LLM Latency Profiling and Optimization: Finding the Real Bottlenecks

Inference time includes model compute, retrieval, network, and tool calls.

Optimizing one layer without measuring the stack rarely helps.

Instrument each stage with precise timing.

Use distributed traces to spot tail-latency outliers.

Cold starts, slow retrieval, and tool timeouts dominate many systems.

GPU utilization often hides CPU or network bottlenecks.

Batching, caching, and kernel fusion reduce compute time.

Routing and tiering reduce high-cost requests.

Set SLOs for p50 and p99 latency.

Alert when tail latency drifts upward.

Define per-stage latency budgets so teams know where to optimize.

Measure variance, not just averages, to reduce tail spikes.

Create dashboards that compare actual latency to budgets over time.

Use budget alerts to detect regressions before users notice.

Align latency targets with user experience expectations by workflow.

Separate interactive and batch paths to avoid mixed targets.

Track latency impact of new features during rollout.

Document bottleneck ownership so fixes are actionable.

Propagate trace IDs across services to avoid blind spots.

Capture cold start counts to explain sudden latency shifts.

Include queue time in traces so overload conditions are visible.

Record cache hit rates to explain fast versus slow requests.

Tag traces with model versions and routing tiers for comparison.

Store a small set of exemplar traces for deep analysis.

Automate latency regression reports for each release.

Use synthetic workloads to measure latency under controlled conditions.

Track network retries to separate infra noise from model issues.

Record token counts per stage to connect cost and latency.

Use sampling to keep trace volume manageable without losing signal.

Standardize trace fields so dashboards stay consistent.

Record serialization overhead for payload-heavy requests.

Track GPU queue time to spot saturation early.

Correlate latency with request size to guide batching rules.

Where do I start? Measure retrieval and model compute first.

Is p99 more important than p50? For user experience, yes.

What is the quickest win? Cache retrieval results for frequent queries.