AI infrastructure spans multiple layers, from compute and networking to storage, orchestration, and applications. When performance degrades, identifying the source can be difficult because a symptom observed at one layer may originate elsewhere in the stack.

A full-stack observability strategy connects telemetry across these layers, helping infrastructure and operations teams detect problems, isolate their causes, and maintain reliable AI workloads. This post presents a practical observability framework for NVIDIA AI infrastructure and shows how to apply it to common monitoring and troubleshooting scenarios.

Consider a distributed training job that is three days into execution. GPU utilization and queue wait times remain normal. After six hours of reduced throughput, the team traces the cause to a single InfiniBand link drifting into an elevated bit error rate.

This is a classic gray failure .…