France's OVHcloud plans frontier AI models to become Europe's second LLM player - Finance news and analysis from Global Banking & Finance Review
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France's OVHcloud plans frontier AI models to become Europe's second LLM player

Published by Global Banking & Finance Review

Posted on June 17, 2026

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· Last updated: June 17, 2026

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OVHcloud to Train Frontier AI Models, Targeting Key Role in Europe's LLM Sector

OVHcloud’s Strategic Move into Frontier AI Model Training

By Leo Marchandon

OVHcloud’s Ambition and Market Context

PARIS, June 17 (Reuters) - OVHcloud plans to train frontier AI models - the most advanced, large-scale systems built from scratch using vast data and computing power - its CEO said on Wednesday, positioning the firm as a potential European challenger to Mistral.

The move marks a shift for OVHcloud, Europe's largest cloud provider, as governments and companies seek alternatives to U.S. and Chinese AI systems - a search made more pressing by the recent abrupt switch-off of Anthropic's top-tier models.

CEO’s Perspective on Technology Mastery

"It became quite clear to us that if we don't master this technology, we can't guarantee our future," OVHcloud CEO Octave Klaba told Reuters at the VivaTech conference.

Economics and Industry Landscape

Reduced Costs and New Opportunities

Klaba said the economics of developing such cutting-edge models have shifted, citing advances in chips, training techniques and synthetic data. A project that might once have cost about 1 billion euros ($1.2 billion) could now be attempted for 150 million to 200 million euros, he said.

The Second Wave of AI Model Development

He described the industry as entering a "second wave," with new entrants building on groundwork laid by firms such as OpenAI, Anthropic, and Mistral. He added OVHcloud would not use client data to train its models.

Model Development and Open-Source Plans

Multiple Models for Diverse Needs

The company plans to launch a family of models rather than a single system. "We can clearly see that the major players release multiple models, because each model is built for something specific," Klaba said. "There's no one model that does all the magic alone."

DragonLLM and Supercomputing Resources

He pointed to DragonLLM, a recently acquired startup, adding that pre-training has been completed on a model using Jupiter, Europe's fastest supercomputer, but cautioned that OVHcloud was not yet ready to make detailed performance claims.

Open-Source Commitment

OVHcloud intends to open-source its models once they reach sufficient performance. "We'll see when we're good enough to open source them, but that is indeed the goal," he said.

($1 = 0.8627 euros)

(Reporting by Leo Marchandon. Editing by Mark Potter)

Key Takeaways

  • OVHcloud shifts toward frontier AI, leveraging DragonLLM acquisition and pre‑training on Jupiter, Europe’s fastest supercomputer.
  • CEO Octave Klaba cites reduced costs—from ~€1 billion to €150–200 million—due to advances in chips, training and synthetic data.
  • The move is driven by Europe’s push for AI sovereignty amid U.S. export‑control shocks like Anthropic’s shutdown of top-tier models.

Frequently Asked Questions

What are OVHcloud's plans regarding frontier AI models?
OVHcloud plans to train advanced frontier AI models from scratch, positioning itself as a leading European contender in large language models.
Why is OVHcloud developing its own AI models?
The move addresses growing demand for European alternatives to U.S. and Chinese AI models and aims to secure the company's technological future.
How does OVHcloud plan to differentiate its AI models?
OVHcloud will not use client data for training, intends to launch a family of specialized models, and plans to open-source them once sufficiently mature.
What technology is OVHcloud using to train its AI models?
OVHcloud is leveraging Europe's fastest supercomputer, Jupiter, and recently acquired DragonLLM for pre-training their AI models.
When will OVHcloud's AI models be open-sourced?
OVHcloud intends to open-source its models when they reach a satisfactory performance level, although no specific timeline has been set.

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