GPU observability isn't one thing - it's eight connected layers from silicon to cost. See why correlation across layers is what cuts debugging from 2 hours to 2 minutes, and why most teams instrument only one or two

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GPU observability is fragmented across siloed tools — hardware metrics in one dashboard, inference metrics in another, cost in a spreadsheet. A framework of 8 connected layers is proposed: L1 GPU silicon, L2 CUDA/NCCL, L3 host/OS, L4 workload identity (Kubernetes/Slurm), L5 training, L6 inference, L7 GenAI semantics, and L8 business/cost. The key insight is that correlation across layers — enabled by OpenTelemetry shared resource attributes — reduces debugging time from 2 hours to 2 minutes. A concrete example traces a TTFT latency spike through thermal throttling on one GPU back to a failing fan sensor on the host node. The post also introduces l9gpu, an open-source OTel agent covering all 8 layers, and compares existing tools (Datadog, Grafana+DCGM, CloudWatch) which typically cover only 2-3 layers.

From GPU Silicon to Business Metrics: The 8 Layers of GPU Observability

Why Correlation Matters More Than Coverage

The Bridge Layer: GPU-to-Workload Identity