Monitor GPU utilization alongside application throughput, memory and interconnect activity, clocks, power, temperature, and host or cluster metrics. A utilization percentage is an activity signal—not proof that the GPU is doing useful work or that the workload is running efficiently. Use broad telemetry to find when and where performance changes; use an application profiler when you need to identify the kernel or code responsible.
What GPU utilization tells you—and what it does not
GPU utilization summarizes activity over a measurement window. NVIDIA DCGM profiling values are interval averages, so a brief reading may hide bursts, idle gaps, or differences between workload phases. The same average can arise from very different activity patterns across time and multiprocessors. Interpret it over a representative period and alongside the work completed during that period.
DCGM exposes distinct signals for different parts of GPU activity, including graphics-engine activity, SM activity, occupancy, tensor activity, memory activity, and interconnect activity. They are not interchangeable. For example, SM activity measures time when at least one warp is active on an SM; it does not establish that the warp is doing useful computation. NVIDIA says SM activity of 0.8 or greater is necessary but not sufficient for effective GPU use, while activity below 0.5 likely indicates ineffective use. These are interpretations of a DCGM metric, not universal performance targets. NVIDIA’s DCGM profiling guide explains the definitions and limitations.
Judge utilization against the result you care about: training examples or tokens processed, inference requests completed, or latency. High activity with poor throughput can still signal an inefficient workload; low activity may mean work is arriving intermittently, or that another part of the system is limiting progress.
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Which metrics should you monitor together?
Start with a small set that lets you compare device activity to workload results. Add signals based on the question you are investigating and what your GPU and platform support.
- GPU activity: GPU utilization, SM-active, tensor activity, and occupancy where available. These describe different kinds of device activity; none alone proves useful throughput.
- Memory: device memory usage and DRAM activity. Memory capacity usage and memory traffic are different questions: one indicates how much memory is occupied, while the other helps characterize activity moving through memory.
- Device health and constraints: clocks, power, and temperature. These are useful context when checking for possible throttling or device-health issues.
- Transfers and fabric: PCIe or NVLink traffic when available. These can help investigate data movement between the GPU and other devices or systems.
- Workload and host context: throughput or latency, training or inference phase, CPU and data-pipeline activity, and relevant Kubernetes object and node metrics.
Metric names and availability vary by GPU model, architecture, driver, and cloud configuration. Check the documentation for the actual deployment before building dashboards or assuming a field is supported.
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- PLEASE NOTE: Exporting an NVIDIA RTX Pro 6000 GPU outside the US requires strict adherence to the U.S. Export Administration Regulations (EAR) and issuance of an export license from the Bureau of Industry and Security (BIS). Compliance and Know Your Customer (KYC) screening may be required as a condition of order acceptance. [NVIDIA Blackwell Streaming Multiprocessor] The new SM features increased processing throughput, and new neural shaders that integrate neural networks inside of programmable shaders | DLSS 4: Multi Frame Generation ensures ultra-smooth frame pacing for lifelike simulations.
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How to set up cloud GPU monitoring
- Choose the collection path. On an NVIDIA Kubernetes cluster, NVIDIA recommends DCGM Exporter to expose GPU metrics for Prometheus. A common wider monitoring stack adds Kubernetes object metrics through
kube-state-metricsand node metrics throughnode_exporter, then uses Grafana to visualize time series. See NVIDIA GPU Telemetry. - Check platform-specific requirements. Managed collection may reduce the work of operating exporters and collectors, but its prerequisites and supported metrics depend on the platform, cluster version, GPU, and configuration. Follow the current instructions for your environment rather than assuming one provider’s setup applies elsewhere.
- Record a representative baseline. Capture telemetry with throughput or latency across enough of the workload to include its meaningful phases. Mark warm-up, input loading, synchronization, training steps, or inference bursts where relevant so a change in phase is not mistaken for a performance regression.
- Build a time-aligned view. Plot application results with GPU signals and host or cluster metrics on a shared timeline. Keep sampling appropriate to the behavior under study: a long averaging window can smooth away bursts, while a very short sample may not represent sustained behavior.
- Compare like with like. Compare the same workload phase and similar operating conditions before drawing conclusions. Look for changes that coincide across throughput, GPU signals, and host or cluster context.
Which monitoring path fits your cloud platform?
| Environment | Documented collection path | Useful context or qualification |
|---|---|---|
| NVIDIA GPUs on Kubernetes | DCGM Exporter exposes GPU metrics for Prometheus; Grafana can visualize them. | Prometheus can be combined with kube-state-metrics and node_exporter for cluster and node context. See NVIDIA GPU Telemetry. |
| Google Kubernetes Engine | Google documents managed DCGM metric collection that installs DCGM Exporter and sends metrics to Google Cloud Managed Service for Prometheus; self-managed DCGM is also an option. | Requirements and defaults depend on cluster version. Check Google’s GKE DCGM metrics guide. |
| Google Compute Engine | Google documents GPU monitoring dashboards and advanced DCGM dashboards. | Advanced views can include SM utilization, occupancy, pipe utilization, PCIe traffic, and NVLink traffic, subject to the documented integration. See Google Cloud GPU monitoring. |
| Amazon EC2 with NVIDIA GPUs | AWS documents a CloudWatch solution for NVIDIA GPU workloads. | The solution includes GPU and memory use, clocks, temperature, and power. Confirm current setup instructions and scope in AWS’s NVIDIA GPU solution guide. |
| Azure Kubernetes Service | Microsoft documents collecting NVIDIA DCGM Exporter metrics with the Azure Monitor agent and a Grafana dashboard path. | GPU profiling fields may not be present by default on every GPU architecture, and Kubernetes has no native GPU-memory pressure signal. See AKS GPU metrics and AKS GPU observability best practices. |
How to troubleshoot low GPU utilization
Low activity is a reason to investigate, not a diagnosis. Use the time-aligned signals to narrow the possibilities rather than concluding that one component is at fault from a single percentage.
- Check whether the workload is continuously supplying work. Compare GPU activity with CPU, data-pipeline, and scheduling signals and with the workload phase. If GPU activity falls during input loading, synchronization, or gaps between bursts, investigate those intervals before treating the device reading as a whole-run result.
- Check the delivered result. Compare throughput or latency with GPU activity over the same period. Activity without proportional output may point to inefficient work or overhead; a high activity value alone is not evidence of good performance.
- Compare compute and memory signals. Microsoft’s AKS best-practices guide cautions that
DCGM_FI_DEV_GPU_UTILalone does not show compute efficiency. Comparing SM-active and DRAM-active profiling metrics can help distinguish compute, memory, and launch or synchronization overhead, but those comparisons are clues, not definitive diagnoses. Verify metric support for the GPU in use. - Inspect data movement and device conditions. Where available, compare PCIe or NVLink traffic with device activity. Check clocks, power, and temperature when investigating possible throttling or device-health constraints; AWS’s documented EC2 solution includes these views.
- Escalate when aggregate metrics stop answering the question. Broad DCGM telemetry can show when activity changes, but it does not identify the source line, CUDA kernel, or instruction responsible. Use a developer profiler such as Nsight Systems or Nsight Compute when you need attribution.
How to tell whether a workload is compute-bound or memory-bound
Do not label a workload from GPU utilization alone. Compare the workload’s throughput with SM or tensor activity, DRAM activity, memory use, and—where relevant—interconnect traffic. A pattern of high memory activity relative to compute can motivate a memory-bound hypothesis; strong SM or tensor activity must still be judged against the actual throughput. Neither pattern is conclusive by itself, and field support differs across GPUs and platforms.
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Also separate device-memory capacity from memory traffic. A workload can occupy a large share of GPU memory without that fact alone showing that it is bandwidth-bound. Conversely, memory activity can be important even when capacity is not full. Treat these signals as complementary evidence and use a developer profiler if aggregate telemetry cannot distinguish the cause.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.When to use an application profiler
Use fleet telemetry to find affected devices, time windows, and workload phases. Use an application profiler when you need to determine where time is spent in the application or which kernels and operations contribute to it. NVIDIA notes that developer profiling tools may need the same hardware resources as DCGM profiling. Its guidance is to pause DCGM profiling collection on the host engine during the developer profiling session, then resume it; profiling watches return blank values while paused. Follow the applicable instructions in NVIDIA’s DCGM profiling documentation.
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