Get Real-Time Visibility into GPU Usage Across Kubernetes Clusters

Platform teams managing AI workloads on Kubernetes often struggle with limited visibility into how their graphics processing units (GPUs) are being used, leading to significant underutilization and scheduling bottlenecks. A new open-source project, the GPU Usage Monitor, has been introduced to address this observability gap. By packaging key monitoring components into a single deployment, the tool aims to give Site Reliability Engineers (SREs) and platform operators immediate, actionable insights into their GPU fleet's performance, which is critical for optimizing costly AI infrastructure.

The core challenge the monitor addresses is twofold: over-provisioning, where entire GPUs are allocated but only partially used, and pod starvation, where queued jobs stall without clear warnings. The GPU Usage Monitor combines several established open-source tools into a unified stack deployed via a single Helm chart, simplifying what was previously a complex manual configuration process. This integrated approach provides a holistic view by correlating hardware-level metrics with Kubernetes-level workload status.

Key Capabilities

The tool is built on a foundation of well-known components, including the NVIDIA Data Center GPU Manager (DCGM) Exporter, kube-state-metrics, Prometheus, and Grafana. Once deployed, its pre-built dashboards surface critical information, allowing teams to:

• Track GPU allocation trends by namespace and spot unused allocations.
• Monitor per-GPU compute utilization against configurable warning thresholds.
• Analyze real-time GPU memory consumption on a per-pod basis to right-size resource requests.
• View a single pane of running versus pending GPU pods to identify scheduling pressure early.
• Filter all metrics by NVIDIA GPU models, such as Hopper or Blackwell, in heterogeneous clusters.

Operational Impact

By lowering the barrier to comprehensive GPU observability, the GPU Usage Monitor enables organizations to transition from reactive troubleshooting to proactive infrastructure management. Instead of discovering scheduling failures only after user escalations, teams can identify and resolve resource contention before it impacts model training or inference workloads. This ultimately allows organizations to maximize the return on their hardware investment by ensuring that expensive GPU resources are utilized efficiently and allocated intelligently based on actual consumption data rather than guesswork. The tool's ability to integrate with existing Prometheus instances also ensures it can fit within established MLOps workflows.

The emergence of standardized, easy-to-deploy open-source monitoring tools for AI hardware signals the maturation of the MLOps ecosystem, where the focus is expanding from raw performance to include operational efficiency, resource optimization, and total cost of ownership.