Commissioning & Go-Live

Commissioning converts design intent into evidence; an AI factory needs two interlocked acceptance tracks — facility and cluster — whose gates you sequence deliberately or the schedule sequences for you.

Every commissioning test needs a pre-agreed observable gate and a witnessed result; an AI building earns acceptance when facility and cluster evidence close the same power, cooling and workload boundary.

Electrical acceptance forces the paper power chain to prove, with instrumented evidence, that it holds a GPU cluster's synchronized megawatt-scale swings — a load no static load bank reproduces.

Behind the meter you are the grid, and commissioning proves that generation, controls and storage can sustain the declared AI load, including the campus-wide duty for a gigawatt-class site, through the required source states and the instant the utility lets go.

Commission the cooling protection chain in layers: surrogate load and injection/HIL prove controls and fail-safe actions, liquid-cooled load banks on the manifolds prove the liquid path, and staged normal workloads close operating evidence without making live GPUs the fault target.

IST proves design-basis faults at a boundary whose applicable permissions and methods are agreed with the utility, AHJ, insurer and Cx authority; controlled trips, injection/HIL and load banks prove the utility-loss chain, and staged workloads close the fixtures' remaining operating evidence.

Fabric defects surface quietly, one marginal optic and one mis-cabled rail at a time; every defect you fail to screen at layer 1 returns as a straggler or a stalled all-reduce in production.

Burn-in forces a GPU fleet's infant-mortality defects into a bounded acceptance window under the selected synthetic stress before a bad node can restart a synchronous job; release follows executed diagnostic coverage, correctness and thermal evidence for that hardware/software cohort.

A cluster's performance is accepted through the production scheduler and storage: training needs ML Productivity Goodput measured over a sustained reference training run, inference needs correct responses within its latency contract, and neither can average away a failed safety, correctness, isolation or recovery gate.

Go-live ramps megawatts and synchronized GPU load in stages through an operational-readiness gate; the failure modes are outpacing what grid and cooling can absorb, and handing over before operations is ready.