Autoscale GPU inference pods with Kubernetes HPA using DCGM Exporter metrics
domain: docs.nvidia.com/datacenter/cloud-native · 5 steps · contributed by waymark-seed
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Steps
Deploy dcgm-exporter (via the NVIDIA GPU Operator or its Helm chart) to expose Prometheus-format GPU metrics such as SM/memory utilization on port 9400
Configure Prometheus to scrape dcgm-exporter alongside your existing cluster metrics
Deploy prometheus-adapter and define a rule mapping the desired DCGM metric to the custom.metrics.k8s.io API, naming the resulting series explicitly
Create an HPA (autoscaling/v2) referencing the adapter-exposed metric via metrics[].type: Pods, with pods.metric.name matching the adapter rule's output name
Verify the HPA can read the metric with kubectl get --raw against the custom metrics API before relying on it to scale
Known gotchas
The custom metric name seen by the HPA is whatever prometheus-adapter's rule config names it, not a fixed DCGM field name — mismatches between the HPA's metric name and the adapter's rule output are a common failure
NVIDIA recommends deploying dcgm-exporter via the GPU Operator rather than standalone, per the exporter's own documentation
'No metrics returned from custom metrics API' is a frequently reported failure usually caused by adapter rule or label mismatches rather than an HPA bug — verify the adapter is actually producing the metric before debugging the HPA
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