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Measure GPU utilization alongside inference throughput and latency, not as a standalone score. Establish a repeatable baseline with representative traffic, collect complementary GPU signals during the run, then change one serving or engine setting at a time and rerun the same workload. The goal is more useful work within your latency objective—not the highest possible utilization reading.
What does GPU utilization mean for inference?
“GPU utilization” can refer to several different signals. A general utilization percentage does not show which GPU resource is busy or whether its activity is producing useful computation. NVIDIA’s DCGM documentation describes profiling metrics as averages over a sampling interval, not instantaneous readings. Its metric definitions and interpretation guidance are in the DCGM feature overview.
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| Signal | What it can help you assess | How to interpret it |
|---|---|---|
| Graphics or compute engine activity | Whether GPU engines are active during the sampled period. | Activity alone does not tell you whether inference is meeting its throughput or latency goals. |
| SM activity | How actively streaming multiprocessors are executing warps. | Active warps may be waiting on memory requests. NVIDIA says an SM-activity value of 0.8 or greater is necessary, but not sufficient, for effective GPU use; below 0.5 likely indicates ineffective use. These are NVIDIA’s metric guidance, not universal service targets. |
| SM occupancy | How much of the available SM capacity is occupied by active warps. | Occupancy is context for diagnosis, not a score to maximize by itself. |
| Tensor Core activity | Whether Tensor Core resources are active during the measured interval. | Interpret it with model precision, workload, and application performance; it is not a substitute for throughput or latency. |
| Device-memory activity | Whether memory resources are active. | High activity can motivate investigation of memory behavior, but a counter alone does not prove a bottleneck. |
| PCIe or NVLink traffic | How much data is moving over the measured interconnect. | Use alongside application timing to investigate transfers or communication overhead. |
How do I measure GPU utilization for AI inference?
- Define the service outcome. Record the model and precision, GPU type, request mix, input and output lengths, concurrency, target throughput, and latency objective. There is no single utilization target established for every model, GPU, or service.
- Run representative traffic. Use a repeatable workload that reflects the traffic you need to serve. Record the GPU identity and configuration so a later comparison uses the same hardware context.
- Capture device telemetry during the run. NVIDIA’s TensorRT benchmarking guidance shows
nvidia-smi dmon -s pcufor monitoring clocks, power, temperature, and utilization. Correlate those readings with the serving system’s own throughput and latency measurements; device telemetry does not provide the full service result. See NVIDIA’s TensorRT performance benchmarking guidance. - Collect complementary profiling metrics. DCGM profiling includes engine activity, SM activity and occupancy, Tensor Core activity, device-memory activity, and PCIe and NVLink traffic. Compare the signals rather than inferring performance from one utilization percentage.
- Choose a sampling interval that fits the test. The Triton GenAI-Perf telemetry guide warns that DCGM Exporter’s 30-second default collection interval is too infrequent for detailed benchmarking. DCGM’s feature overview documents configurable profiling intervals and a 1 Hz default in its feature description; available metrics and configuration depend on hardware and software version. Consult the GenAI-Perf GPU telemetry guide and the DCGM documentation for your deployment.
- Change one variable, then repeat. Keep the workload and measurement method constant. Compare throughput, latency (including tail latency if available), resource activity, and the recorded clock, power, and temperature readings. NVIDIA notes that changing clocks and throttling can make benchmark results less stable.
Why is my GPU utilization low during inference?
A low reading is a clue to investigate, not a diagnosis. The cause depends on what the application is doing during the sampled interval, and a single counter cannot establish it.
- Requests may arrive too slowly or irregularly to keep the device supplied with work. Compare request arrival patterns and concurrency with GPU activity over the same period.
- Batching may expose too little parallel work. If the serving system processes small batches, test batch-size or scheduling changes and check both throughput and latency.
- Work may be outside the GPU. Preprocessing, data movement, or host-side gaps can leave the device with little work to execute. Compare application timings and telemetry, then use a profiler if the gap is unclear.
- The workload may be limited by memory movement or communication. High device-memory or interconnect activity can point toward areas to investigate, but confirm the suspected bottleneck with application timing or profiling.
- The sampling window may hide short bursts. Interval-averaged telemetry can smooth brief periods of activity. Use an interval suitable for the test duration rather than relying on a collection period that is too long.
How can I increase GPU utilization without increasing latency?
Test each change against the same request mix and service objective. Higher utilization is useful only if the resulting service still meets its latency requirements. These options are supported by NVIDIA’s TensorRT optimization guidance and, for the scheduler comparison, the Triton TensorRT-LLM backend documentation.
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| Change to test | Why it may help | Trade-off or check |
|---|---|---|
| Batch size or dynamic/opportunistic batching | More requests processed together can expose parallel work and amortize per-layer overhead, often improving throughput. | Waiting to form a batch can add latency. Benchmark candidate sizes rather than assuming the largest is best. NVIDIA notes that on Ada Lovelace or later, smaller batches can improve throughput when they help inputs and outputs fit in L2 cache. |
| TensorRT-LLM scheduler policy | max_utilization greedily packs requests to pursue throughput. |
If KV-cache limits are reached, it can incur pause/resume overhead. Compare with guaranteed_no_evict, which prioritizes not pausing requests that have already started; evaluate both under your traffic and KV-cache constraints. |
| TensorRT engine or execution settings | Options such as CUDA graphs, multi-streaming, layer fusion, and Tensor Core targeting may improve execution for a particular workload. | Treat each as an experiment. Recheck accuracy as well as throughput and latency when a setting changes precision or numerical behavior. |
| Preprocessing, host scheduling, or data movement | Reducing gaps between GPU work can help when telemetry and application timing indicate the device is waiting for inputs or work. | Use a profiler and application timings to confirm where time is spent; low device activity alone does not identify the responsible stage. |
When should I use a profiler?
Continuous DCGM counters are useful for comparing phases or replicas, but they do not identify the source line, CUDA kernel, or instruction responsible for a metric. When telemetry cannot explain a throughput or latency result, use a developer profiler to examine execution more closely. NVIDIA advises coordinating access to hardware counters: pause DCGM collection while profiling tools need the same resources, then resume collection afterward.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How should I judge whether a change worked?
Keep a record for each run: workload conditions, GPU configuration, clocks, power, temperature, throughput, latency, and the GPU signals relevant to the suspected bottleneck. Compare like with like, and retain a change only if it improves the service outcome without breaking the latency objective. A utilization increase by itself is not evidence of a better inference service.
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