AI factories are power-constrained industrial systems. The question is no longer how many GPUs fit in a data center, but how much AI output each available megawatt can deliver . For AI inference workloads, this makes application-level performance per watt the key metric for measuring AI factory efficiency.

Not every megawatt translates to revenue-generating compute . Power distribution, cooling, networking, storage, backup, and facility overhead take a share of the power before it reaches a GPU. Static rack provisioning exacerbates this: an outdated approach to data center design allocates the maximum power draw per rack to meet worst-case peak demand, even though real workloads have different power needs and may leave some portion of that maximum power unused.…