Low GPU utilization does not, by itself, mean your inference server needs more CPU. In an agentic workload, the model may be waiting for an external tool; queues, memory pressure, or client-side limits can also leave the GPU idle. Diagnose the cause by matching time-aligned CPU, GPU, queue, cache, and latency measurements to the same repeatable workload.
Start with a repeatable workload
Before changing hardware or server settings, record what is running and what it is doing. Include the model, serving engine and version, hardware, prompt and output lengths, concurrency or request arrival rate, and whether agent tools are enabled. Keep these conditions consistent when comparing runs.
Where possible, compare a run that includes tool calls with a controlled run without tool waits, keeping the model and request shape as similar as practical. Agentic tasks can involve many model invocations: NVIDIA describes a range of 50–500 sequential invocations for a single task. That is vendor-published workload context, not a universal rate. Its agentic inference overview also explains why irregular waits during tool calls can interrupt model activity.
Collect measurements over the same time window. A utilization number without the request timeline cannot tell you whether the model was compute-limited, waiting, or unable to receive work.
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Use latency and serving metrics to find the symptom
Separate the request into phases rather than relying only on average end-to-end latency. NVIDIA AIPerf documents server-side signals that help distinguish model execution, queueing, and capacity effects. Its metrics are scraped every 333 ms by default during an AIPerf benchmark; that is an AIPerf tool default, not a general monitoring interval.
- Time to first token (TTFT): How long a request takes to produce its first output token.
- Inter-token latency: The time between generated tokens, useful for spotting slow generation after output begins.
- End-to-end latency: The total request duration, which can include queueing and agent tool waits as well as model work.
- Queue and concurrency signals: Waiting and running request counts, queue depth, and token throughput show whether demand is accumulating or the server is being kept busy.
- Memory-pressure signals: KV-cache utilization and preemptions can point to capacity constraints that affect throughput or latency.
Consult the AIPerf server metrics guide for metric definitions and troubleshooting context. A growing waiting queue can indicate saturation; cache use approaching capacity can signal OOM risk. Conversely, low running and waiting counts may point to a client that is not sending enough work. These clues are not mutually exclusive, and none alone identifies the root cause.
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Distinguish CPU, GPU, queue, and tool-wait constraints
| Candidate constraint | Pattern to look for | What to verify |
|---|---|---|
| CPU-side orchestration or serving work | Host CPU saturation or contention coincides with delayed request processing or scheduling, while GPU work is not continuously supplied. | Correlate host CPU and process activity with request timing and GPU activity. For vLLM V1 specifically, check whether the API server, engine core, and GPU workers have sufficient CPU time. |
| GPU execution | GPU work remains busy while throughput or latency is constrained. | Confirm with workload-specific GPU activity and execution traces. The cited documentation does not establish a universal utilization threshold that separates CPU-bound from GPU-bound inference. |
| Queue or memory capacity | Waiting requests grow, latency tails rise, or KV-cache use and preemptions indicate pressure. | Compare waiting and running requests, cache utilization, preemptions, and throughput during the same interval. |
| External agent tool wait | GPU activity drops during tool-call intervals while the model worker waits for external work. | Align tool-call start and finish times with model activity. This pattern points to agent-loop or tool latency, not by itself to a need for more host CPU. |
These causes can overlap. For example, a heavily queued service can also encounter cache pressure, and CPU contention can worsen the time needed to schedule requests even when GPU execution is substantial. Use correlated evidence to decide which stage is limiting the workload rather than treating CPU and GPU as mutually exclusive explanations.
Apply CPU guidance only to the serving stack it describes
In vLLM V1, the API server, engine core, and GPU workers all compete for host CPU time. vLLM says the engine core is sensitive to CPU starvation and documents a minimum of 2 + N physical CPU cores for N GPUs: one API process, one engine core process, and one GPU worker per GPU. The documentation also notes that additional cores are often beneficial. This is a vLLM V1 minimum guideline, not a universal sizing formula for other serving engines or deployments. See vLLM’s optimization documentation, updated August 20, 2026.
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If the observed pattern suggests CPU contention, check actual process scheduling and contention before adding cores. A machine can have many cores on paper while the relevant processes still compete for time, are constrained by configuration, or share the host with unrelated work.
Benchmark the workload you actually serve
A benchmark is useful only if it reproduces the conditions behind the slowdown: representative prompts, output lengths, concurrency or arrival rate, and tool behavior. A run with short prompts, no tools, and little concurrency may exercise a different limit from a production agent loop. Compare runs with matching request shapes and track latency distributions and throughput, not just a single average.
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For Triton-served models, NVIDIA says GenAI-Perf is being phased out and directs new performance benchmarking work to AIPerf. See the GenAI-Perf documentation for that transition. The appropriate benchmarking tool does not replace a representative workload or time-aligned server metrics.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Profile only after the symptom is repeatable
Once measurements identify a repeatable interval or stage to investigate, use a profiler to inspect CPU/GPU overlap and wait time. vLLM recommends Nsight Systems for lower-overhead profiling in performance-critical cases and PyTorch Profiler when richer debugging detail is needed. Profiling can significantly slow inference, so do not report profiled throughput as an uninstrumented benchmark result.
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vLLM’s profiling page states: “Profiling is only intended for vLLM developers and maintainers to understand the proportion of time spent in different parts of the codebase. vLLM end-users should never turn on profiling as it will significantly slow down the inference.” The warning applies to the profiling workflow described on that page; consult vLLM’s profiling documentation and verify options against your installed release. The documentation says --profiler-config is available from vLLM v0.13.0.
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