Get Real-Time Visibility into GPU Usage Across Kubernetes Clusters

2026-05-21 · Source: NVIDIA Technical Blog · Field: Technology & Digital — Cloud Computing & IT Infrastructure, Artificial Intelligence & Machine Learning · Depth: Intermediate, short

Summary

The NVIDIA GPU Usage Monitor is an open-source project designed to provide real-time visibility into GPU utilization across Kubernetes clusters. Built on the NVIDIA Data Center GPU Manager (DCGM) Exporter, kube-state-metrics, Prometheus, and Grafana, it addresses common observability gaps like GPU over-provisioning and pod starvation. Many platform teams lack insight into who consumes GPUs, memory usage, or pod status, leading to underutilization and scheduling bottlenecks. The monitor deploys a fully integrated observability stack via a single Helm chart, offering pre-built dashboards that surface GPU allocation trends, compute utilization with thresholds, memory usage per workload, and running/pending pod counts. It supports Kubernetes 1.19+ and Helm 3.0+, and is available under the Apache 2.0 license.

Key takeaway

For MLOps Engineers and platform teams managing GPU-accelerated Kubernetes clusters, implementing the GPU Usage Monitor is crucial for optimizing resource allocation and preventing costly delays. You can gain immediate visibility into GPU consumption, memory usage, and pod states, allowing you to right-size allocations and proactively address scheduling bottlenecks. Deploying this Apache 2.0 licensed tool via Helm will centralize your GPU monitoring, ensuring efficient infrastructure operation and avoiding user-reported failures.

Key insights

The GPU Usage Monitor provides integrated, real-time GPU observability for Kubernetes, preventing over-provisioning and scheduling delays.

Principles

• Operational simplicity drives adoption.
• Unified observability prevents costly blind spots.
• Right-size allocations based on actual usage.

Method

The GPU Usage Monitor integrates DCGM Exporter, kube-state-metrics, Prometheus, and Grafana into a single Helm chart deployment, providing pre-built dashboards for immediate GPU visibility.

In practice

• Deploy with a single Helm command.
• Filter metrics by NVIDIA GPU platform.
• Configure external Prometheus integration.

Topics

• Kubernetes
• GPU Monitoring
• DCGM Exporter
• Prometheus
• Grafana
• MLOps Infrastructure

Code references

• NVIDIA/gpu-usage-monitor
• NVIDIA/DCGM
• kubernetes/kube-state-metrics

Best for: MLOps Engineer, AI Architect

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Editorial summary, takeaway, and curation by AIssential. Original article published by NVIDIA Technical Blog.