Antimatter signs €580M CloudGrid deal for 280 Policloud units

Antimatter’s Policloud unit signed a €580 million framework agreement with CloudGrid Energy to deploy 280 distributed computing modules across renewable-energy sites in five European countries by the end of 2027. The deal aims to add 29,000 GPUs and 35 MW of capacity for AI workloads as Europe races to build more sovereign compute infrastructure. Why it matters: - The deal is designed to expand Europe’s access to sovereign AI computing capacity that is powered by renewable energy. - The rollout targets SMEs, startups and European entrepreneurs that face high inference costs and growing reliance on non-European hyperscalers. - The 280-unit buildout is set to add 29,000 GPUs, 2,000,000 vCPUs and 35 MW of energy capacity across Europe. - The infrastructure is meant to support open-source AI models such as Mistral, Nemo and Magistral. What happened: - CloudGrid Energy and Policloud, part of the Antimatter Group, signed a framework agreement for the supply of 280 Policloud units. - The contract value is €580 million. - CloudGrid Energy will deploy the units by the end of 2027. - Sixteen sites have already been secured in France, Germany, Italy, Spain and Sweden. - The first Policloud unit is already operating at the Bonne Voisine site in the Aube region of France. The details: - CloudGrid Energy will lead development, financing and operation of the deployment projects. - The sites are expected to offer green power, available land and existing grid connections. - The planned locations include solar plants, wind farms, biomass plants and electric-vehicle charging infrastructure. - Urbasolar is a deployment partner and provides access to its solar portfolio, plus installation, permitting and grid-connection support. - Policloud units are designed as modular high-performance computing modules connected to the Poligrid distributed network. - Each unit can be deployed on-site within a few months. - Each unit combines high-performance GPUs and CPUs, storage, water-free cooling and ultra-high-speed connectivity. - The design supports gradual scaling, local maintenance, minimal space use and lower land costs. - The model is intended to improve latency, resilience and operating costs compared with traditional hyperscale data centers. - The system also uses local, decarbonized energy supply. Between the lines: - The agreement lands as hyperscaler capacity remains constrained, especially by power access. - Microsoft has reported about $80 billion in unfulfilled Azure contracts, mainly tied to limited energy availability. - Deloitte expects inference to account for about two-thirds of global AI-computing demand in 2026, up from one-third in 2023. - The International Energy Agency predicts data-center electricity use will double between 2022 and 2026. - The partnership reflects a broader push to pair Europe’s energy transition with its digital-sovereignty goals. - INRIA is an investor in the Antimatter group through Inria Studio and works with the group on distributed computing and energy efficiency research. - The distributed computing model used by Policloud stems from that collaboration. - The technology has earned the Deep Tech label. - Bpifrance and One Ragtime are also investors in the Antimatter group. What’s next: - CloudGrid Energy and Antimatter plan to keep expanding the European portfolio of deployments with renewable-energy and decarbonized infrastructure partners. - The companies want to build a site-by-site sovereign European computing network over the next 18 months and beyond. - The rollout will continue across the secured sites in the five-country footprint through 2027. - The companies said the aim is to make AI infrastructure more accessible and competitive for European businesses. The bottom line: - The agreement gives Antimatter and CloudGrid a large-scale path to build renewable-powered AI infrastructure in Europe, while reducing dependence on overseas cloud giants.

Disclaimer: This article was produced by AGP Wire with the assistance of artificial intelligence based on original source content and has been refined to improve clarity, structure, and readability. This content is provided on an “as is” basis. While care has been taken in its preparation, it may contain inaccuracies or omissions, and readers should consult the original source and independently verify key information where appropriate. This content is for informational purposes only and does not constitute legal, financial, investment, or other professional advice.

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