To find genuinely underused GPU capacity, measure device activity over a representative workload cycle, then match those readings to memory use, processes or pods, and scheduler state. GPU utilization alone cannot tell you whether a device is idle, holding a loaded model, allocated to a quiet job, or simply unavailable to a waiting workload.
What to measure before judging capacity
Record the GPU model, driver and runtime, monitoring tool and version, host or cluster, device identity, and whether the GPU is partitioned or shared. Keep these dimensions distinct:
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- Compute activity: how busy the GPU is during the sample.
- Memory use: how much GPU memory is occupied, which may reflect a loaded model or cache even when compute activity is low.
- Power and clocks: useful operating signals to interpret alongside activity.
- Ownership and allocation: which process, pod, or job has the device or its memory.
- Scheduling state: whether workloads are running, pending, or otherwise unable to obtain a device.
NVIDIA documents utilization, power, temperatures, clocks, and optional memory metrics as separate signals in its nvidia-smi documentation. A scheduler’s requested or allocated GPU count is not a measurement of how much compute the device is using.
Take a local sample on the GPU host
NVIDIA: use dmon and pmon
Run nvidia-smi dmon for recurring device-level readings. NVIDIA documents a one-second default sampling cycle on supported configurations. Select the metric groups you need and add timestamps or CSV output when useful; consult the installed tool’s help and documentation for available flags, because support varies by device and configuration.
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For process-level statistics, use nvidia-smi pmon where supported. It reports per-process average utilization values since the previous cycle. If a metric is unsupported or unavailable, treat it as unknown rather than zero. The NVIDIA command reference describes both commands and their limitations.
AMD: select signals with amd-smi monitor
On AMD systems, amd-smi monitor can report graphics utilization and clock, memory utilization and clock, VRAM used and total, power, temperature, and other signals. The AMD SMI guide for Release 24.6.3.0, corresponding to ROCm 6.2.4, documents watch intervals and JSON, CSV, or file output. Confirm the syntax and supported signals against the documentation for the AMD SMI version installed on your host; the consulted guide is version-specific, not a guarantee for every release. See AMD SMI documentation for ROCm 6.2.4.
Collect a representative time series
A single snapshot can miss bursty inference, batch boundaries, data-loading stalls, scheduled jobs, or daily demand cycles. Capture enough time to include the workload’s meaningful operating cycle and label observations with the device and workload identity. There is no universal observation duration: choose one that reflects the service’s actual patterns rather than treating a brief quiet interval as spare capacity.
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For persistent NVIDIA monitoring, DCGM Exporter exposes selected DCGM fields in Prometheus exposition format and can run as a systemd service, OCI container, or Kubernetes DaemonSet. The installation guide names DCGM_FI_DEV_GPU_UTIL for GPU utilization and DCGM_FI_DEV_FB_USED for framebuffer memory used. Collection cadence is controlled by --collect-interval; the guide gives a default of 30,000 milliseconds. Check the installed version’s support matrix and collector configuration: not every field is exposed automatically in every setup.
Connect GPU readings to pods and jobs
For Kubernetes, combine device telemetry with pod and resource metrics, including whether GPU pods are pending or running. NVIDIA recommends DCGM Exporter for GPU telemetry and describes a broader monitoring pattern using Prometheus and Grafana with kube-state-metrics and node-exporter for Kubernetes API and node context. See NVIDIA’s GPU telemetry overview.
Workload labels are not guaranteed merely because the exporter is running. NVIDIA’s DCGM Exporter installation guide points operators to pod-resources socket access, device ID type, service account, and RBAC when Kubernetes labels are missing. It also documents HPC job mapping and runtime container-label options. Validate that the labels and device identifiers in your dashboards actually identify the workload and device granularity you intend to analyze.
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A device chart without workload context can show low activity but not who owns the allocation or memory. NVIDIA’s GPU Usage Monitor blog describes an integrated stack using DCGM Exporter, kube-state-metrics, Prometheus, and Grafana to surface over-provisioning and pod starvation. This is NVIDIA’s account of its monitoring project, not an independent effectiveness benchmark.
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- Low compute and low memory use: the device may be idle or lightly loaded. Verify ownership, allocation, and the observation period before considering it reusable.
- Low compute with substantial memory held: a model, cache, or reservation may remain resident during a quiet interval. That pattern does not prove the workload can safely be evicted or share the device.
- High compute but weak application throughput: utilization does not establish whether work is productive. Compare it with application throughput, latency, and queue depth; those are additional operator checks, not thresholds validated by the vendor documentation cited here.
- Pending GPU pods while device activity looks low: investigate scheduling and allocation rather than concluding that the capacity is available. Check requests, device allocation, labels, and placement constraints. NVIDIA identifies pending pods as a visibility concern in Kubernetes GPU clusters.
Do not adopt a universal “underused” utilization percentage or fixed sampling window. The cited vendor documentation describes telemetry and operational failure modes, not a threshold that applies across workloads. Set alerts and capacity decisions against local service goals and representative workload cycles.
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NVIDIA states that on MIG-enabled GPUs, querying GPU, memory, encoder, decoder, JPEG, and OFA utilization through dmon is not currently supported. A missing reading is not a 0% reading. In a MIG environment, verify which entity levels and fields the deployed DCGM and exporter versions support, and identify whether a chart measures a physical GPU or an instance.
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When metrics are absent or implausible, check host GPU detection, whether the exporter is running and its endpoint is reachable, selected collector fields, driver/DCGM compatibility, and capabilities required for profiling fields. For Kubernetes workload labels, also verify pod-resources access and RBAC. NVIDIA’s exporter guide covers configuration and troubleshooting considerations.
Choose the monitoring approach that fits the question
| Need | Local CLI | Persistent NVIDIA telemetry | AMD host sampling |
|---|---|---|---|
| Fast diagnosis | nvidia-smi dmon and, where supported, pmon |
Query the exporter endpoint after deployment | amd-smi monitor |
| Fleet history and dashboards | Requires separate logging or collection | DCGM Exporter with Prometheus and Grafana | The consulted AMD guide documents local output and file capture; a fleet backend depends on the operator’s chosen stack |
| Workload attribution | Process view where supported | Kubernetes labels and job mapping require configuration | Confirm workload attribution in the deployed AMD SMI/ROCm environment |
| Key caveat | Product and MIG support vary | Validate selected fields, DCGM/driver compatibility, and permissions | The cited guide covers AMD SMI/ROCm 6.2.4; version details can differ |
These approaches are not interchangeable percentage meters. Confirm metric definitions, sampling behavior, device granularity, and hardware support before comparing readings across vendors or configurations.
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