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Google’s Finland Plan Highlights the Search for Power- and Cooling-Advantaged AI Data Center Sites

Редакция ЦОД:Главное

Google plans to invest at least €13 billion over two years in Finnish data centers and associated infrastructure, alongside a 22-year agreement with Fortum for up to 50% nuclear power supply. The announcement sits within a broader set of initiatives spanning Nordic campuses, water-free cooling, and early-stage floating, subsea and space-based compute concepts.

Главное
  • Google plans at least €13 billion of Finnish data center and related-infrastructure investment over two years.
  • Google’s Fortum agreement runs for 22 years and covers up to 50% nuclear power supply.
  • Stargate Norway is planned for a 230 MW initial phase and a further 290 MW expansion.
  • Meta’s planned 1 GW Alberta data center is intended to use closed-loop liquid and dry cooling without water for cooling.
  • Floating, subsea and orbital data center concepts remain development-stage or experimental propositions rather than proven operating capacity.

Google’s planned Finnish expansion underscores the growing importance of energy procurement and thermal management in AI data center development. The company says it will invest at least €13 billion over the next two years in data centers and related infrastructure in Finland. Its 22-year agreement with Fortum provides for up to 50% nuclear power supply.

The Nordic market is also represented by OpenAI’s Stargate Norway project in Narvik. The project is planned to start with a 230 MW initial phase, followed by a further planned 290 MW expansion. Its initial target is deployment of 100,000 Nvidia GPUs. These figures describe planned phases and an IT deployment target, rather than operational capacity.

In Canada, Meta is building a 1 GW data center in Sturgeon County, Alberta. The facility is intended to use closed-loop liquid cooling and dry cooling without water use for cooling. The project illustrates the industry’s interest in designs that reduce dependence on water while supporting high-density compute loads.

Google has also disclosed Project Suncatcher, an experimental satellite project incorporating a TPU capable of AI inference. The concept extends the search for new infrastructure environments beyond terrestrial locations, but its cost has not been disclosed. Commercial viability would depend on future economics for launches, satellite replacement and data transmission, while heat rejection in vacuum, radiation, connectivity and orbital debris remain unresolved constraints.

Marine concepts remain earlier-stage. Hanwha Ocean has presented a 60 MW-class floating data center model. HD Hyundai Heavy Industries and Schneider Electric have signed an MOU to jointly develop a floating data center and 100 MW-class power equipment. Separately, a subsea data center in Ulsan is planned to move to sea trials in autumn 2027 after land-based testing. These developments represent models, development work, agreements and planned trials—not evidence of operating commercial capacity.

The broader implication is that grid capacity, access to lower-carbon generation and cooling design could become constraints on AI data center growth alongside server supply and land availability. The reviewed data indicate that more than 40% of US data center electricity supply came from natural gas in 2024, compared with about 24% from renewables, about 20% from nuclear power and about 15% from coal. Northern land-based projects with access to hydroelectric, nuclear or other stable generation may be nearer-term options; marine, subsea and space-based formats currently offer technological optionality but carry materially higher execution and economic uncertainty.

Почему это важно

For AI infrastructure, access to dependable power and effective heat rejection is increasingly shaping site selection alongside land and server availability. Nordic terrestrial projects appear closer to commercial deployment, while floating, subsea and orbital concepts remain higher-risk technology options with uncertain economics and execution paths.

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