Meta Rolls Out Massive AI “Superclusters” Driven by 5GW Hyperion Hub
Meta is constructing new AI-focused data centre clusters, including a flagship facility named Hyperion capable of scaling to 5 gigawatts that’s enough power to fuel a small city, while another cluster, Prometheus, is expected online in 2026 with over 1 GW capacity.
That level of energy consumption places Hyperion alongside some of the world’s largest computing installations. As Mark Zuckerberg shared on Threads:
“We’re also building Hyperion, which will be able to scale up to 5 GW over several years,” adding that the first Prometheus cluster will activate in ’26.
Driving factors:
- Meta plans to boost its 2025 capital expenditure on data centres to between $64 billion and $72 billion, nearly double last year’s amount.
- The increase follows Meta’s acquisition of a 49 percent stake in Scale AI, a $14.3 billion deal intended to secure high-quality training data, and major hiring of top AI personnel.
Temporary solutions under pressure:
Meta is deploying tented data centre modules adjacent to full-scale facilities to rapidly expand compute capacity and avoid construction bottlenecks.
SemiAnalysis notes that these prefabricated setups prioritize speed over elegance:
“This design isn’t about beauty or redundancy. It’s about getting compute online fast!”.
Local impact in focus:
The resource demands present challenges for nearby communities.
- In Georgia, a Meta campus has been linked to dry residential wells and rising water bills.
- Engadget reports that new AI data centres could consume millions of gallons of water per day, placing strain on local supplies.
Competitive positioning:
These “superclusters” are Meta’s strategic response to earlier delays with Llama 4 and the rapid expansion efforts of OpenAI and Google. Capabilities matching Prometheus and Hyperion would give Meta computing power similar to that under development by rivals such as OpenAI’s Stargate and Google’s hyperscale facilities.
Meta’s approach combines aggressive infrastructure development, large-scale financial investment, and rapid personnel recruitment. The urgency is clear: Meta aims to be a frontrunner in the race toward advanced AI capabilities.
That acceleration comes with consequences. Community resource stress and broader environmental demands will test whether gains in AI performance will outweigh local and ecological costs.