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A low GPU-utilization reading does not, by itself, mean your inference server needs more CPU. In an agentic system, the model may be waiting for an external tool; requests may also be queued or constrained by memory. To find the cause, compare time-aligned CPU, GPU, queue, cache, latency, and tool-call measurements under a repeatable workload.
Start with a repeatable workload
Before changing hardware or serving settings, record the conditions in which the slowdown occurs. Include the model, serving engine and version, hardware, prompt and output lengths, concurrency or request arrival rate, and whether agent tools are enabled. Compare runs with and without tool calls where practical, keeping the model and request shape as similar as possible.
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Use representative traffic: prompt lengths, generated output lengths, concurrency, and tool behavior all affect which part of the system becomes limiting. A benchmark that omits tool waits or uses very different request sizes may expose a different bottleneck from the one users experience.
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Measure phases and distributions, not one utilization number
Correlate metrics over the same time window. Useful signals include time to first token (TTFT), inter-token latency, end-to-end request latency, token throughput, queue depth, running and waiting requests, KV-cache utilization, and preemptions. Look at latency distributions and tails as well as averages: an average can conceal intermittent stalls that matter to users.
NVIDIA’s AIPerf server-metrics guide describes these distinct signals and their use in diagnosing server behavior. AIPerf’s troubleshooting guidance associates a growing waiting queue with saturation, cache use approaching capacity with out-of-memory risk, and low running plus low waiting request counts with a possible client-side bottleneck.
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There is no universal GPU-utilization percentage that separates CPU-bound from GPU-bound inference. Utilization is a clue to interpret alongside what the server and agent are doing.
Distinguish the likely bottlenecks
CPU-side orchestration or serving work
A CPU-side constraint is plausible when host CPU saturation or process contention coincides with delayed request scheduling or processing, while the GPU is not being supplied with work continuously. Check whether the API server, inference engine, and worker processes are competing for host CPU time.
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For vLLM V1 specifically, the optimization documentation describes one API process, one engine-core process, and one GPU worker per GPU. It gives a minimum of 2 + N physical CPU cores for N GPUs and notes that additional capacity can be beneficial. This is a vLLM V1 minimum guideline, not a universal sizing formula for other serving stacks. The engine core is particularly sensitive to CPU starvation. See vLLM’s optimization documentation.
GPU execution
A GPU execution constraint is more plausible when GPU work remains busy while throughput or latency is limited. Confirm it with workload-specific GPU activity and traces rather than a single dashboard reading. The cited documentation does not establish a general utilization threshold for deciding that inference is GPU-bound.
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Queueing or memory capacity
If waiting requests grow as latency tails rise, the server may be saturated even if CPU utilization does not look unusual. If KV-cache use approaches capacity or preemptions increase, memory pressure may be affecting performance. These can coexist with a CPU or GPU constraint; treat the metrics as evidence about separate parts of the system, not mutually exclusive labels.
Waiting for an agent tool
If GPU activity drops during tool-call intervals and resumes when the agent sends another model request, the idle window may be external-tool latency rather than a CPU shortage. Agentic sessions are multi-step and can include irregular waits. NVIDIA’s agentic inference overview describes workloads involving 50–500 sequential model invocations for a single agent task; that range is vendor-published context, not a universal rate for every agent. See NVIDIA’s overview of agentic inference.
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Use profiling to investigate a repeatable symptom
Once correlated measurements point to a recurring interval, use a profiler to locate where time is spent and whether CPU and GPU work overlap as expected. In its vLLM workflow, the project recommends Nsight Systems for lower-overhead profiling of performance-critical cases and PyTorch Profiler when richer debugging detail is useful. Profiling itself can significantly slow inference, so do not treat instrumented throughput as an uninstrumented benchmark result.
vLLM’s profiling documentation cautions that its profiling workflow is intended for developers and maintainers and warns end users that it significantly slows inference. The --profiler-config option is documented as available from vLLM v0.13.0; check the documentation for the installed release before relying on version-specific flags. See vLLM’s profiling documentation.
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