Nvidia, AMD, Google and Meta, direct competitors in almost everything, jointly signed an open specification for building AI data centers. It was presented at the OCP Global Summit in San Jose (October 12-15, 2026) and targets the real bottleneck: not the chips, but how fast the buildings that house them can be built.
The specification is a reference blueprint for building a data center: it covers the building's physical layout, the cooling infrastructure, and the power architecture down to the rack. The stated goal is to move from a bespoke process to a modular, repeatable one. System makers, colocation providers, and operators can use predefined templates instead of designing each hall from scratch, which shortens construction timelines. Nvidia, AMD, Google, and Meta contribute.
One honest caveat: the coverage we reviewed doesn't publish concrete figures for per-rack power density or cooling capacity, so we don't treat them as known. That will become clear when the full document is released.
In parallel, an OCP Grid Resilience Specification was presented, with contributions from Nvidia, Google, Meta, and Microsoft. It defines how data centers should respond to power grid disturbances; according to reports, it requires restoring nearly all load in under two seconds. It matters because a training cluster that drops from a fluctuation loses hours of work.
Nvidia's Joe Delaere summed it up: "the real bottleneck of the AI industry now is how fast the world can build and deploy AI factories." Note that all performance figures come from Nvidia, and there is no independent measurement yet.
A common building design reduces the risk of building for one specific architecture and makes it easier to mix vendors, which favors AMD against Nvidia's incumbency. At the same time, if data centers go up faster, more chips and networking get sold: Nvidia wins even if the standard is open.
Takeaway for your company: if you buy AI compute through the cloud or colocation, a standard like this points, over the medium term, to shorter capacity wait times and less dependence on a single design. In the meantime, when contracting capacity, ask whether the provider follows these specifications, what recovery time it has for power failures, and whether it can migrate workloads between different chip architectures.
Carlos Montiel is an enterprise AI solutions architect. He implements LLMs, Agents, RAG, and orchestrators for companies across Guatemala and Latin America. Reach out for a consultation.
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