Digi Power X signs $2.5B Master Services Agreement with Cerebras — 40 MW AI data center in Columbiana Alabama anchors compute-capacity buildout
Digi Power X announced a Master Services Agreement with Cerebras Systems for a 40-megawatt AI data center campus in Columbiana, Alabama, with an initial 10-year term valued at approximately $1.1 billion and total potential contract value up to $2.5 billion. The deal is among the largest single-customer compute-infrastructure commitments outside the hyperscaler-internal-buildout track and confirms Cerebras's strategy of placing wafer-scale capacity in dedicated multi-MW campuses.
The capacity-commitment substance is the operational piece. 40 MW of dedicated AI-compute capacity at the Columbiana site is meaningful at the Cerebras platform tier — wafer-scale systems are larger-form-factor than standard GPU racks, so a 40 MW campus translates to substantial wafer-scale-engine deployment. The $1.1B initial 10-year contract value plus $2.5B total-potential ceiling implies a sustained multi-year commitment with optionality on expansion. The Alabama location is consistent with the broader U.S.-Southeast pattern for AI-infrastructure buildout: grid availability, lower electricity costs, and reasonable proximity to major fiber routes make the region competitive for the new wave of AI-cloud campuses.
The competitive context is the AI-cloud capacity build. NextEra Energy's $67B acquisition of Dominion Energy consolidates utility-side grid-capacity for AI buildout. Cerebras's $66B post-IPO market cap provides the public-company balance sheet that supports the multi-year capital commitment Digi Power X is contracting against. The combined picture across May 2026 — utility consolidation, AI-accelerator IPO success, multi-billion compute-infrastructure deals — reflects an AI-infrastructure cycle that is shifting from speculative-buildout to contracted-multi-year-capacity at scale. The procurement-side decision for hyperscalers and AI-cloud operators is which capacity allocations to lock in at which price points across which silicon architectures.
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