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Measure time to first token (TTFT) and output throughput from the same deployed endpoint, with a defined client-side timing boundary and a workload that resembles production. A useful benchmark reports TTFT, full-response latency, a decode-speed metric such as inter-token latency (ITL) or time per output token (TPOT), and aggregate output tokens per second (TPS) across several load levels—not just one peak-rate number.
Define what each measurement means
Benchmark labels are not consistent across tools. Compare the timing points and formulas, not just names; vLLM’s benchmark documentation makes the same distinction.
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Time to first token (TTFT)
For an endpoint benchmark, TTFT is the client-observed time from sending a request until the first streamed output containing a token arrives. It includes whatever falls within that boundary: network travel, queueing, prompt processing, first-token generation, and any client-side work you have not excluded. If the server sends an empty initial streaming chunk, do not count it as the first token. State whether client tokenization or other preprocessing is included.
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End-to-end latency runs from request submission until the final output token arrives. It includes TTFT and the subsequent generation interval, so it captures the total wait for a complete response.
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ITL and TPOT
ITL describes the elapsed gaps between streamed output events or tokens. The exact observation depends on streaming and chunk behavior: vLLM records an ITL sample for each output event. TPOT is a request-level estimate of average time per output token after the first:
TPOT = (end-to-end latency − TTFT) / (output tokens − 1)
Requests with one or fewer output tokens need special handling. In vLLM’s benchmark statistics, requests with one or fewer tokens are excluded from TPOT summaries; its documented server-side TPOT histogram records zero for these cases. ITL and TPOT can differ because they use different sample units and aggregation weights, and one streamed event may contain multiple tokens.
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Aggregate output throughput and request rate
Aggregate output TPS is the total number of generated output tokens divided by a declared benchmark interval. It is a system-level rate, not the tokens-per-second experience of an individual request. Request rate (RPS) is completed requests divided by a stated interval.
The interval matters. NVIDIA’s comparison of benchmarking tools says GenAI-Perf measures from the first request to the last response, whereas LLMPerf uses the total benchmark duration, including client-side preparation and storage overhead. Those rates are not directly comparable unless the windows and counting methods match. Report the benchmark tool, version, interval, generated-token count, and whether TPS is aggregate or per request. See NVIDIA’s AIPerf benchmarking guide for its load-control and measurement context.
Build a repeatable endpoint benchmark
- Fix the measurement scope. Record the endpoint, model, serving configuration, streaming mode, client location and network path. Decide whether you are measuring user-visible endpoint performance or internal server stages; client and server measurements answer different questions.
- Choose a representative workload. Specify realistic prompt and output token-length distributions, request patterns, and shared prefixes if production traffic reuses them. Prompt length affects prefill and TTFT; output length affects generation time and resource use.
- Set warmup and cache policy. Define warmup requests, measured sample count, and whether the run should represent cold or warm behavior. Repeated vLLM runs can reuse prefix-cache entries and inflate apparent throughput. If production cache reuse is not part of the test, vary the seed, restart or reset the server, or use the vLLM sweep workflow that clears caches between runs. If caching is representative of production, preserve the shared prefixes and disclose the cache context. The relevant CLI guidance is in vLLM’s benchmark serve documentation.
- Measure a range of loads. Start at low concurrency to see behavior for individual requests, then increase concurrency or offered request rate through expected demand and toward saturation. Keep the workload and configuration consistent between runs. Record errors, timeouts, and completed requests alongside latency and token counts.
- Capture distributions as well as rates. Retain per-request results or histograms. Summarize TTFT, end-to-end latency, and ITL or TPOT with mean and tail percentiles such as p50, p95, and p99 where available. Report aggregate output TPS and RPS with their formulas and benchmark windows.
- Compare results against your service objective. Plot throughput against TTFT and end-to-end latency. Choose the operating point that satisfies your own latency objective at peak demand, rather than selecting the highest observed TPS in isolation.
- Correlate endpoint results with server telemetry. When the runtime exposes internal metrics, use them to explain where time is spent and where capacity is constrained.
Read latency and throughput together
Higher concurrency can increase aggregate throughput while making each request slower. A single tokens-per-second figure hides that trade-off. Plot the latency-throughput curve across the tested concurrency or request-rate levels, then identify the range that meets the deployment’s latency objective under expected peak load.
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NVIDIA uses an average TTFT of 250 ms as an example constraint for an interactive-chat sizing exercise, not as a universal pass/fail standard. Your acceptable target depends on the application and its service-level objective. The example appears in NVIDIA’s inference sizing article.
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Use server metrics to diagnose endpoint results
Client-observed timings include the network and queueing effects users experience. Server instrumentation can help separate those effects from work inside the serving system. vLLM documents histograms for TTFT, ITL, TPOT, end-to-end latency, and prompt and generation token counts, along with queue, prefill, decode, running-request, and KV-cache signals. Keep these clearly labeled as server-side metrics rather than presenting them as endpoint timings. Its production metrics documentation describes a Prometheus and Grafana collection path.
Make deployment comparisons fair
Compare options using the same model and workload wherever possible. If infrastructure differs, disclose the configuration rather than treating raw rates as an apples-to-apples result. A useful comparison records:
- TTFT and end-to-end latency distributions at the target concurrency or request rate.
- Aggregate output tokens per second and completed requests per second, with formulas and timing windows.
- ITL or TPOT, including whether the samples represent streamed events or per-request calculations.
- Prompt and output token-length distributions, stream chunk behavior, and prefix-cache policy.
- Warmup policy, benchmark client location, service configuration, errors, and timeouts.
- GPU count and cost or capacity context. NVIDIA recommends normalizing throughput per GPU when compared systems use different GPU counts.
Record the benchmark tool and version with the results: CLI flags, metric definitions, and runtime instrumentation can change between releases. NVIDIA’s AIPerf guide states it was last updated July 20, 2026; consult the documentation matching the versions actually used.
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