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 Static rack provisioning exacerbates this: an outdated approach to data center design allocates the maximum power draw per rack to meet worst-case peak demand, even though real workloads have different power needs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. Operators reserve additional capacity for failures, operational flexibility, and expansion.
In one representative power-budget view examined by NVIDIA, about 60% of delivered site power is allocated to compute for AI output.
NVIDIA DSX MaxLPS is a suite of chip, thermal, system, and software technologies that maximizes AI factory throughput within a fixed power budget. MaxLPS stands for Maximum Land Power Shell, the site-level constraints that define an AI factory: land, utility power, and the physical shell holding power, cooling, networking, and compute infrastructure.
