Why GPU Cluster Health Checks Pass but AI Workloads Still Fail

It is an operational test that goes beyond basic health checks to confirm a cluster can actually run a distributed AI workload at expected performance before launch.

Because hidden faults such as one GPU running slower than others, a link that degrades under load, or a configuration routing traffic over a slower path do not appear in surface-level checks.

Yes. As sovereign and private AI data centers expand across the region, every wasted compute hour from a hidden fault is a direct cost to national and enterprise projects.

Before launching any large training or inference workload, and ideally on a recurring basis after any network, configuration, or hardware change.