GPU Observability on Kubernetes: What to Monitor to Prevent AI Workload Failures

GPU observability on Kubernetes requires more than average utilization, which may be low across enterprise fleets and does not reliably indicate workload efficiency, health, or failure risk. Effective monitoring combines scheduling signals such as prolonged pod pending times, silent restarts, and Spark executor heartbeat delays with separate GPU and host-memory measurements, since CUDA out-of-memory errors and Kubernetes OOMKills have different causes and remedies. Hardware telemetry including XID events, ECC error trends, retired memory pages, and thermal or power violations can identify degrading GPUs before complete failure. The central challenge is correlating these infrastructure signals with application-level context, including Spark stages, executor status, shuffle behavior, and AI job progress, because identical pod failures can originate from preemption, memory exhaustion, hardware faults, or application errors. The text argues that isolated infrastructure tools such as CloudWatch make this correlation manual and difficult, increasing downtime and the cost of idle or failing GPU capacity, particularly on managed platforms, and presents Kubernetes-native, integrated observability platforms such as Acceldata as an approach for connecting data, AI, GPU, and infrastructure telemetry through shared lineage.

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