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The fairest way to compare hosted AI inference providers is to measure a latency-throughput curve with the same model snapshot, workload, client location, API mode, and load pattern. Record client-observed streaming timings, sweep concurrency or request rate from light load to saturation, and publish percentile latency alongside completed requests per second and generated output tokens per second. A single “tokens per second” figure cannot show whether a service is responsive at low load or collapses when demand rises.
This guide gives a reproducible procedure for interactive and batch workloads, defines the metrics precisely, explains how to use common benchmarking tools, and shows how to report results without implying a provider winner that your test did not establish.
What a provider benchmark should answer
Start by writing the decision your benchmark must support. An interactive assistant needs a short startup delay and a smooth stream of tokens for each user. An offline summarization or batch pipeline may tolerate a long wait for the first result if it maximizes total output per hour. Those are different optimization problems, so use separate test profiles rather than one blended score.
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- Batch profile: prioritize completed requests and output-token throughput while checking that queueing and total job time stay within an operational limit.
- Capacity profile: identify the load at which throughput stops improving, errors begin, or latency exceeds your service-level objective.
The result should be a curve, not a leaderboard claim: each point represents one controlled load level and includes the conditions under which it was measured. Provider speed can change with model snapshot, region, routing, request mix, and date; a benchmark is therefore a dated observation of a specified configuration.
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Use explicit metric definitions
Metric names are not standardized across benchmarking tools. vLLM documentation states, “Metric terminology is not standardized across benchmarking tools.” NVIDIA’s NIM benchmarking documentation similarly advises, “Tool implementations vary, so compare results only when definitions align.” Before combining results, record the event that starts and ends each timer, how empty stream chunks are handled, and whether aggregation is per request or across all tokens.
| Metric | Definition to publish | What it tells you | Important caveat |
|---|---|---|---|
| TTFT | Elapsed time from sending a request until the client receives its first non-empty streamed token. | User-visible startup delay. | Can include queueing, batching, prefill, tokenization-related work, and network delay. Long prompts often increase it. |
| End-to-end request latency | Request submission until the final response token arrives. | Total wait for a complete response. | Includes TTFT, generation, queueing, batching, and network effects. NVIDIA describes it as TTFT plus generation time. |
| Inter-token latency (ITL) | Time between consecutive streamed outputs or tokens, measured at the client. | How continuously a stream arrives after startup. | Speculative decoding can emit multiple tokens in one streamed event, so ITL and time per output token can diverge. |
| Time per output token (TPOT) | For one request: (end-to-end latency − TTFT) / (output token count − 1), then aggregated across requests. |
Average decode time after the first token. | With standard decoding it is usually close to ITL; speculative decoding and event grouping can produce a difference. The denominator excludes the first token. |
| Request throughput | Completed responses divided by benchmark duration. | How many jobs the service finishes per unit time. | A response that errors, times out, or is abandoned is not a completed request; state your counting rule. |
| Output-token throughput | Generated output tokens divided by benchmark duration. | Aggregate generation capacity. | It is not the same as the speed experienced by one user. |
Report both aggregate output tokens per second and per-request behavior. As concurrency rises, system tokens per second can increase because the server keeps more hardware busy, while each user’s token rate worsens. Neither number substitutes for the other.
Fix the comparison boundary
Measure at the boundary that matters to your application. If users see streamed text, start the timer immediately before the client sends the request and stop TTFT when the client receives the first non-empty token. A server-side timestamp can hide network and client effects that users actually experience.
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- Use the same API mode, authentication path, TLS settings, streaming setting, timeout policy, and retry policy.
- Record the provider endpoint and region for every run. Do not silently allow a provider-specific router to choose different regions between tests.
- Use a synchronized clock and a single timing implementation. Capture raw arrival timestamps, not only summary values emitted by a tool.
- Decide whether client tokenization is part of the measurement. If the API accepts text and the client performs token counting separately, document that choice rather than mixing client-side and server-side token counts.
TTFT can include queuing, prefill, tokenization-related work, and network delay. That is a feature when measuring user experience, not noise to be removed without explanation.
Design a workload that resembles production
Pin the model and request parameters
Record the exact model identifier and version or snapshot, system prompt, temperature, top-p or top-k settings, maximum output limit, stop sequences, random seed where supported, and any structured-output or tool-calling mode. A provider label alone is not sufficient: two endpoints carrying the same model family can use different snapshots, quantization, context limits, or server settings.
Represent prompt and output lengths
Create distributions, not one convenient example. Measure short, median, and long prompts and expected output lengths that reflect the application. Keep prompt content fixed when comparing providers if semantic variation could alter generation, but include a representative dataset when content-dependent behavior matters. Synthetic prompts are valuable for controlled sweeps; label them synthetic and do not present their results as production performance.
Choose streaming and decoding deliberately
Use streaming for an interactive profile and non-streaming only when the application really waits for a complete response. Keep decoding parameters identical. Record whether the server can send multiple tokens in one streamed event, because that affects ITL and TPOT calculations.
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Model arrivals realistically
A closed-loop test keeps a fixed number of clients active and starts a new request only after one finishes. It approximates a bounded pool of users. An open-loop test sends requests at a target rate regardless of completion and exposes queue growth and overload behavior. State which model you use. NVIDIA GenAI-Perf supports concurrency and request-rate load, allowing both styles of sweep.
Control repeated prefixes and cache state
Repeated vLLM benchmark runs can reuse prefix-cache entries and inflate apparent throughput. If production does not reuse a prefix, clear or vary state between runs; changing seeds or restarting the server are possible controls, and vLLM documents a sweep command that resets server caches between runs. If production does contain a repeated system prompt or document prefix, preserve that pattern and report the cache behavior as part of the workload. Do not compare a warm-cache run with a cold-cache run.
Choose and pin a measurement tool
| Tool | Useful capabilities | What to disclose |
|---|---|---|
| vLLM benchmark CLI | Serving benchmarks with client-side TTFT, ITL, and TPOT; documentation warns about prompt-prefix cache reuse across repeated runs. | Tool version, command, timing boundary, cache policy, and how streamed events were converted to tokens. |
| NVIDIA GenAI-Perf | Inference-server throughput and latency measurements with synthetic or dataset prompts, concurrency or request-rate load, TTFT, ITL, request latency, request throughput, and output-token throughput. It can export CSV and JSON and compare profiles. | Pin and report the version because the CLI and functionality are under active development. Preserve the generated files and command configuration. |
| NVIDIA AIPerf | Documentation defines request latency, TTFT, ITL, output-token count, request throughput, and output-token throughput. | The documentation identified version 0.13.0 as latest at the time it was accessed; capture the actual installed version for your run rather than assuming it remains current. |
Tools can disagree even when labels match. Compare the measurement point, whether queueing and network time are included, how empty stream chunks are treated, whether tokenization is counted, and whether the statistic is calculated per request or from pooled events.
Run the benchmark step by step
1. Write the decision and acceptance limits
Specify an interactive TTFT target, an end-to-end latency target, an acceptable p95 or p99 tail, and the minimum request or output-token throughput needed. Define separate limits for batch jobs. Without these limits, a chart can show differences without telling you whether either service is acceptable.
2. Create a run manifest
Make a machine-readable manifest containing provider endpoint and region, model identifier and snapshot, client region, API mode, streaming flag, decoding parameters, prompt and output length distributions, request count, load model, warm-up policy, duration, timeout and retry rules, cache policy, tool name and version, operating-system and runtime versions, and the UTC date.
3. Validate one request manually
Send a single request to each endpoint before loading it. Confirm that the model, prompt, output limit, stream format, token counting, and final-response marker are what your harness expects. Save the raw event sequence, including empty chunks, errors, and finish reasons.
4. Warm up consistently
Use the same warm-up count or warm-up duration for every provider. Exclude warm-up requests from reported statistics, but retain their logs. Warm-up may populate server, connection, or prefix caches; therefore state whether the measured phase is intentionally warm or cold.
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5. Run a light-load baseline
Begin with one concurrent request or a low open-loop rate. This reveals the minimum service and network overhead before queueing dominates. Run long enough to collect a useful sample and use the same duration or completed-request target across providers.
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Increase load through several points, for example 1, 2, 4, 8, 16 and higher concurrency, or an equivalent series of request rates. Continue until output-token throughput flattens, latency crosses its budget, error and timeout rates rise, or the endpoint enforces a limit. The exact points should fit your expected traffic and rate limits; do not stop at a convenient peak.
7. Repeat each load point
Run multiple independent repetitions, using a documented randomization or ordering plan so one provider does not always run first. Keep the same cache policy and workload seed. Record interruptions, rate-limit responses, retries, and timeouts separately from successful responses.
8. Export raw and summarized data
Save per-request start time, first-token time, final-token time, output-token count, status, error class, retry count, and load assignment. Export tool summaries in CSV or JSON when available. Store the exact command and manifest beside the files so another person can rerun the test.
9. Check data quality before plotting
- Verify that every successful streamed request has a first non-empty token and a final marker.
- Check that output-token counts are positive and consistent with the tokenizer or provider usage field you selected.
- Separate retries from original attempts and state whether retries are included in throughput.
- Inspect timestamps for clock jumps, client pauses, connection resets, and batches of identical values.
- Confirm that each load point has enough completed samples to support the reported tail percentile.
How many concurrent requests should you test?
There is no universal correct concurrency. Test the range your application can generate, then continue beyond it until you observe saturation. A practical sweep starts at one request, doubles or otherwise increases load geometrically, and adds points near the latency budget crossing. For an open-loop workload, choose rates below, near, and above the expected arrival rate.
Interpret the sweep as a queueing curve:
- Underloaded region: throughput rises roughly with offered load and latency remains stable.
- Transition region: throughput gains shrink while p95 or p99 latency begins to climb.
- Saturated region: additional load produces little or no throughput improvement, while queueing, timeouts, or errors increase.
Report the highest load that meets your latency and error budgets, not merely the highest observed throughput. A provider that reaches a larger aggregate token rate only after violating your interactive target is not the better choice for that workload.
Calculate and report distributions
For every load point, publish at least p50 and a tail percentile such as p95; include p99 when the sample size supports a stable estimate. Show request throughput and output-token throughput beside TTFT and end-to-end latency. Averages are useful context but should not be the only statistic because a small number of slow requests can determine user experience.
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| Load point | TTFT p50 / p95 | End-to-end p50 / p95 | ITL or TPOT | Completed requests/s | Output tokens/s | Errors and timeouts |
|---|---|---|---|---|---|---|
| Concurrency or request rate used | Milliseconds, with timer boundary stated | Milliseconds, final-token boundary | Milliseconds per token or streamed event, with formula | Successful completions divided by measured duration | Generated output tokens divided by measured duration | Count and rate, separated by class |
Plot throughput against TTFT and against end-to-end or decode latency. Annotate the point where a latency budget is crossed. Keep request rate and concurrency visible on the chart so readers can distinguish a high-capacity point from a low-latency point.
Understand common misleading results
A high tokens-per-second result with poor user experience
Aggregate output-token throughput can rise as more requests run simultaneously, even while each request waits longer in a queue and receives tokens less smoothly. Present system throughput and per-request TTFT, ITL, and end-to-end latency together.
Low TTFT but slow completion
A service can begin streaming quickly and decode slowly. This may feel responsive for a chat interface but be unsuitable for a workflow that needs the complete answer promptly. Use both TTFT and final-token latency.
Low average latency with a dangerous tail
Queueing and transient routing effects often appear in p95 or p99 before they move the mean. If your application has a strict response target, select against the tail percentile that matches your service-level objective.
Different ITL and TPOT values
With ordinary one-token-at-a-time streaming, ITL and TPOT are usually similar. Speculative decoding or event batches can contain multiple tokens, so the gap between streamed events is not always the time for one output token. Explain which quantity your tool reports and avoid treating the labels as interchangeable.
Reproducibility that improves after every run
Repeated prefixes may be entering a server-side cache. Compare a cold or varied-prefix condition with the production-like warm-cache condition, and never mix them in one curve.
Troubleshoot unstable or invalid runs
- TTFT spikes at all providers: check client-region distance, DNS and TLS setup, connection reuse, local CPU saturation, and whether the endpoint was rate-limited. Repeat from the same host before attributing the change to model performance.
- Throughput increases without an obvious ceiling: extend the sweep, verify that the benchmark duration is long enough, and inspect whether output lengths are shorter than intended. A short-output workload can hide decode saturation.
- One provider shows many missing first tokens: inspect stream parsing for empty chunks, comments, keep-alives, and provider-specific event formats. Define TTFT as the first non-empty token and log malformed streams separately.
- Results vary by run order: randomize provider order, use equal warm-up, and check for shared client or network contention. Record the UTC start time because provider load can change during the day.
- Tail percentiles are erratic: increase the number of completed requests at each load point and report the sample count. Do not imply precision that the sample cannot support.
- Retries inflate apparent capacity: maintain separate attempted, successful, retried, and failed counts. State whether throughput uses wall-clock duration including backoff.
- Tool summaries cannot be reconciled: reproduce one request from raw events, compare timer boundaries and token counts, and use one tool consistently for the provider comparison.
Make provider comparisons genuinely comparable
Compare providers only when these axes are aligned:
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- Client-perceived startup and full-response latency, including the same p50 and tail percentiles.
- Decode pace, with ITL or TPOT defined from the same event and token rules.
- Completed requests and generated tokens per second across the same load sweep.
- Behavior as concurrency increases and as latency budgets are crossed.
- Input and output length distributions, decoding settings, streaming mode, and cache behavior.
- Endpoint, region, model snapshot, client location, network path, date, tool version, and server-side options that are exposed.
Price per token or cost per completed task can be added to an engineering decision, but it requires current, dated pricing for the exact model, region, tier, and any input/output distinction. Do not combine an old price with a new performance run or infer cost from token throughput alone.
Archive enough evidence to reproduce the result
Keep the raw CSV or JSON output, per-request logs, benchmark command, manifest, and analysis script together. Include:
- UTC date and time, provider endpoint, region, model identifier and version, and client region.
- Hardware, operating system, runtime, network environment, and benchmark-tool version.
- Prompt and output length distributions, representative samples or dataset identifier, system prompt policy, and decoding parameters.
- Streaming and API mode, concurrency or request-rate schedule, duration, request count, warm-up policy, and cache state.
- Timeout, retry, rate-limit, failure, and exclusion rules.
- Metric formulas, percentile method, token-count source, treatment of empty chunks, and whether timing includes network delay.
GenAI-Perf can export CSV and JSON, which makes a useful archival starting point, but its command-line interface is documented as under active development. Pin the version and retain the exact invocation rather than relying on a future default.
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Open the published report with a one-sentence scope statement: model snapshot, endpoint regions, client location, workload profile, and test date. Follow it with a table of conditions, then one chart per profile showing throughput against TTFT and end-to-end latency. For each load point, provide sample count, p50, tail latency, request throughput, output-token throughput, and failures.
Explain the practical decision in terms of the stated limits: for example, identify the load range in which a profile remains below its TTFT and p95 budgets, then separately identify the highest sustainable output-token rate. If the curves cross, report the trade-off instead of forcing a single ranking. Include the raw-data location and enough commands and configuration to let another engineer repeat the run.
What this method can and cannot establish
A controlled benchmark can show how specified endpoints behaved for a specified model, workload, client location, and date. It cannot establish a permanent fastest provider. Routing, capacity, model updates, regional placement, cache policy, and traffic conditions can change the result. Re-run the same protocol when any of those variables changes, and treat a provider comparison as a dated engineering measurement rather than a timeless property.
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