0:09This is how intelligence is made.
0:14A new kind of factory, generator of tokens, [music] the building blocks of AI. Tokens have opened a new frontier, [music] turning data into knowledge, and knowledge into action.
0:34[music] They decipher nature's code to reveal [music] hidden cures, [music] mirror our cities, [music]
0:57and tirelessly navigate our world [music] to keep us moving. Tokens learn from the way we move, [music] [music] deliver the care we need, [music] and walk into danger to protect the ones we love.
1:33[music] They see what we cannot, so the steadiest hands never work alone. [music] Every idea tokens spark, [music] every success they share, puts what matters [music] within reach.
2:01[music] Tokens are helping us break new ground [music] on a scale never seen before.
2:21[music] So, we can reach [music] for the sky [music] and beyond.
2:37[music] Together, we take the next [music] great leap into a bright new future [music] built for all mankind.
2:52[music] [music] And here is where it all begins.
3:03[music] [music] Welcome to the stage, NVIDIA founder and CEO, Jensen Huang.
3:25[music] Hello, Berlin!
3:30[music] Hello, Berlin!
3:37Welcome to GTC. It's great to be here. Before we start, let's give it up for our pre-game show hosts.
3:51[applause] [applause] Smart and adorable. Smart and adorable. They spoke about AI infrastructure, sovereign AI, AI safety, and physical AI. They covered enormous amount of ground this morning, and we are going to take it one step further. It's really interesting what do L'Oreal, Revolut, Spotify, Merck, Vodafone, Zara, Porsche, Nestlé, and HSBC have in common. Well, they're all European companies, and they're all working with Nvidia on AI.
4:39Everybody everybody here is all activated on the AI ecosystem and working towards the AI economy. We've been working here in Europe now for 33 years. You probably don't 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.
5:11Our first partner was SGS Thomson. We manufactured a chip in Crolles, France, assembled it in Malta, and shipped it around the world.
5:21Our first largest customer, the largest customer of GeForce days in the in the in the beginning, was a company called MediaMarkt. Do you know them?
5:35MediaMarkt 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. Now, 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.
6:12Then in year 2020 we started working on autonomous vehicles with Mercedes. As you can see Nvidia's life journey, the entire 33 years, I've spent a lot of time in Germany. So, I'm delighted to be back. Thank you.
6:31[applause] I'm delighted to do GTC here.
6:36This year, 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've invested nearly 10 billion dollars in Europe across startups, big partnerships, small partnerships all over all over Europe. This last week alone as I journeyed from France and UK to
7:05Sweden and now here in Berlin, we announced the investments of 14 startup companies. Overall since the beginning we now have 12,000 plus partnerships across Europe. And we're all working towards one goal now. After decades 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.
7:39Nice, 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're 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 produce production of intelligence, which integrates into every single
8:07industry 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's digital intelligence, the foundation of everything.
8:47Every 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're at the very beginnings of that.
9:04The question is, what is AI?
9:08And this is a big deal. If you want to be part of creating this future, understanding what is AI is vitally important. And 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.
9:32AI of course runs on a computer, but the way that a computer operates is fundamentally different. Rather than a tool that's 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 about as think of AI as a five-layer cake.
10:00Energy 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.
10:18To be part of this AI economy, you don't 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've already started. Europe is building its AI economy. And it's five-layer cake represented here. We've got great partnerships. And when you look across this five-layer cake, you could recognize that in fact, it is a new industry because there's growth at every single layer.
10:50We're creating jobs, creating growth at extraordinary rate. Companies here in Europe, Schneider, Siemens Electric, in cred Siemens Energy, in doing incredibly well around the world. We're the computing layer. We integrate into AI factories that are now flourishing all over Europe. Startup companies like Nebia's doing incredibly well. A new company called Firebird, which is setting up a new cloud in the Central Asia region. And even Cassava in Africa.
11:25Every single telco in the future, and I predict this, every single telco in the future, with 100,000 sites representing some 100 gigawatts 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.
11:51It's really important to recognize that long before the AI models emerge, the infrastructure has to exist. Just as before you could 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. And so the models are the next layer.
12:15It's 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, and there are so many here in Europe. So the the Europe's AI economy is starting and it's really accelerating. It's great to see.
12:52At 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 the time over time, we added CUDA acceleration to that, reinvented computing as we know it, which then brought forth artificial intelligence. But NVIDIA today is really a full stack, end-to-end, complete AI factory platform.
13:27The 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'll 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,
13:57to Grok 3, a decode accelerator for generation text for token generation, to Vera 4, Vera BlueField 4 storage acceleration, which I'll say a few words in that in just a second, and then Vera RTX compute, and I'll mention that in just a second. But, this now represents the breadth of technology necessary to create the AI factory. But, nothing works unless you have software that activates it all.
14:29The first part is the system software that runs the AI factory. These systems are gigantic. A 1-gigawatt data center, 1-gigawatt AI factory, costs about $50 billion. Land, power, shell, all of the compute, networking, storage, power, cooling, all connected together, multiple acres of AI factory.
14:56Just turning it on and operating it is a marvel in itself. The mag- 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 is an algorithm company that designs silicon to accelerate them.
15:27Of 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. It's cuDNN is to AI what SQL is to the world's database industry. It's a domain-specific language, a domain-specific library. Just as OpenGL and DirectX enabled real-time computer graphics, cuDNN enabled the AI computing industry.
16:00350 other libraries, those libraries are in fact the crown jewels of our company. Everything from computational lithography to signal processing, image processing, data 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
16:29connect NVIDIA into algorithms that enable you to do computation at incredible speeds. And then finally, on the right-hand side, on that side, the models. This is the new software, the new engine of new applications. And it sits on top of and it collaborates with the CUDA-X libraries. These models NVIDIA have been working on across multiple domains. With these models, it opens up new opportunities, and I have lots to say about that later on.
17:03Well, every single generation, NVIDIA started with Pascal. The AI era started with the Pascal new what is called DGX1 system. DGX1 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.
17:50Notice six generations of architectures. With each generation we advance the technology. In the course of that last 10 years, we've 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 it would have the first 5 years would have improved by factor of 10, the second fact second 5 years by another factor of 10, a 100X improvement in 10 years time.
18:20In the same period of time because of the way NVIDIA extreme co-designs we're 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 the entire domain. What we call extreme co-design, where we're able to accelerate AI by a million times. And we're going to continue to do so.
18:48This 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. Now, 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.
19:14Then it was Blackwell that introduced to the world the first rack-scale computing, and I'll show you that in just a moment. Rack-scale computing turned one entire system into one giant computer, one GPU. And now, with Vera Rubin, we're extending it to processing beyond models, beyond large language models, to now processing the entire agent. Now, we'll refer back to this slide later, but remember this. The agent has to think. And that's where the model comes in.
19:56Perception, reasoning, planning, and using tools. All of that runs on the large language model, and the models are designed to do that. But the agent also has what is called a harness.
20:12Basically, 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. And then lastly, a computer dedicated to CUDA-X libraries. And its job is to run tools.
20:50And these computers now represent the entire agentic system. So, Vera Rubin is in full production, and it's nice to kind of see it. This is the first computer in the world that we've created. No cables, no wires, completely 100% liquid cooled, and it's just really delightful. Whoops.
21:15Really delightful to be able to pull it out. And this is one Vera Rubin computer. Okay, so this has two GPUs. Yep.
21:27[applause] What used to take, in the case of Grace Hopper Blackwell, two hours to assemble one node, and when you see it, you'll you'll know what I mean. Cables and wires everywhere. 80% liquid cooled. Now Vera Rubin is 100% liquid cooled, and it takes less than 5 minutes to assemble it. And so, incredible, incredible achievement. Of course, this translates to higher cycle times in manufacturing,
21:56more better much, 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. Okay, so each GPU is 20 petaflops, connecting 72 of them into one gigantic system. And this is our new Vera computer.
22:34So this this auditorium is packed. We have 100 other watch parties around the world. 100 watch parties, 15,000 people are watching us right now. And so, I'll get through the keynote. There's some really, really fun things I want to show you in the end. Okay, so you just have to you have to I have to torture you through some education first. And then the entertainment is coming.
22:59This [snorts] this is brand new 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 subs- memory system so that we can have high performance, very high bandwidth, as well as extremely excellent energy efficient efficiency.
23:23The 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. And this computer here This computer here is the Spectrum X. This is the world's first co-packaged optics. And you can see it here. I'll show you.
23:57Some of this Some of this area falls into the ground. And some of this area you can walk on. You I'm just not sure which. All right. So So if I if I acci- accidentally disappear, you guys know You guys know us. It's not a stunt. It's an accident. And so uh you could see here uh these are the lasers.
24:26And these lasers come up through optics and it directly connects directly connects to our chip. And it connects using a process technology we invented with TSMC called COOP. And so silicon photonics are directly attached right onto our chip. Otherwise, what you would see is all of this would be PC board and PC boards' transmission capability is much worse than optical cables. And so we now
24:56have extremely high data rate, extremely high quality data that comes directly onto the chip. And this goes directly into otherwise transceivers without without going through without going through a PCB and a transceiver goes right into the optics. Okay, so this is the world's first CPO. We're in complete volume production and uh 512 port at 200 gigabits per second, the world's most advanced Ethernet switch and uh we're really proud of it. Thank you.
25:29[applause] And so, so what that what that gives us what that gives us is the ability to uh process the thoughts, the large language model, uh 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'll
25:58show you more in a second. While Vera Rubin is in full production and the performance is off the charts.
26:05Every single generation, as I mentioned, through extreme co-design, we're able to achieve performances that are well beyond reason, well beyond what Moore's law would have would have dictated. And as you know, Moore's law has really largely ended. And so, we need these extreme co-design ideas working from chips to systems, systems to system software, to algorithm, to the entire AI factory. Well, Vera Rubin is another giant step
26:33on top of Blackwell. Blackwell was about 30 times faster than Hopper in doing agentic in doing large language model processing. And now, Vera Rubin is another 30 times greater than Blackwell in just 2 years' time. Let's take a look at how Vera Rubin turns into what is a DSX full stack AI factory.
27:04AI is creating the largest infrastructure [music] build-out in history. The factories are massive. The investments are significant. We have to build it right. But great chips alone don't make a great AI factory. Nvidia DSX is a full stack AI factory reference architecture [music] co-innovated with the world's leading infrastructure companies. Compute, [music] networking, storage, software, power, and cooling co-designed as one system. Optimized from design to operation. DSX starts in a digital twin.
27:39[music] Performance, power, water, and scale validated virtually before [music] construction begins. PTC turns equipment designs into simulation-ready assets. [music] Dassault Systèmes connects rack and facility design through model-based systems engineering. Schneider Electric simulates [music] power distribution and liquid cooling. Jacobs brings it all together in the Omniverse [music] digital twin. Siemens evaluates thermal, acoustic, and grid impacts. [music] And Procore supports construction phase planning.
28:16Power limits how much an AI factory can produce. [music] DSX Max LPS is an end-to-end control loop that dynamically steers power [music] to match workload demand. It uses 30% less power for the same output. [music] DSX rethinks cooling. Nvidia Vera Rubin systems are designed for closed-loop liquid cooling with coolant temperatures up to 45°C. In suitable climates, outdoor dry coolers release heat into [music] the air without evaporating water.
28:49At 2 gigawatts, DSX can save 54 million liters of water a day. That's the daily water use of 180,000 households. But an AI factory can take several months to build. Until it is operational, it produces no tokens. Today, we are announcing NVIDIA DSX Pod, [music] bringing this full-stack platform into a factory-built integrated system. It starts with [music] Vera Rubin NVL72. Seven liquid-cooled compute racks connected with 17 km of Spectrum-X cables form a Vera Rubin DSX Pod.
29:30Vera Rubin DSX Pods are integrated, fully cabled, and factory tested. Then delivered in transportable [music] containers. Dry coolers replace traditional evaporative cooling. 800 V [music] DC connects to the racks for efficient power delivery. Once site [music] utilities are ready, estimated on-site deployment goes from several months to days. NVIDIA DSX AI factory is responsible by design. And now [music] with DSX Pod, AI factories can be built at the speed of light. More intelligence per watt.
30:07[music] Less time to first token.
30:17[applause] [applause] So, in 2016, we introduced the world's first AI supercomputer. It was in one node.
30:36Then 3 years ago, we introduced the Grace Blackwell NVLink-72 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 MVLink 72, 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.
31:13It is the reason why we're 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 MVLink 72. The scale at which we are now producing computing is unheard of ever, unimaginable.
31:36Well, 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.
31:5917 km, 17,000 km. It's crazy number. And so, 17 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, it is fully functional. The amount of cabling and on-site assembly is going to be greatly reduced. But, there's something much, much more.
32:31In fact, what's going to happen here is that by moving this into the supply chain, The amount of idle computing, the amount of idle computing that's going to be sitting inside at your data centers will reduce dramatically. Remember this. Each gigawatt each gigawatt data center is $50 the revenue generation you expect is tens of billions of dollars per year.
33:02And so if we just said it's $40 per year of revenue generation that comes out of that infrastructure. $40 if delayed by just 3 months that's $5 excuse me, what is that? $10 worth of revenues. $10 worth of revenues will make anybody anxious.
33:28And so 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 4 to 5 months down to about a month. Okay, so this is a complete transformation of the world supply chain and this will change the way that people install and build data centers and hopefully as a
33:57result velocity goes up the wasted idle compute, the wasted idle cap x will reduce and everybody becomes more profitable. And so this is NVLink 72 now into a large cabinet system we call it DSX pod. Okay, so this is the next click of Nvidia computing.
34:24[applause] Now what you make and how you make it makes you special. Just as here in Europe, you, the work you know, makes you special.
34:43You design machines.
34:45You operate factories.
34:47You discover medicine.
34:50You have extraordinary craft. That craft, that specialty, makes you special. Well, 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 don't specialize on just one one algorithm, for example, transformers. We specialize across anything that could be paralyzed. Could be fluids and particles. It could be quantum. It could be quantum chemistry.
35:25Could be image processing. Could be data processing. Structured vector data Processing of all kinds. And because we've 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 installed base, our general-purpose architecture, our dedication to this
35:54architecture, 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.
36:21But 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. And so, we now have the combination of these two ideas, CUDA, full-stack AI factories, and it exposes us to markets that otherwise is impossible.
36:49We're 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. But, as you can see in the beginning of our keynote, I mentioned a whole bunch of companies who are not those. So, CSPs remain very large businesses for the world's computing.
37:13But, there are AI natives, companies that are starting all over the world. There's so many that are here. Neo clouds, 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.
37:34For example, Mistral here in in Europe, Mistral in France, Nebia is here in Europe. So many companies around Europe that are building up neo clouds. OEMs and ODMs who are building computers for the world's enterprise.
37:51Of course, we know about last last year, all we spoke about was Open AI. To- Today, we spoke about we speak about Open AI, Anthropic, but we also talk about the incredible Meta's Muse, Grok, Bot, and of course, Gemini. So many different AI labs are now participating at the frontier.
38:13And 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's in the automotive industry because we're building self-driving cars, or the way you manufacture with robotics, the way you discover new drugs in healthcare, your
38:42financial services in inst 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. And so, one architecture, many markets.
39:11And because these infrastructure are so significant and so expensive, we need to make sure that they're as productive as possible, as soon as possible, that they're 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
39:40languages to every single industry and the algorithms that they use. And so, NVIDIA's architecture is fungible and as result of that, continuous innovation, continuous updating of software. Because the installed base is so great, we have such an incredibly rich ecosystem of software, then these architectures also durable, fungible, durable, productive. Well, that lays the foundation.
40:08That is the infrastructure. Now, what do we do with it?
40:12This last year, we saw a transition in AI. AI is now productive and it's one of the most important times was in the beginning of the year when we spoke about Open Claw, which introduced the concept of an open-source agent, and it really started [music] what we all see across the world today. Agents think, reason, iterate, use tools, and as a result, consume an enormous amount of tokens.
40:42But as a result, they become useful. So, more agents are being developed, they're more useful, and they're and therefore they're 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's processing it is this. This is an agent. These are the five elements of an agent. The first part are the language models.
41:09These are the tokens that are running on top of the GPUs, and they're generating tokens that are consumed by the agent, what I I call the exoskeleton. That Open Claw harness sits inside a sandbox, and that sandbox isolates it from the rest of the system. This is a very important concept that we're going to come back to.
41:30There are five things. There's the model, there's 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.
41:58The agent is autonomous. It's an autonomous system that has access to files, has access to resources, networks, access to tools, and 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's called Open Shell. Open Shell came just in time. This is a secure runtime that provides for the things that I just mentioned.
42:32And has an additional hardware-based monitor. Before I explain it to you, let's watch a video.
42:44Enterprises are preparing to deploy billions of AI agents. To be useful, they'll reason, plan, use tools, access data, and work together. Today, the only thing stopping an agent from misusing all that is asking it [music] nicely. Please don't delete anything. Please don't push to main. Please don't touch [music] my SSH keys. That's the please don't approach, but hope isn't security. Safety isn't an aspiration. It's technology and engineering.
43:15Nvidia Open Shell and Sentry put that philosophy into practice. You write a policy for what each agent can touch, and Open Shell enforces it at the granularity of system calls, file access, and network connections. Formal verification checks every policy change against your [music] permission ceiling. So, agents never gain more access than intended. It's like the early internet. Viruses were everywhere until browsers enforced strict limits on [music] what a webpage could do.
43:46Open Shell does the same for agents. Enforcement [music] happens outside the agent, not just in LLM guardrails, bringing agent native security [music] into your infrastructure. Every agent runs inside its own isolated sandbox with no direct route to the [music] network. Every outbound network request goes through the supervisor outside the sandbox. It checks the action against your policy. If it's not permitted, it's denied.
44:14Keys stay outside with the supervisor and are added only on the way to an approved [music] endpoint. A compromised agent can't leak a key it never had. The gateway is the control [music] plane. The supervisor makes the decisions. The sandbox holds the agent and the policy prover checks proposed policies against your permission [music] ceilings. Open Shell combines Linux kernel isolation with runtime policy enforcement.
44:41Nvidia Sentry [music] adds an optional layer of protection outside the host itself, away from the agent. Running on Nvidia BlueField 4, Sentry monitors [music] agent activity and enforces security policies in a hardware isolated trust domain. Because agent reasoning traffic [music] 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.
45:09And it keeps tamper evident [music] records that operators can verify for detection, investigation, and response. Open Shell is open source under Apache 2.0, [music] runs everywhere your agents do, edge to cloud, and takes two commands to adopt. Install the CLI, create a sandbox, [music] and run your favorite agent. Agents are only as useful as the access you give them. Open Shell and Sentry put that access under your [music] control.
45:37That's how we advance AI capabilities and safety together.
45:45[applause] 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.
46:03Now, it's incredible. After announcing Open Shell, 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 Open Shell. It's really fantastic to see. And 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 Open Shell as
46:32they deploy agents. This is really about all of the world's companies also adopting Open Shell 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.
46:59And every single time that an agent is launched, the privileges are different.
47:04And so, privileges, the policy enforcement, the monitoring, all has to be done dynamically. And so, Open Shell is really revolutionary. I'm delighted to say that Anthropic and Nvidia are partnering to create Claude Open Shell agents. And so, you can go to the website. There's a really great write-up about that. So, the first part the first part is how do you use these harnesses, Open Claw, Hermes,
47:33Open Hand. You could create your own own harnesses from LangChain, Deep Agents. There's or just write it from Python. There's a whole bunch of different ways you could create agent harnesses that sits inside now an Open Shell secure harness. Okay? And that's the sandbox that it sits in, the secure sandbox. And then next, let's talk about models.
48:00Uh there you should use uh models of all kinds. In the future, it won't just be one model that we run everything on. We're going to run We're going to use the best models, whether they're closed, open, or custom.
48:13We'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.
48:36Every 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 for to open up new opportunities and enable people to build their own custom AIs. NeMo-Tron is for languages. Cosmos is our world foundation model. We are the frontier.
49:10GRoot for human and robotics. We are the frontier. Alpaca IO for autonomous navigation. We are the frontier. I Sing for quantum classical computing. We are the frontier. And of course, Earth-2 physics AI. We are the frontier.
49:27So, in all of these different areas, we offer you all of the models, and we're continuously working on it. We've been working on it for a decade, and we'll 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.
49:49So, 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. Well, these models are not necessarily the frontier of frontiers, especially for the language model.
50:11However, 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's an example of A21 helping Alice turn Neumotron into a model that exceeds frontier capabilities. Here's Base 10 helping Code Rabbit turning a Neumotron model after fine-tuning on your data
50:40turned into frontier capability. Base 10 and Code Rabbit. Here's Trajectory and Harvey. Again, using Neumotron, post-trained it and turned and created their own frontier-level capability. Here's 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's incredibly good like Neumotron, and with your own data, post-train it for your use case and achieve, of course, superintelligence.
51:15That is a very sensible thing to do and all the technology exists today. Here's 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're incredible companies and they could work with you, you know, from around the world. Okay, so I we work it's just anyways, I'll I'll mention one and I'll offend everybody else.
51:40I'll have to sit here and mention all of them. And I love all I love them all. And so, first, Agent Harness running in a secure sandbox open shell. Second, Neumotron large language models, the first two components of what you need. But ultimately, what turns these super agents, these agents into super agents, is that you give them access to your company's knowledge.
52:08And this is really special. This is called the agent knowledge foundation that we put together. Libraries of models that are completely world-class. It allows you to turn your company's data The data could be in the assets could be in uh 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.
52:37We have the libraries and the tools necessary to help you extract meaning and understanding from your company's data. One at a time. Neumotron parse, completely world-class, top of leaderboard. Neumotron embed, taking a query and associating with the knowledge that's in inside the vector database.
52:59Top of leaderboard. Neumotron rerank, it might find many different sources where the query is associated with different pieces of data. It brings it together, ranks ranks them based on which one's the most relevant uh to you.
53:14Um Kumo, to be able to predict from your structured database what is likely to happen. And so this is, for example, if you were to receive um I notice that one of your suppliers will be uh short on supply or delay on supply. That That piece of information is likely uh unstructured data. It's a document. The information about the orders in your company are likely inside
53:43a structured database, uh your SQL database, for example. And in order to predict what to what's going to happen to all of these to orders, who are going to be short, the amount of delay, you use CUDA to make that prediction.
53:59And then in order to uh figure out what to replace it with, you might use vector database search and go find a engineering document of a replacement component. Okay? So, all of those different uh libraries and data processing capabilities now makes for you your knowledge foundation. You've now armed your AI with proprietary data, proprietary [music] knowledge. Now, the last part they'll talk about is ultimately tools. Tools of the trade.
54:30NVIDIA's been working on CUDA-X, and for most of you, in the last before AI, all of our conversations have been about around how do we accelerate simulators and solvers, data processing engines, domain-specific libraries of all kinds. They're used in scientific discovery, in all the different fields of science. They're used in engineering, they're used in research, they're used in designing, building, creative arts.
54:58At this core, are CUDA-X libraries, and these CUDA-X libraries are algorithms. And algorithms, as you know, are beautiful. Let's take a look at them.
57:36[laughter] [applause] 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're building an AI assistant, it could be a a lab assistant, it could be a research assistant, it could be a co-designer, a somebody who's 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.
58:12And so we 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. And these tools we've 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.
58:40And 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 10, 50, 100 x.
59:01And CUDA CUDA X tools is what give the agents the tools of the trade. And it's so cool to see how other people are using it. Let's take a look.
59:37And so [applause] So this is the magic magic behind the agents. Ultimately, the agents have to use tools. Now, of course, in one of the first fields of agentic use is software programming and therefore a compiler or Python is all really it needs. But in the future, as we have these tools do more and more more and more useful work, we're going to need to give them more tools. And here's some examples. So here's Luminous Sciences. They're creating a lab assistant and they're
1:00:07using Nvidia's BioNeMo models that are designed for understanding proteins and understanding cells and generating proteins and chemicals in drug discovery. They're 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. And here's example of Dassault creating a virtual companion that uses
1:00:35Nvidia's NeMo Triton, our language model, and CUDA-X tools. Let's see them in use.
1:00:44The 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 [music] coordinates the work. Knowledge foundation gives the agent context from your company's structured and unstructured [music] data. Tools let the agent simulate, experiment, and act. Nvidia Open Shell controls what agents can access [music] and execute.
1:01:19Start with a frontier closed model or customize an open model. With NVIDIA's agent toolkit, any company can turn [music] its knowledge and expertise into a customized super agent. Let's look at three examples. For Lyra Sciences, the goal is to design a therapy for B-cell [music] lymphoma. The Lyra Iris agent, built on NVIDIA NeMo tron, turns that goal [music] into an experimental plan.
1:01:46In the computational dry lab, Lyra agents use NVIDIA BioNeMo agent toolkit to call models for protein design and RNA optimization, [music] running millions of virtual experiments. The most promising [music] candidates move into Lyra's AI science factory, where autonomous robots and instruments, developed with NVIDIA Omniverse [music] and Cosmos, make and test them. The results return to Iris, shaping the next designs [music] and experiments.
1:02:14The loop continues.
1:02:17At Siemens electronic factory Erlangen, [music] the goal is to bring new products into production. Process preparation X agents use NVIDIA [music] Cosmos to generate virtual prototypes and synthetic data, then train and validate inspection models before production starts. Intelligence [music] center X agents, built with NVIDIA's factory operations blueprint, use orders, supplies, and [music] line setups to recommend production plans.
1:02:45Digital twin composer uses NVIDIA Omniverse [music] libraries to validate those plans with physics simulation and real-time factory data before changes [music] reach the factory floor. With the Dassault Systèmes platform, an engineer needs to adapt a Volkswagen [music] Golf door design for the Tiguan. Aura and Leo use NVIDIA NeMo tron to [music] reason over engineering requirements. The harness coordinates the specialized sub-agents [music] and preserves context across planning, design, and simulation.
1:03:17Aura uses Innovia to plan the work around [music] 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 [music] 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.
1:03:50Different goals, different [music] expertise, the same building blocks. Open models, CUDA X accelerated tools, and the open shell secure runtime brought together with the NVIDIA agent toolkit.
1:04:08So, [applause] some people said that AI is here is 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 created over time than ever before. Every single tools company will now become an AI company.
1:04:31They'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. Keynes 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 four compo- five components.
1:04:55It's got the model, the harness, both of them have closed and open versions, the open shell secure runtime, and then most importantly, the knowledge and tools, and those knowledge and tools belong to you. That's your expertise.
1:05:10And so, all of this is now possible to do and I'll show you more in just a second. But before I do that let's talk about physical AI. That's really the 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 a appropriate tools to interact with the
1:05:41Whenever people think about physical worlds, they think robotics. In fact, one of my favorite physical world applications is the radio cell phone cell towers. And the reason for that is this. It uses instead of cameras instead of speakers and microphones, it uses radio waves to sense the world.
1:06:03And to it reasons about it, it processes information, reasons about it and interacts with the world using beam forming. So 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
1:06:32of computation we thought necessary in the past and now that we apply AI to is a million to one difference. And so we're going to bring to the telecommunications network an enormous step up in spectrum efficiency. But beyond that, we're also going to revolutionize all of these cell towers.
1:06:51There's 100,000 sites around the world, around the western world, about 100 gigawatts 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 and just this last week after working on this for nearly eight years Nvidia made the first commercial phone call on the on the T-Mobile network with Nokia. And so now CUDA makes phone calls the traditional way.
1:07:24Then once it can do that, then we can layer AI on top of it. And so 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.
1:07:40Your two the tumor is changing in shape the physical body is changing while you're in in therapy the robot this radio adaptive radiotherapy system has to perceive the changing environment understand where the tumor is segment it just perfectly and direct precisely direct the radiotherapy accordingly. Essentially robotic that's right. Go ahead.
1:08:06[applause] This is a form of robotic surgery. Now of course the highest volume the most near-term robotics application or physical AI application that everyone recognized has been something we've been working on for over a decade.
1:08:25And now we recognize that every single car company will want to make their cars part of the ride hailing fleet. Today we have right right hailing and it's part of Uber and here in in Europe we have uh Bolt and we have Grab and Lyft and so on so forth and all these different ride hailing services in the future there'll be robo-taxis that will be connected to those ride hailing services
1:08:53as well. But it won't be from just one brand. And so we recognize 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, umteen ultrasonics, this cocoon of a shell of sensors powered by dual Nvidia GPU systems for the AV computing system.
1:09:27We turned this into an industry standard, and now it's being designed into one car fleet after another.
1:09:34On top of it, are AV software companies, and we are in 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 a trillion miles per year around the world. Many of those miles in the future, you know, a dollar or two per mile are going to be on mobile taxis. So, this is one giant
1:10:04opportunity, but it's incredibly fragmented. And so, creating an industry standard, Hyperion, and working with an entire ecosystem, we could create essentially a giant virtual fleet that everybody can connect into.
1:10:18Today, I'm super proud to announce that four more brands, four more cars are going to lines are going to come in. The VW van, I just can't 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
1:10:45robotaxi fleet. So, welcome to Hyperion.
1:10:49[applause] The next major industry, the next major industry, the next great industry and this one is likely to be extremely large is human robotics. There's a reason why robotics is hard to do because all the different work is rather diverse. And so 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.
1:11:24The reason why the cell phone has advanced so quickly, the reason why personal computers have advanced so quickly, the reason why Nvidia's NVLink 72 has advanced so quickly is because of 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. And so we need a robotics uh configuration that is as standard as
1:11:55Now human robotics obviously has the ability it probably isn't perfect for everything, but it's likely to work for many things. As a result, this is likely going to be the largest volume robotics configuration. Now if we could just create the AI that allows it to learn very quickly, then for small medium businesses who don't have an entire robotics team to teach and program the robot can now show the robot what needs to be done.
1:12:23The robot because it has general capabilities and general intelligence would be able to learn and adapt quickly. And so this is likely to be the next major platform, technology platform. We're working with so many different companies you will recognize many of them here in Europe. This is your great opportunity.
1:12:43No 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's so many companies that are now part of it, and we're delighted to to be partnered with you.
1:13:11[applause] [applause] Now, in order to create in order to create a physical AI system, you need three computers. The first computer is the one we've 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.
1:13:36You can't 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.
1:13:47We create a digital twin called Omniverse. You've seen us talk about that repeatedly, and in fact, just now when when you saw all the CUDA-X libraries, they're all being done inside a digital twin world. That digital twin world is called Omniverse. Omniverse is a library that's connected into all kinds of platforms and tools, Siemens and Dassault and Cadence and so on and so forth. And so you could see these digital twin systems being created.
1:14:13The second computer.
1:14:15The third computer is the robotics computer. That computer integrates, in the case of the self-driving car, the robo-taxi, we call it Hyperion. Hyperion consists of the computer itself, the functional safety system, the active safety system, a operating an operating system designed for physical AI safety. What we need to do exactly the same for robotics systems.
1:14:42So that every single time you start to develop research and start to work on your own robotic systems, you don't 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. And so we decided to build a reference robot.
1:15:02This is the NVIDIA Agile One in partnership with Agile Robotics. This robot is going [applause] This reference robot will be available in the first quarter of next year and it will come pre-assembled with computer inside, software integrated, and it should be able to perform quite well right out of the bat. And so this is a great starting point and it will be the flywheel by which we can accelerate development of robotics.
1:15:36Well, let me start 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 turbo charge. We came since then, invented the GPU, programmable shading, real-time real-time ray tracing, we added CUDA to the computing model and helped ignite the modern AI revolution.
1:16:05And so four 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. And the way you interact with this computer would be essentially a companion. Somebody who helps you do work. And so this computer needs to have exactly the five things that I mentioned today.
1:16:40It 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, open shell. And it needs access because it's available on your PC, it needs access to knowledge and tools.
1:16:57And 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.
1:17:14And so this is it. After 4 years of partnership with Microsoft, we have created the RTX Spark. And this is a brand new line of computers this every literally thousands of years thousands of engineering years went into this. We've been secretly working on it for 4 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 one petaflops.
1:17:45One petaflops, just to put that in perspective, in the year 2016, I delivered one petaflops and 128 GB of memory. One petaflops and 128 GB to a small company called OpenAI. This is one petaflops and 128 GB.
1:18:07[applause] The The only difference, of course, is that I can hold this like this and DGX-1 took two people to carry upstairs. And so this is the brand new RTX Spark PC. And if you would like to have a companion workstation that runs CATIA and everything the cell and everything Autodesk and auto you know, Adobe and Cadence and Synopsys, you can always get yourself a DGX Station that runs Windows.
1:18:42And this is 20 petaflops and 754 gigabytes of memory. And so, incredible. And of course, there's also the desktop version. All of these are now these are now available and I think the DGX Station with RTX Spark with a DGX 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.
1:19:11And now the question is how do you build it?
1:19:14How do you build an agent that runs, let's say, on one of those computers or runs in the cloud or runs on prem somewhere?
1:19:21Well, the answer is it's really easy and you do it this way. The way you build an agent is you ask an agent to build it for you.
1:19:33[applause] So, this this is the prompt that is going to run the next application. And what's what you're about to see is this. You're about to see a robotics application. And 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.
1:20:13Okay? So, two very two ideas. The first idea is agents. How do you build it and run it?
1:20:19And what you can use it to do. And the second is physical AI computing model which consists of three computers. Let's take a look.
1:20:40Meet Micro Duck, Hugging Face's robot.
1:20:43Let's teach it to see and walk with NVIDIA RTX Spark. I use Hermes as my agent harness, Nemotron and Open AI's GPT-6 Astra as my models, [music] and CUDA-X accelerated tools FreeCAD, Blender, and Isaac Sim, Newton physics engine, and Isaac Lab. I'll run everything on my RTX Spark, [music] except when I need to burst larger workloads to cloud. First, I describe the course and what my Micro Duck needs to learn.
1:21:14On 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 Micro Duck's joints, actuators, and links, and simulates how Micro Duck moves with Newton physics engine.
1:21:57Now, training begins.
1:22:00In the cloud, [music] thousands of Micro Ducks train in parallel in Isaac Lab. Every fall, a lesson, improving the policy every time. Omniverse libraries [music] simulate Micro Duck's cameras, teaching it to see. With perception and physics working together, my Micro Duck can avoid obstacles, navigate tight spaces, and adapt to its surroundings.
1:22:33[music] I download the policy to RTX [music] Spark and use NeMo and Triton and Isaac Sim to evaluate my Micro Duck with multi-robot orchestration. [music] [crying and laughter]
1:22:55I deploy the validated policy to the real duck and he's on his way. Nvidia RTX Spark, build and run AI agents and their accelerated tools right on your laptop. [music] [applause] Hey, guys.
1:23:24Okay, come on. Here, come over here. Show people you can walk.
1:23:31And you have a camera?
1:23:42Can you guys sit down?
1:23:51[applause] Okay, stand up.
1:23:59You need to take a nap.
1:24:05Did you know that you were you were created in here?
1:24:08You were created on RTX Spark.
1:24:12And trained you in the cloud on a whole bunch of Nvidia supercomputers?
1:24:18And you learned your skills right here on RTX Spark in a digital twin, the Omniverse digital twin. Isn't that incredible?
1:24:29You guys are so cute. And and uh your your uh your parents are called Pollen, p o l l e n, is that right? Uh-huh.
1:24:43And apparently everybody loves you guys so much, you're sold out.
1:24:50I know. So, have you gone to the Pollen website, everybody?
1:24:53Everybody watching this, you know, there's going to be like 20 million viewers. Everybody watching this, go to the Pollen website and place an order. The I think this startup company is like 10 people. They might blow up. Poof. It's going to be so exciting. Everybody should have one. You guys are so cute. And so, we'll just have an RTX Park and we'll teach you new skills. Isn't that right? Oi oi.
1:25:18Oi oi. Oh, look at that. That's incredible.
1:25:21Can you do a somersault?
1:25:30[laughter] That's so good.
1:25:40Okay. Well, guess what? Okay, so today, what did we talk about? We talked about Europe building its AI economy. And it starts with infrastructure. And we announced a brand new system called DGX Pod. It makes it easy for you to build AI factories. And these AI factories don't just run models anymore.
1:26:06Stay focused. Come here. Come here. Come over here. Stay focused. And these AI factories don't just run There you go. Sit down. Yeah. They they don't just run AI models anymore. They run entire agents. And these agents are consistent of five things. There's the large language model that does the perception, reasoning, planning, and acting. And it runs in conjunction with a harness that turns it essentially exoskeleton.
1:26:34And it runs that harness that's inside, runs inside a secure runtime we call Open Shell, which has just so many people that have supported it. And you give it access to knowledge and tools. And 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. So, that's it. That's what we talked about. Everybody can build agents.
1:27:03Every company in Europe will come will become an AI company. There's no question everybody will become an AI company.
1:27:11And we're going to have a thriving AI economy here in Europe. So, after 33 years, what an incredible journey. And we made a really cool video. You guys want to watch it together?
1:27:24Okay. Come on. Come over here. Just come over here. Just a little bit. We'll get out of the way. Come here. Top speed. Top speed.
1:27:35[cheering] How the heck are we supposed to follow that?
1:27:40Well, there is one thing that can make this crowd go even crazier.
1:27:50[music] You won't believe this.
1:27:54GTC has just gone full Eurovision!
1:27:59[music] The keynote starting in Berlin, AI'S NEXT CHAPTER BEGINS. Agents working by your side. Europe's moment has arrived. But if you [music] missed what we said, a GTC will break it down for you. Germany [music] Oh, this is the story all about how the world building new infinal. First power plants let energy scale, then came data [music] center. Now, we've got mail.
1:28:30Now, we're way past turning coal into watts. AI factories [music] turn sand into thoughts. Move over oil, the S&P 2 does something more investable. [music] Our GPU works like an asset, producing green, looks like a [music] chip, acts like a cash machine. They make revenue while you relax.
1:28:58[music] [music] Agents code and make decisions. Give them freedom, but control their missions. Open shell on various, checking the list. No credentials, access dismissed.
1:29:18[music] Sentry on Bluefield never clocks off, protecting the sandbox like Hasselhoff. We advance AI, but limit risks. We're responsibly optimistic. [music] Our TX Spark keeps agents safe and bright. Our spot rates keep the price [music] just right. We do quantum wrecks as gaming all-in-one, cuz you can't spell fungible
1:29:43[music] AI has got a body, now it's learning the rules of gravity, friction, and using real tools. With Isaac Lab and [music] Cosmos, they train on their own. It's like college for robots without student loans. DSX pods [music] keep AI in sync. Less power, more brains, like Legos that think. QVS name, Aetron, and Comma are here. Throwing knowledge faster [music] than a burning cashier. Your full stack, it's all on its checklist. AI's five layers [music]
1:31:10[applause] Welcome to GTC. I want to thank all of you. 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. Micro Duck, my new friends, um let's work together to build Europe's AI economy and put AI to work. All right, give it away. Have a great GTC.
1:31:44[applause] [applause]
