The Nvidia GTC Round Up: What’s Next for the AI Industry?
Nvidia Founder and CEO Jensen Huang presented at the company’s GTC Paris conference in detail how European nations are building AI infrastructure.
He announced partnerships with governments and cloud providers across the continent and outlined a roadmap to even further success and investment.
Speaking at the Dôme de Paris at Europe’s largest technology event, Jensen outlined the company’s strategy for supporting European AI development through sovereign computing models and industrial applications.
“We now have a new industry, an AI industry and it’s now part of the new infrastructure, called intelligence infrastructure, that will be used by every country, every society,” he says.
GB200 systems enter full production
It is no surprise that Nvidia has seen demand for its inference services grow exponentially. Jensen said that inference users have increased from 8 million to 800 million over two years.
As a result, Nvidia announced that its GB200 NVL72 platform, which the company describes as its most powerful AI system, has entered full production.
The platform combines 72 processing units into what Jensen characterises as “one giant GPU” designed for reasoning and planning tasks.
“This machine was designed to be a thinking machine, a thinking machine, in the sense that it reasons, it plans, it spends a lot of time talking to itself,” he says.
Nvidia’s manufacturing partners are producing 1,000 GB200 systems weekly, according to Jensen. The company offers systems ranging from the compact DGX Spark to rack-mounted RTX PRO Servers for different deployment requirements.
The company is now working with European partners to establish both AI infrastructure services for third-party use and AI factories that companies build for internal revenue generation.
Nvidia launching industrial cloud in Germany
Nvidia announced plans to build what it describes as the world’s first industrial AI cloud in Germany.
The facility will support European manufacturers in simulation, automation and optimisation processes using Nvidia’s Omniverse platform, which creates digital twins of physical systems.
“We’re working on industrial AI with one company after another,” Jensen says, referring to collaborations across the continent using digital twin technology.
Nvidia is also expanding its European technology centre network with new facilities in Finland, Germany, Spain, Italy and the UK to accelerate skills development and quantum computing research.
Quantum computing partnerships advance
Nvidia’s CUDA-Q platform, which enables hybrid classical and quantum computing, is now operational on Denmark’s Gefion supercomputer. The company has also made CUDA-Q available on its Grace Blackwell systems.
Jensen announced partnerships with European supercomputing centres and quantum hardware manufacturers to advance hybrid quantum-AI research and quantum error correction development.
“Quantum computing is reaching an inflection point,” he says. “We are within reach of being able to apply quantum computing, quantum classical computing, in areas that can solve some interesting problems in the coming years.”
How Nemotron models target sovereign AI
Nvidia has additionally introduced Nemotron, a system designed to help developers build large language models tailored to local requirements. These models will integrate with Perplexity, a search engine that uses reasoning capabilities, enabling multilingual AI deployment across Europe.
“You can now ask and get questions answered in the language, in the culture, in the sensibility of your country,” Jensen says.
The company further released agentic AI blueprints, including safety frameworks for enterprises and governments – such as the NeMo Agent toolkit and AI Blueprint for data flywheels are designed to accelerate development of AI agents that can operate autonomously.
Nvidia is also partnering with European governments, telecommunications companies and cloud providers to deploy its DGX Cloud Lepton platform across the region. Lepton provides access to accelerated computing capacity and integrates with Hugging Face, a platform for machine learning models.
“One model architecture, one deployment and you can run it anywhere,” Jensen says.
The future of automotive and robotics applications
Nvidia DRIVE, the company’s autonomous vehicle platform, has entered production to support large-scale deployment of intelligent transportation systems. The platform provides the software and hardware stack for self-driving vehicle development.
The company demonstrated its robotics capabilities through a partnership with DeepMind, Google’s AI research unit, and Disney to develop Newton, a physics training engine for robotics applications.
“Soon, everything that moves will be robotic,” Jensen says, “and the car is the next one.”
Jensen was joined on stage by Grek, a demonstration robot, to illustrate the convergence of physical and digital AI systems.
“We have physical robots, and we have information robots. We call them agents,” he says. “The technology necessary to teach a robot to manipulate, to simulate — and of course, the manifestation of an incredible robot — is now right in front of us.”
Finally, Jensen positioned AI factories as the next generation of data centres, designed specifically to generate tokens, the basic units of AI model processing.
“These AI factories are going to generate tokens,” he concluded, “and these tokens are going to become your food, little Grek.”
Explore the latest edition of AI Magazine and be part of the conversation at our global conference series, Tech & AI LIVE.
Discover all our upcoming events and secure your tickets today.
Also sign up to our free weekly newsletter for the latest insights and stories straight into your inbox.
AI Magazine is a BizClik brand