How to Choose Full-Stack Observability for NVIDIA AI Factories

Normal GPU utilization can conceal a slow training job. NVIDIA's monitoring guide illustrates this with a degrading network link: retransmissions delay one participant in a distributed computation, and the others wait at a shared synchronization step.

The guide's proposed remedy is to connect network, hardware and workload measurements during diagnosis. Operators need to see whether a drop in useful work coincides with a link problem, even when the machines remain online. This explains why a dashboard showing busy GPUs alone cannot establish that a training cluster is working efficiently.