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GPU observability

GPU observability is the discipline of correlating accelerator health and performance with the compute, memory, network, Kubernetes, and job-level signals that govern a distributed-training run. A GPU-utilisation aggregate alone is insufficient: a performance drop can originate in GPU memory pressure, network throughput, pod placement, node readiness, resource allocation, power or thermal conditions, or hardware health. (Source: sources/2026-08-13-aws-adobe-firefly-simplified-observability-with-amazon-managed-prometheus)

Why it is different from CPU-fleet monitoring

A distributed GPU job needs visibility across at least three interacting layers:

  1. Job layer — GPU utilisation, memory consumption, and network throughput per training job identify bottlenecks in distributed computation.
  2. Kubernetes layer — pod status, node readiness, and resource allocation influence placement and scheduler decisions.
  3. Accelerator-health layer — GPU health determines whether a device must be cordoned and replaced.

Those dimensions create many label combinations, so GPU observability often runs directly into metric-cardinality and time-series query-scaling limits.

Operational implication

The relevant outcome is a sufficiently long and fast query window to correlate a job's full lifecycle with infrastructure events. In Adobe Firefly, moving critical GPU metrics to AMP grew the practical diagnostic window from six to 24 hours and made 24-hour GPU-utilisation queries 28.8× faster. (Source: sources/2026-08-13-aws-adobe-firefly-simplified-observability-with-amazon-managed-prometheus)

Relationship to GPU failures

GPU observability supplies the input signals that make GPU training failure modes detectable. The two concepts are distinct: failure modes classify what breaks, whereas GPU observability specifies the cross-layer telemetry needed to find and explain an active or impending problem.

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