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A trustworthy AI-serving benchmark is a controlled experiment, not a race to the highest tokens-per-second figure. Choose the production decision you need to make, freeze the model, software, hardware, workload, scheduler, cache policy and quality target, then vary only the harness or serving stack under test. Measure from a declared point, include queueing and streaming behavior where users experience them, and publish latency distributions, throughput, failures and run-to-run variation.
The protocol below separates cold start from warm service, catches prefix-cache and load-generator effects, and plots the throughput–latency frontier instead of hiding the trade-off at one convenient concurrency. It also gives other teams enough configuration and workload detail to reproduce your result.
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What makes an AI-serving benchmark trustworthy?
A benchmark is credible when another engineer can identify the decision it represents, reproduce its inputs and system state, and see both performance and failures. Treat the run as a controlled experiment: define one factor to study, hold every other material variable constant, and record enough metadata to explain a change.
Start with the decision, not the metric
Write a one-sentence objective before selecting a tool. An interactive assistant may need a first-token SLO; a batch job may care almost entirely about aggregate output tokens per second; a capacity plan needs sustainable throughput at an error-rate limit; a cost study needs the cheapest configuration that meets an accuracy and latency target; a regression test needs a stable, repeatable operating point.
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| Decision | Scenario to model | Primary evidence |
|---|---|---|
| Interactive responsiveness | Streaming requests with realistic prompt and completion lengths | TTFT, inter-token behavior, tail request latency and errors |
| Service capacity | Steady arrival rates and a concurrency sweep until saturation | Sustainable output-token throughput, queue growth, p99 latency and failure rate |
| Offline or batch work | Many queued requests with the batch policy used in production | Total elapsed time, aggregate tokens per second and completion failures |
| Cost at an SLO | The production mix at a stated quality threshold | Hardware or instance consumption while the SLO and quality gate are met |
| Regression detection | A pinned, repeatable slice of the production workload | Distribution shift versus a recorded baseline, not a single peak number |
MLPerf distinguishes single-stream, multistream, server and offline scenarios for this reason. MLPerf Endpoints describes a frontier across operating points rather than pretending that a serving system has one “best” setting: real systems trade total throughput against per-user speed.
Which variables must I freeze before comparing harnesses?
Record the complete system under test in a machine-readable manifest and use the same values for every comparison run. If a variable changes, it is a new experiment, not a fair harness comparison.
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|---|---|
| Model and inputs | Model identifier and revision or commit, weight files and checksum, tokenizer revision, chat template, context limits, prompt distribution and output-token distribution |
| Decoding | Temperature, top-p or other sampling controls, seed policy, stop conditions, maximum new tokens, speculative-decoding settings and beam or sampling mode |
| Endpoint behavior | API and endpoint version, streaming or non-streaming mode, request batching, transport protocol, timeouts, retry policy and whether tokenization occurs client-side or server-side |
| Hardware and software | GPU type and count, tensor or pipeline parallelism, CPU and RAM, clock and persistence policy, network path, driver, CUDA or other runtime, serving-engine version and harness version |
| Serving policy | Scheduler, queue limits, batch-size limits, quantization, memory settings, prefix or key-value cache policy, cache size and eviction behavior |
| Load generation | Arrival process, concurrency levels, client host capacity, fixed random seed, warm-up rule, trial length, repetition count and treatment of retries |
| Quality target | The evaluation set, scoring method, acceptance threshold and any output-normalization policy used to decide whether an optimization is acceptable |
TensorRT-LLM documents a fixed request schema containing a task ID, prompt or input IDs and an output-token count. Adopting an equivalent schema prevents a harness from silently changing tokenization, stopping rules or requested completion length between systems.
Keep the comparison one-factor-at-a-time
If you are comparing two harnesses, keep the serving engine, model revision, scheduler, endpoint and client workload identical. If you are comparing serving stacks, keep the harness and request generator identical. A simultaneous change to quantization, batching and concurrency cannot tell you which change produced the result.
How do I build a representative workload?
Use production traces when privacy and policy permit. Otherwise publish a synthetic generator with a fixed seed and explicit distributions; “one prompt repeated forever” is a cache test, not an interactive-service benchmark.
Describe the request population
- Specify the input-token distribution with at least median and tail values, and do the same for generated output tokens.
- State the proportion of repeated prompts, shared prefixes, tool calls, structured outputs and multimodal requests.
- Define stop conditions, maximum output length, cancellation behavior and whether clients disconnect before completion.
- Document the random seed and the exact corpus or generator version so another team can recreate the sequence.
The IETF methodology separates prefill and decode behavior and calls out streaming explicitly. A workload that omits long-prefill requests can make a scheduler look excellent for short prompts while failing the real mix. MLCommons likewise emphasizes representative scenarios and reproducibility rather than a synthetic maximum.
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A closed-loop concurrency test starts a new request when one finishes; it answers how a fixed number of users behave. An arrival-rate test injects requests at a declared rate; it answers whether the service can sustain a target demand while queues form. Run both when capacity planning matters, because a closed-loop client can hide queue instability by slowing its own arrivals.
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For each test, publish the intended rate or concurrency, the achieved rate, and any deviation. Include dispatch timestamps from the client so a saturated generator is not mistaken for an efficient server.
What warm-up and cache policy prevents inflated results?
Report cold-start behavior separately from warm steady state. State how many requests are used for warm-up, which measurements are excluded, and what event marks the start of the recorded trial.
Separate cold and warm paths
- Start the server from the same image, model files and configuration used in the comparison.
- Measure startup, model loading and the first requests as a cold-start sample if those costs affect the production decision.
- Run a declared warm-up sequence until latency and throughput stabilize; do not silently discard an arbitrary number of requests.
- Record the warm-state trial, then repeat the complete trial enough times to show run-to-run spread.
Decide whether cache reuse is part of the scenario
Prefix or key-value caching can be a real production advantage, but it must be intentional. vLLM warns that repeating vllm bench serve against the same server can reuse prompts left in the prefix cache and inflate throughput. Either reset or restart the cache between independent trials, vary prompts so reuse is controlled, or label the result explicitly as cache-warm and publish the hit-rate policy.
If production traffic has a known warm-prefix ratio, create separate cold, warm-hit and mixed scenarios. Do not average them into one unexplained number.
Where should latency and throughput be measured?
Choose a measurement point before running the test and name it in every chart. Client-side timing reflects what a caller experiences; server-side timing isolates generation and scheduling. Neither is universally “the” latency.
| Metric | Definition and formula | Important qualification |
|---|---|---|
| TTFT | Time from request send to the first streamed output | At the client it includes queueing, tokenization, preprocessing, network and transport; at the server it may exclude those costs. |
| ITL | Gap between consecutive streamed outputs | A chunk can contain multiple tokens, so chunk-to-chunk ITL is not always token-to-token time. |
| TPOT | (end-to-end latency − TTFT) / (output tokens − 1) under the vLLM definition |
State the token-count convention; the expression is undefined for a one-token completion. |
| Request latency | Elapsed time from request start to completion | Keep it distinct from scheduled latency, which includes dispatch delay before service begins. |
| Output-token throughput | Aggregate generated tokens per second across requests | Pair it with per-user speed, tail latency and failures; a peak alone is not a capacity claim. |
| Errors and incompletes | Counts and rates for rejected, failed, timed-out or unfinished requests | Report the denominator and whether retries are counted once or multiple times. |
Metric names are not standardized across tools. vLLM defines TTFT, ITL and TPOT, while GuideLLM exposes dispatch and scheduled latency to show when the load generator or queue is responsible for delay. Publish the timestamps and formulas, not labels alone.
How many concurrent users or arrival rates should I test?
There is no universal concurrency number. Test the range that brackets your expected demand, then continue until you can see saturation, queue growth or the error budget being exceeded.
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Build a sweep
- Select low, nominal, high and overload points from the production forecast; include a single-user point for responsiveness.
- At each point, run a stable window after warm-up and record achieved concurrency or arrival rate rather than only the target.
- Stop increasing load when errors, incomplete requests or unbounded queueing make the result invalid; retain that boundary as an overload observation.
- Repeat points near the expected SLO limit so the decision is not determined by one noisy run.
Plot aggregate output-token throughput against per-user speed for every measured point, and annotate TTFT, tail request latency and error rate. MLPerf Endpoints specifies a step frontier across measured points; a smooth interpolated curve can imply operating points you never tested.
What is the correct benchmark procedure?
Use the following sequence for each harness or serving stack. The only intentional difference between comparison arms should be the factor named in your decision statement.
- Write the scenario contract. State the user journey, request mix, SLOs, quality threshold, streaming mode and whether the result represents cold, warm or mixed service.
- Freeze and capture the environment. Save model and tokenizer revisions, configuration files, container or package versions, GPU inventory, drivers, runtimes, clocks, scheduler and quantization settings.
- Generate the workload deterministically. Store the corpus or generator version, seed, token-length distributions, stop rules, tool or multimodal mix and arrival process.
- Verify the endpoint. Confirm that both systems receive equivalent prompt or input IDs, requested output length, decoding parameters and stream semantics. Record retries and timeouts.
- Run a cold trial. Capture startup and first-request behavior when it affects the decision; otherwise label it and keep it out of warm headline metrics.
- Warm the service. Follow the declared warm-up rule, and establish whether cache reuse is reset, controlled or intentionally measured.
- Execute repeated stable trials. Collect per-request timestamps, token counts, status, error class, queue or dispatch timing and GPU telemetry. Repeat each load point so variation is visible.
- Validate quality. Score outputs against the predeclared test and threshold before accepting a speed, quantization or cost comparison.
- Analyze the frontier. Report distributions, achieved load, failures and the throughput–latency trade-off; never promote the fastest isolated run as the headline.
What should a benchmark report contain?
A reader should be able to reconstruct both the numerator and denominator of every headline metric. Publish a summary table plus raw per-request data or an export schema.
| Report item | Minimum detail |
|---|---|
| System identity | Model and revision, tokenizer and template, engine and harness versions, hardware, driver and runtime |
| Workload | Prompt and output distributions, seed, stream mode, stop policy, cache scenario, arrival process and concurrency points |
| Latency | TTFT, ITL or TPOT, request latency and scheduled or dispatch latency with measurement point and formulas |
| Throughput | Input and output tokens, aggregate output tokens per second and per-user output rate |
| Reliability | Successful, errored, timed-out and incomplete counts, rates and retry treatment |
| Statistics | p50, p90, p95 and p99 where relevant, sample counts and confidence intervals or run-to-run spread |
| Quality | Evaluation set, score, acceptance threshold and output policy |
| Operating frontier | Measured concurrency or arrival-rate steps with throughput, tail latency and errors annotated |
AIPerf reports token metrics and percentile breakdowns; MLPerf Endpoints uses P90 TTFT and per-user output rate. Those examples reinforce a useful rule: choose percentiles and per-user measures that match the SLO, and publish enough raw context to audit them.
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Use a scorecard rather than a single ranking. The following axes expose differences that a tokens-per-second chart can hide.
| Axis | Questions to answer |
|---|---|
| Workload realism | Can it replay production traces or controlled distributions, including streaming, shared prefixes and multimodal or tool traffic? |
| Measurement semantics | Are client and server timestamps clear, and are TTFT, ITL, TPOT and scheduled latency defined? |
| Load control | Can it hold a target arrival rate or concurrency, and does it expose achieved rate and client dispatch lag? |
| Warm-up and cache handling | Can cold, warm-hit and cache-reset scenarios be selected and recorded? |
| Tail behavior | Are p95 or p99 latency, queue depth and overload behavior available? |
| Failure accounting | Are errors, timeouts, cancellations and incomplete generations separated from successes? |
| Reproducibility | Can it pin seeds, versions, request schemas and export the exact configuration? |
| Quality and observability | Can you attach an accuracy gate and capture GPU, memory, power and scheduler telemetry? |
| Raw-data access | Can per-request records be exported for independent percentile and frontier calculations? |
Keep the harness, server and workload roles explicit. MLPerf Endpoints puts it succinctly: “What gets tested is the endpoint itself, not a framework integration.” A harness that adds client-side batching or cannot maintain the requested schedule is part of the measured system and must be disclosed.
How do quality and optimization settings change the verdict?
Performance is not a substitute for answer quality. Quantization, speculative decoding, batching and cache strategies can alter outputs, refusal behavior or tool-call correctness. NVIDIA’s benchmarking guidance treats accuracy validation as a separate responsibility and recommends measuring cost only after an acceptable accuracy result is established.
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Set the quality gate first
- Choose an evaluation set that reflects the production task, including difficult and long-context cases if they matter.
- Define the score, evaluator version and minimum acceptable threshold before looking at speed results.
- Run the same output policy and decoding constraints used in the performance test.
- Exclude configurations that fail the threshold from cost or speed rankings, or present them in a clearly separate exploratory group.
When comparing cost, report the hardware or runtime consumption required to meet both the quality threshold and the service SLO. A cheaper configuration that misses either requirement is not a lower-cost solution for that scenario.
Which traps produce misleading benchmark results?
Prefix-cache contamination
Symptom: later repetitions become implausibly faster with identical prompts. Fix: reset or restart caches, vary prompts with a controlled seed, or report a cache-warm scenario and its reuse rate.
Load-generator saturation
Symptom: the server appears underloaded while target arrivals are missed. Fix: monitor dispatch and scheduled latency, client CPU and network, achieved arrival rate and generator errors. Add generator capacity or distribute clients before judging the server.
Ambiguous latency labels
Symptom: two teams both report “latency” but disagree by queueing and transport overhead. Fix: publish timestamp definitions, formulas, stream chunk rules and the measurement location.
Unrealistic request shape
Symptom: a single short prompt and fixed completion produce excellent averages but fail real traffic. Fix: use measured input and output distributions, realistic stop conditions, streaming and the relevant tool or multimodal mix.
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Symptom: an optimization wins speed after changing quantization or decoding, but nobody checks outputs. Fix: apply a predeclared quality threshold and show the test result alongside performance.
Version drift
Symptom: a rerun changes without an intentional configuration edit. Fix: pin and publish model, tokenizer, engine, harness, scheduler, driver, CUDA or runtime and container versions.
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Symptom: one peak tokens-per-second number is presented as capacity. Fix: sweep concurrency or arrival rate and show tail latency, per-user speed, achieved load and failures across the frontier.
How can I reproduce another team’s tokens-per-second result?
Ask for the artifacts that determine the denominator and numerator, not just a screenshot of a dashboard.
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- The exact model, revision, weights checksum, tokenizer and chat template.
- Prompt corpus or generator, seed, input and output token distributions, stop policy and requested output length.
- Decoding, streaming, endpoint and retry settings, including whether tokenization is client- or server-side.
- GPU model and count, parallelism, CPU and RAM, clocks, driver, runtime, engine and harness versions.
- Scheduler, batching, quantization, cache state and warm-up or reset procedure.
- Concurrency or arrival-rate schedule, achieved load, trial duration, repetitions and excluded samples.
- Per-request output-token counts, elapsed times, status, TTFT and stream timestamps, plus the formulas used.
- Quality evaluation, threshold and any rejected configurations.
Recalculate aggregate throughput from the exported token counts and elapsed measurement window. If the other team cannot state whether its figure is client-side, server-side, cache-warm, cold, streamed or error-free, treat it as an anecdotal result rather than a portable benchmark.
How should I interpret the final chart?
Choose the point on the measured frontier that satisfies the production SLO and quality gate, not the point with the largest aggregate throughput. At low load, per-user speed and TTFT usually dominate the decision; near saturation, queueing and tail latency reveal the cost of additional concurrency. Mark any point with meaningful errors or incomplete requests as outside the valid capacity envelope.
Keep cold-start, cache-warm and cache-reset curves separate when they represent different user experiences. Preserve the raw runs and manifest with the published chart so a future model, driver or scheduler change can be diagnosed instead of mistaken for a harness improvement.
Frequently Asked Questions
Should I report average latency or p99 latency?
Report both when possible, but use the percentile tied to your service-level objective for the decision. Averages can look healthy while a small group of requests times out; p99 is informative only when the sample count is large enough to contain a meaningful tail, so publish the count with it.
How long should a serving benchmark run?
There is no universal minute count. Run each load point until warm behavior is stable and the estimate or run-to-run spread is small enough for the decision, then repeat the point; document the stopping rule instead of choosing a duration that happens to favor one system.
Can I compare streaming and non-streaming endpoints in one chart?
Treat them as separate scenarios unless your users genuinely switch between them. Non-streaming responses have no observable TTFT or ITL, and buffering can make end-to-end latency look better or worse than the streamed experience.
What if prefix caching is an intentional production feature?
Benchmark at least cold, warm-hit and mixed hit-rate scenarios. Publish the hit-rate distribution and cache lifetime, because a result achieved with 100% repeated prefixes will not predict a workload with mostly novel prompts.
What files should accompany a published benchmark?
Include a versioned configuration manifest, workload generator or sanitized trace, launch instructions, raw per-request records, quality-evaluation outputs and plots generated from those records. Hash large model and dataset artifacts so readers can verify that reruns use identical inputs.
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