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This guide gives you a reproducible method for comparing providers without claiming a universal fastest service. Provider performance changes with model version, region, routing, request mix, cache state, and date, so the benchmark is only meaningful when those conditions are explicit.
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Start with the decision your benchmark must support
Write down what you are choosing before sending the first request. An interactive assistant, a streaming coding tool, and an offline document-processing queue have different definitions of “fast.” Interactive users notice startup delay and pauses between tokens. A batch pipeline may accept a higher first-response delay if it receives more completed jobs per minute.
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Set a maximum acceptable time to first token (TTFT), a target for the gaps between streamed tokens, and a full-response latency budget. Measure from the client that a real user or application would run, not from an internal server process. Include the network path and region in the test record.
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Offline or batch inference
Prioritize completed requests per second and generated output tokens per second, then check whether latency remains bounded at the intended queue depth. A high aggregate token rate is useful only if failed, timed-out, or excessively delayed requests are counted and reported honestly.
Mixed workloads
If production combines interactive and batch traffic, benchmark separate classes first, then create a mixed arrival pattern that reflects their proportions. Do not average unlike workloads into one number; show each class and the combined result.
Use precise metric definitions
Metric names are not standardized. vLLM documentation explicitly warns that “Metric terminology is not standardized across benchmarking tools.” NVIDIA’s NIM Benchmarking documentation similarly says, “Tool implementations vary, so compare results only when definitions align.” Before comparing two reports, document the event that starts and ends every timer, treatment of empty stream chunks, token counting, and aggregation method.
| Metric | Operational definition | What it tells you | Important caveats |
|---|---|---|---|
| Time to first token (TTFT) | Elapsed time from sending the 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 | Elapsed time from request submission until the final response token arrives. | How long the complete response takes. | Includes TTFT, generation, queueing, batching, and network latency. NVIDIA describes it as TTFT plus generation time. |
| Inter-token latency (ITL) | Time between consecutive streamed outputs or tokens, measured at the client. | Perceived decode smoothness after the first token. | Aggregations differ. Speculative decoding can emit multiple tokens in one streamed event, so ITL and TPOT may diverge. |
| Time per output token (TPOT) | For each request, (end-to-end latency − TTFT) / (output token count − 1), then aggregated across requests. |
Average decode time after the first token. | Usually close to ITL with standard decoding, but not necessarily with speculative decoding. Definitions may exclude the first-token delay. |
| Request throughput | Completed responses divided by benchmark duration. | How many requests the service finishes per unit time. | State whether failed, timed-out, retried, or rate-limited requests are excluded. |
| Output-token throughput | Generated output tokens divided by benchmark duration. | Aggregate generation capacity. | Not a substitute for per-user speed; more concurrent requests can raise system tokens per second while each user waits longer. |
Why client-side timing matters
A server-side generation timer can omit queueing and transport delay. If your application displays streamed text, capture the timestamp when the client receives each non-empty stream event. Record the client region, provider endpoint, network path, and API mode so another tester can reproduce the same boundary.
Token counting must be explicit
State whether output-token counts come from provider usage fields, a tokenizer run locally, or streamed token events. Different tokenizers and event formats can produce different totals. Keep the raw response and the count used in every calculation.
Freeze the comparison boundary
Use identical conditions for every provider. A benchmark that changes regions, client locations, or protocol modes is measuring several variables at once.
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- Client location: Run all providers from the same machine or region. Record the cloud region, availability zone when relevant, and date.
- Network path: Keep DNS, proxy, TLS, and HTTP settings consistent. Do not compare one provider through a nearby private link and another over a distant public route.
- API mode: Use streaming for all providers when measuring TTFT and ITL. Use non-streaming only for a separate full-response test.
- Endpoint and model: Record the exact endpoint, model identifier, model snapshot or version, and any deployment tier.
- Timing implementation: Use the same monotonic clock and event parser. Document whether the timer starts before serialization, at socket write, or at HTTP request dispatch.
- Authentication and headers: Keep request headers, system prompts, and optional features equivalent. Provider-specific safety or tool settings can alter work and output length.
Construct a representative workload
Model identity alone is not a workload. Prompt length, output length, decoding, cache reuse, and arrival pattern all change latency and throughput.
Record the workload contract
- Model identifier and version or snapshot.
- Prompt-length distribution, including minimum, median, upper percentile, and maximum.
- Prompt content distribution. Use a representative dataset where possible; label synthetic prompts as synthetic.
- Expected output-length distribution and stopping conditions.
- Streaming or non-streaming mode.
- Temperature, top-p, top-k, maximum output tokens, stop sequences, seed, and any provider-specific decoding controls.
- System prompt size and repeated prefixes.
- Total request count and either concurrency or an open-loop arrival rate.
- Retries, timeouts, cancellation behavior, and rate-limit handling.
Use distributions, not one convenient prompt
A single short prompt can make a provider look excellent while saying nothing about your application. Build a fixture that preserves the proportions of short, medium, and long requests. Keep prompt text stable when you need a controlled comparison, then run a second scenario with production-like variation.
Synthetic versus dataset inputs
Synthetic inputs make controlled sweeps easier: you can change only prompt or output length and isolate its effect. A real dataset captures formatting, repeated prefixes, unusual vocabulary, and output variability. NVIDIA GenAI-Perf supports both synthetic and dataset inputs; label which mode you used and keep the dataset version with the benchmark archive.
Run a reproducible benchmark: step by step
- Define acceptance criteria. Write the latency budget, required request rate, expected output size, and whether the workload is interactive, batch, or mixed.
- Pin the software. Record the benchmark tool version, runtime, client library, operating system, tokenizer version, and configuration file. NVIDIA describes GenAI-Perf as under active development, so pin and disclose the version rather than relying on an unrecorded latest install.
- Prepare identical requests. Generate the prompt fixture once, store it with a checksum, and feed the same requests to each provider. Keep decoding parameters and maximum output tokens aligned.
- Verify the event parser. Test that the client identifies the first non-empty token, every subsequent streamed event, the final token, error responses, and connection termination. Save a raw stream sample for audit.
- Warm up each endpoint. Send a documented warm-up phase before collecting measurements. Record its duration and request count. Do not silently mix warm-up observations into the measured sample.
- Set cache policy. Decide whether prefix-cache reuse is part of production. If it is not, vary seeds or prompt prefixes as appropriate, clear or restart the server when you control it, and use the documented vLLM sweep procedure that resets server caches. If repeated prefixes are normal in production, preserve them and report the resulting cache behavior instead.
- Run a light-load baseline. Start with one in-flight request or the lowest open-loop rate that your harness can sustain. This reveals network and single-request behavior before queueing dominates.
- Sweep load. Increase concurrency or request rate through several points until throughput stops improving, tail latency exceeds the budget, errors appear, or the service reaches a stated limit. GenAI-Perf supports both concurrency and request-rate load.
- Repeat each point. Use the same run duration or request count at every load level. Record start and end times, completed requests, failures, timeouts, rate-limit responses, and cancellations.
- Archive raw evidence. Keep CSV or JSON output, exact commands and configuration, prompt fixture checksum, endpoint and region, model version, client location, cache policy, and tool versions. A result that cannot be regenerated is not a dependable comparison.
How many concurrent requests should you test?
There is no universal concurrency number. Test enough points to show the entire transition from idle behavior to saturation. A practical sequence is 1, then small increments, then larger steps as utilization rises; choose values that cover your expected production range and continue until one of three boundaries is clear: throughput plateaus, p95 or p99 latency violates the budget, or errors and rate limits become material.
Concurrency versus request rate
Closed-loop concurrency keeps a fixed number of requests in flight and starts a new request when one completes. It is useful for asking how many simultaneous users the system can serve. Open-loop request-rate testing sends arrivals at a target rate regardless of completion time and is better for finding queue buildup and overload behavior. Report which model you used; the curves are not interchangeable.
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When throughput still rises and tail latency is stable, the service is below saturation. Near the “knee,” a small load increase causes a disproportionate latency increase. Add extra points around that transition instead of spacing every test evenly. This is where a deployment decision is usually made.
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Control warm-up, cache state, and randomness
Repeated runs can accidentally become easier. vLLM benchmark documentation warns that prompt-prefix cache entries may be reused across runs, inflating apparent throughput. A warm cache can be valid for an application with repeated system prompts, but it is not equivalent to a cold-cache workload.
Cold-cache scenario
Use a fresh or cleared cache, or change prefixes so requests do not reuse prior entries. Document the reset method and verify that the first measured run is not inheriting state from warm-up.
Warm-cache scenario
Keep the repeated prefix and report its size, reuse rate, and warm-up policy. Do not compare a warm-cache result from one provider with a cold-cache result from another.
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Random decoding
Set a deterministic seed where the API supports it, but do not assume identical output lengths across providers. If outputs vary, report the observed output-token distribution and use enough requests for the distribution to stabilize.
Choose tools by the question they answer
| Tool | Useful measurements or controls | What to verify before comparing |
|---|---|---|
| vLLM benchmark CLI | Client-side TTFT, ITL, and TPOT for serving benchmarks; documentation calls out prompt-prefix cache reuse across repeated runs. | Its formulas, event boundaries, aggregation, and cache state. vLLM notes that terminology differs among tools. |
| NVIDIA GenAI-Perf | Inference-server throughput and latency using synthetic or dataset prompts, concurrency or request-rate load, TTFT, ITL, request latency, request throughput, and output-token throughput. Exports CSV and JSON and can compare profiles. | Pin the version because the CLI and functionality are under active development. Confirm how the selected profile counts tokens, errors, and streamed events. |
| NVIDIA AIPerf | Metric reference covering 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 reviewed; capture the actual installed version and align formulas with other tools. |
Tools are measurement instruments, not interchangeable scoreboards. Compare the underlying timestamps, included stages, empty-chunk handling, tokenization, and aggregation before combining results from different tools.
Report distributions and draw the right curves
For each load point, publish at least p50 and a tail percentile such as p95 or p99 when the sample size supports it. Include request throughput and output-token throughput, and retain averages as supplementary context rather than the only statistic.
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Latency-throughput chart
Plot throughput on one axis and TTFT or end-to-end latency on the other. Mark each concurrency or request-rate point. A provider that leads at low load may lose its advantage after queueing begins; the curve makes that visible.
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Plot ITL or TPOT against load to show how smoothly responses stream after the first token. Keep the metric definition beside the chart because speculative decoding can make ITL and TPOT diverge.
Capacity table
For each load point, include:
- Target concurrency or arrival rate.
- Completed requests and generated output tokens.
- Request throughput and output-token throughput.
- TTFT p50, p95, and p99 where supported.
- End-to-end latency p50, p95, and p99.
- ITL or TPOT percentiles.
- Error, timeout, cancellation, and rate-limit counts.
- Observed prompt and output-token counts.
Per-user speed versus system speed
Aggregate output tokens per second can increase as more requests run simultaneously, even while each user receives tokens more slowly. Present system throughput and per-request decode pace as separate measures. Never use a system-wide token rate as a substitute for an interactive user’s experience.
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Throughput plateaus while latency rises
This is the classic saturation signature: additional work is waiting in a queue rather than increasing useful compute. Treat the first load point that breaches your latency budget as a practical operating limit, even if later points show a slightly higher token rate.
TTFT rises but ITL stays stable
Queueing, admission control, prefill, or network delay is likely growing while decode capacity remains steady. Check prompt lengths, batching behavior, and the client-to-endpoint path.
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ITL or TPOT becomes erratic
Inspect stream parsing, speculative decoding, pauses caused by batching, and output-length variation. Confirm whether a single streamed event can contain multiple tokens and whether your parser timestamps events or individual token boundaries.
One run is dramatically faster
Check cache reuse, warm-up contamination, a different model snapshot, a shorter prompt or output, and a changed region or route. Compare raw metadata before accepting the result.
Errors appear only at high load
Report them rather than dropping the affected interval. Separate provider rate limits, client-side connection exhaustion, timeouts, and model errors. If you retry, count the original failure and document whether the retried request contributes to throughput.
Make results reproducible for another engineer
Publish a run manifest with every comparison:
- Benchmark date and UTC start time.
- Provider endpoint, region, model identifier, and snapshot or version.
- Client machine or cloud region and network configuration.
- Tool, runtime, SDK, tokenizer, and operating-system versions.
- Prompt dataset checksum, prompt and output-length distributions, and system prompt.
- Streaming mode, decoding parameters, seed, and stop rules.
- Warm-up duration, measured duration or request count, and repetition count.
- Concurrency or open-loop request-rate schedule.
- Cache policy and any reset or restart procedure.
- Definitions and formulas for every metric.
- Counts of successes, failures, retries, timeouts, cancellations, and rate limits.
- Raw CSV or JSON output and the exact command or configuration used.
Structured exports from GenAI-Perf can preserve CSV and JSON evidence. Store those files with the manifest, but remember that the tool’s CLI may change between releases.
Comparing providers without overclaiming
Use the same six comparison axes for every provider:
- Client-perceived startup and completion: TTFT and full-response p50 and tail latency.
- Decode pace: ITL or TPOT under the same streaming and decoding conditions.
- Capacity: completed requests and generated tokens per second across the load sweep.
- Scaling behavior: where throughput plateaus and where latency budgets are crossed.
- Workload fidelity: matching input and output lengths, repeated prefixes, and cache behavior.
- Measurement conditions: region, endpoint, model snapshot, date, client path, and tool version.
Do not declare a permanent “fastest provider” from one run. The result applies to the tested model, workload, route, date, and load schedule. Pricing or cost per completed task can be added only after retrieving and dating current provider pricing; a latency benchmark by itself does not establish that comparison.
Quick Recap
A compact checklist before publishing
- Are TTFT, end-to-end latency, ITL or TPOT, request throughput, and output-token throughput defined by measurement event and formula?
- Did every provider use the same model version, prompt fixture, decoding settings, client location, API mode, and load schedule?
- Did you separate warm-up from measured runs and disclose cache policy?
- Did you sweep enough concurrency or request rate to show both light load and saturation?
- Are p50 and tail percentiles shown for each load point?
- Are failures, retries, timeouts, and rate limits visible in the data?
- Can another engineer retrieve the raw CSV or JSON, manifest, exact command, and tool version?
- Does the conclusion state the tested conditions instead of naming an unconditional winner?
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