GPU scarcity makes every surrounding system more visible. Storage layout, queue depth, dataset locality, streaming lag, retry storms, and slow metadata paths all show up as idle accelerators.
A useful AI infrastructure review starts before the model code. I look at where data lands, how it is partitioned, how it is discovered, how jobs are admitted, how failures are isolated, and what operators can see when a workload is unhealthy.
The lesson from big data operations is blunt: compute is only as good as the data plane around it. If the platform cannot explain why a workload is waiting, spilling, retrying, or starving, the cluster is not really production-ready.