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Founded in a tent near the Berlin Wall in 1980, the Tempodrom moved several times before settling near Anhalter Bahnhof in 2001. Its permanent home has since hosted concerts, galas and political events. Today, it hosts a different kind of spectacle. GTC Berlin runs three days, with more than 100 sessions on agentic AI, robotics, physical AI, open models and accelerated computing underway at STATION-Berlin down the road. Here, journalists, analysts and partners have gathered for the main event: NVIDIA founder and CEO Jensen Huang’s keynote. Huang Takes the Stage Huang opened with a question: What do L’Oréal, Revolut, Spotify, Merck, Vodafone, Zara, Porsche, Nestlé and HSBC have in common? They’re all European companies and they’re all working with NVIDIA on AI, he said. The keynote comes on the heels of NVIDIA’s weeklong road across Europe — Paris, London, Stockholm, Dublin — with the company committing nearly $10 billion to the technology ecosystem across Europe and working with 12,000+ partners.“After decades of advancement in the computer industry, we are now at the beginnings of a new industrial revolution,” Huang said. AI is a five-layer cake, Huang explained. Energy powers advanced computers that are part of an AI factory, which generates tokens and digital intelligence from the models that run on it, which are then integrated into every application. “Long before the AI models emerge, the infrastructure has to exist,” he said, highlighting how, similarly, the internet first needed networking, and electricity first required the grid. “To be part of this AI economy, you don’t want to just be consuming the AI; you want to be producing the AI,” Huang said. “We could do all of that here in Europe, and in fact, we’ve already started.” The AI Factory Gets a New Form Factor Huang framed NVIDIA’s full-stack AI factory — spanning hardware, system software, CUDA-X libraries and models — as a broad platform for building and operating AI infrastructure. “This now represents the breadth of technology necessary to create the AI factory,” Huang said. “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.” He said Vera Rubin, in full production, extends NVIDIA’s computing platform beyond models and large language models to process the entire AI agent. He also showed several of NVIDIA’s latest rack-scale hardware offerings. Next, Huang announced NVIDIA DSX POD, a reference design for complete NVIDIA Vera Rubin AI factories built by partners inside modular containers, integrated and validated before delivery. With DSX POD, onsite deployment can shrink from six months to just 14 days once site utilities are ready. “This is a complete transformation of the world supply chain,” Huang said. “This will change the way that people install and build data centers, and hopefully, as a result, velocity goes up.” The NVIDIA DSX AI Factory Platform enables companies to reduce water consumption for cooling by up to 100% and generate the same AI output with up to 30% less power. Agents Are the New Computing Platform — and Safety Is an Engineering Problem Huang said agents think, reason, iterate and use tools, driving token consumption as they become more useful. He described an agent as comprising five elements: a model, a harness that orchestrates its work and memory, a sandbox that isolates the harness, knowledge and tools. In that framing, an agent is more than a model answering questions; it can draw on knowledge and act through tools. “The most important thing is to make sure the agent is secure,” Huang said. The autonomous agent can access files, resources, networks and tools, so its privileges should be minimal and it should be monitored. NVIDIA’s answer is the new NVIDIA OpenShell Reference Architecture for Safe AI Agents, a full-stack design built on NVIDIA OpenShell — an open source, policy-controlled runtime with kernel-level sandboxing that sits between an agent and the systems it can affect. Huang said that means privileges, policy enforcement and monitoring must adapt dynamically. “Every single time an agent is launched, the privileges are different,” Huang said. Open Models Turn Expertise Into an Advantage Huang presented NVIDIA’s open models as a starting point for building AI tailored to specific needs. NVIDIA’s frontier, open models are available for use across industries: Nemotron for language, Cosmos for world foundation models, GR00T for humanoid robotics, Alpamayo for autonomous vehicles, Ising for quantum-classical computing and Earth-2 for physics AI. “We are the frontier,” Huang said. “So 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.” NVIDIA provides the models, data and scripts to reproduce them, and companies can post-train a model on their own data for a particular use case — turning proprietary data into what he called “sovereign intelligence.” “You could start with an open model, something that’s incredibly good like Nemotron, and with your own data, post-train it for your use case and achieve, of course, super intelligence,” Huang said. “That is a very sensible thing to do, and all the technology exists today.” NVIDIA Gives Agents a Knowledge Layer Huang then introduced the NVIDIA Agent Knowledge Foundation as a set of models and libraries for connecting agents to enterprise data, whether it’s unstructured information in documents or structured data in databases. He highlighted Nemotron tools for parsing documents, embedding queries and reranking relevant information, along with new NVIDIA Kumo models for making predictions from structured data. “Ultimately, what turns these super agents, these agents into super agents, is that you give them access to your company’s knowledge,” Huang said. NVIDIA CUDA-X — Accelerated Tools for Agents Huang then described NVIDIA CUDA-X as a collection of accelerated libraries used across science, engineering, research and creative work. NVIDIA’s accelerated computing platform has more than 11 million developers building on it. Huang said those libraries can accelerate simulators, solvers and data-processing engines — the kinds of tools agents may draw on to do useful work. “CUDA-X tools are what gives agents the tools of the trade,” Huang said. Agents Go to Work in Labs, Factories and Engineering A video showcased NVIDIA Agent Toolkit bringing company knowledge, models and tools together in three industry examples: At Lila Sciences, agents help design a potential B-cell lymphoma therapy, using virtual experiments to guide work in an automated lab. At Siemens’ Erlangen factory, agents help prepare new products for production, testing plans in a digital twin before changes reach the factory floor. And at Dassault Systèmes, engineering agents adapt vehicle design, using simulation to identify a problem and refine the design. “And [all this] knowledge and tools belong to you,” Huang said after the video rolled. “That’s your expertise, and so all of this is now possible to do.” Telcos as Distributed AI Infrastructure Huang said AI is transforming telecommunications, improving spectrum efficiency and turning cell towers into distributed AI infrastructure. He cited 100,000 sites and about 100 gigawatts of distributed data centers, and said NVIDIA has made its first commercial phone call on T-Mobile’s network with Nokia after nearly eight years of work. “Now CUDA makes phone calls,” Huang said. “AI will revolutionize the way we do telecommunications. It will enhance our spectrum efficiency tremendously.”