microduck needs to learn. On RTX Spark, Hermes and Nemotron plan the course, listing every object required. Astra takes it from there, using FreeCAD to model 3D objects, Blender to apply materials, and Omniverse SimReady libraries to add physical properties.
In Isaac Sim, Astra composes the scene. Astra rigs Microduck's joints, actuators, and links, and simulates how Microduck moves with Newton physics engine.
Now, training begins. In the cloud, thousands of Microducks train in parallel in Isaac Lab. Every fall, a lesson. Improving the policy every time. Omniverse libraries simulate Microduck's cameras, teaching it to see. With perception and physics working together, my Microduck can avoid obstacles, navigate tight spaces, and adapt to its surroundings. I download the policy to RTX Spark and use Nemotron and Isaac Sim to evaluate my Microduck with multi-robot orchestration. I deploy the validated policy to the real duck, and he is on his way. NVIDIA RTX Spark: build and run AI agents and their accelerated tools right on your laptop.
Hey, guys. Oh, yeah? Okay, come on. Here, come over here. Show people you can walk. You have a camera. Oh, yeah? Don't trip. Can you guys sit down? Okay, stand up. Take a nap. Take a nap. You need to take a nap. Okay, stand up.
Did you know that you were created in here? You were created on RTX Spark. I know. I trained you in the cloud on a whole bunch of NVIDIA supercomputers. You learned your skills right here on RTX Spark in a digital twin, the Omniverse digital twin. Isn't that incredible? I know. You guys are so cute. Your parents are called Pollen, P-O-L-L-E-N, is that right? Uh-huh. Pollen Robotics. Apparently, everybody loves you guys so much, you are sold out. I know. If you go to the Pollen website, everybody watching this, there is going to be 20 million viewers, everybody watching this, go to the Pollen website and place an order. I think this startup company is 10 people. They might blow up! Poof! It is going to be so exciting. Everybody should have one. You guys are so cute.
We'll just have an RTX Spark, and we'll teach you new skills. Isn't that right? Oi-oi. Oi-oi. Oh, look at that. That's incredible. Can you do a somersault? That's so good. Wow. Okay. Well, guess what? Okay, today, what did we talk about? We talked about Europe building its AI economy, and it starts with infrastructure. We announced a brand-new system called DSX POD that makes it easy for you to build AI factories. These AI factories don't just run models anymore. Pay attention. Stay focused. Come here. Come over here. Stay focused. These AI factories don't just run. There you go. Sit down, yeah. They don't just run AI models anymore. They run entire agents. These agents are consistent of five things. There's the large language model that does the perception, reasoning, planning, and acting.
It runs in conjunction with a harness that turns it essentially exoskeleton, and it runs that harness, sits inside, runs inside a secure runtime we call OpenShell, which has just so many people that have supported it. You give it access to knowledge and tools. This agent runs on top of these computers, but we're also extending into the next generation, which is where you guys come in, physical AI. That's it. That's what we talked about. Everybody can build agents. Every company in Europe will become an AI company. There's no question everybody will become an AI company, and we're going to have a thriving AI economy here in Europe. After 33 years, what an incredible journey, and we made a really cool video. You guys want to watch it together? Okay, come on. Come over here.
Just come over here, just a little bit. Well, get out of the way. Come here. Top speed.
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Welcome to GTC. I want to thank all of you, all the sponsors. You can see all the names of companies that are here, all the partners we've been working with for 33 years. Microduck, my new friends. Let's work together to build Europe's AI economy and put AI to work. All right, everybody. Have a great GTC.