The Tool Desk
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Benchmark the exact serving workload you expect to run, on every hardware candidate, and choose the operating point that meets your latency target at the required concurrency. Do not select a GPU or accelerator from one peak tokens-per-second figure. Measure time to first token (TTFT), inter-token latency (ITL, also called time per output token or TPOT), complete-response latency, aggregate output-token throughput, and cost under the same model, software stack, request mix, and timing rules.
A useful comparison shows how each system behaves as concurrent requests increase: responsiveness usually worsens while aggregate throughput rises toward saturation. First filter out systems that miss your user-facing latency objective; then compare the qualifying systems on throughput per accelerator and throughput per dollar using an explicitly stated cost basis.
What does an AI inference benchmark actually need to prove?
An inference benchmark is useful only if it predicts the experience and capacity of your production service. That means reproducing the model, precision, prompt and response lengths, streaming behavior, request mix, concurrency, and serving configuration you will deploy. A synthetic test with a different shape can rank hardware incorrectly.
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The result should answer two separate questions. First, can one user receive a response quickly enough? Second, how much total work can the system complete when many users arrive together? Those questions require different measurements. Per-user speed can decline as concurrency rises even while system-wide token throughput increases.
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Report measured operating points rather than a single “maximum” score. MLPerf Endpoints describes performance as a frontier across concurrency, combining system throughput with per-user speed and first-token latency. Its published frontier is a step function because interpolation would imply operating points that were never measured. Your own report should preserve the same discipline.
Which inference metrics should you measure?
Use a metric set that separates initial responsiveness, streaming pace, complete-response time, and capacity. Define every metric in the report because benchmark tools do not always calculate the same label in the same way.
| Metric | What it measures | Important interpretation |
|---|---|---|
| TTFT (time to first token) | Elapsed time from request submission until the first non-empty output token or chunk | Can include queueing, prompt processing, first-token generation, and network latency; longer prompts can increase it |
| ITL (inter-token latency) / TPOT (time per output token) | Generation pace after the first token | Tools differ on whether TTFT is excluded and how token counts are handled; state the formula |
| End-to-end latency | Time from submission until the complete response is received | Depends on both first-token delay and the tested output-length distribution |
| System output-token throughput | Total generated output tokens per unit of time across requests | Usually rises with concurrency until saturation, while individual users may see slower generation |
| Completed requests per second | Finished responses per unit of time | Useful when requests have comparable output lengths; interpret alongside token throughput |
How should TTFT be interpreted?
TTFT is the user’s wait before any visible output. It is not just model generation time: a queue, prompt processing, creation of the first token, and transport across the network may all be inside the interval. Keep those contributors in the measurement if they exist in the deployed path; removing them can make a hardware choice look better than the service users will experience.
Prompt length matters. A candidate that appears responsive with short prompts may have a substantially longer first-token wait when your application sends long context. Include the prompt-length distribution you actually expect instead of reporting only a short fixed prompt.
What is ITL or TPOT, and why do formulas differ?
ITL describes the pace after the first token, which is the interval users perceive while a streamed answer is arriving. Some tools call the same concept TPOT. The name is not enough to make two results comparable.
NVIDIA’s AIPerf Metrics Reference, version 0.13.0, excludes TTFT from its ITL calculation and divides the remaining time by the output-token count minus one. NVIDIA’s 2025 benchmarking guidance notes that GenAI-Perf and LLMPerf do not treat ITL identically: GenAI-Perf excludes TTFT, while LLMPerf includes it. Write the exact equation, included token counts, and aggregation method beside your result.
Why is end-to-end latency still necessary?
A service can show a fast first token but take a long time to finish a large answer. End-to-end latency captures the complete user-visible transaction for the response-length distribution you tested. Read it with TTFT and ITL rather than substituting one for the others.
What is the difference between per-user speed and system throughput?
Per-user speed describes how quickly one request receives tokens. System output-token throughput sums generated tokens across all active requests. Increasing concurrency can push aggregate throughput upward toward saturation while reducing each user’s generation speed, so a higher system number does not automatically mean a better interactive experience.
How do you define a workload that makes hardware comparisons fair?
Freeze the workload before testing. Every candidate must receive the same model, precision, request distribution, client behavior, and serving features; otherwise you are measuring configuration differences rather than hardware.
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Choose the model and precision
Record the exact model identifier, version or checkpoint, quantization or precision, and any model-specific serving options. A change in precision can alter memory use, kernel selection, quality, and speed, so do not compare one candidate in one precision with another in a different precision unless that is the deployment decision you are evaluating.
Represent prompt and output lengths realistically
Use distributions, not only averages. Document the prompt-length buckets and the expected output-length buckets, then replay the same mix on every system. Long prompts stress prompt processing and can raise TTFT; long outputs put more weight on generation pace and total completion time.
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Include streaming and request behavior
State whether responses stream token by token or arrive only after completion. Record the client’s request interval, timeout behavior, connection reuse, and retry policy. A benchmark that measures a batch of isolated calls is not interchangeable with one that keeps a live stream of concurrent users.
Match the serving stack
Record the inference server, runtime, driver and accelerator software, parallelism settings, batching policy, maximum context or sequence limits, and hardware configuration. Keep these settings aligned across candidates or label a result as a software-and-hardware comparison rather than a hardware-only comparison.
How should you set latency and capacity targets before testing?
Set pass/fail objectives before you see the results. At minimum, choose a TTFT target, an ITL or TPOT target, and an end-to-end latency target, each with the percentile you will enforce for your service-level objective. Averages alone can hide a damaging tail.
Google Cloud’s accelerator benchmarking guidance presents example latency thresholds as guidance, not universal standards. Use targets derived from your product: an interactive assistant, a batch extraction service, and an offline generation pipeline can reasonably have different objectives. Write down whether the target applies at low load, at the intended concurrency, or throughout a specified operating range.
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Also define the capacity requirement: expected concurrent requests, minimum aggregate output-token rate, or completed requests per second. If both a latency ceiling and a throughput floor matter, treat both as hard constraints when selecting hardware.
How do you run the benchmark step by step?
Use the same client and timing policy for every candidate. A staged run exposes both idle responsiveness and behavior near the load your service must support.
- Freeze the test plan. Write down the model and precision, prompt and output distributions, streaming mode, request mix, concurrency levels, latency targets, throughput target, software stack, and hardware configuration.
- Define metric formulas. State when the clock starts and stops, what counts as the first non-empty token or chunk, whether TTFT is excluded from ITL, which output tokens enter the denominator, and how percentiles are calculated.
- Choose a warmup policy. Specify how warmup requests are handled and exclude or include them consistently. The policy must be identical across systems because tools and benchmark harnesses differ in their treatment of warmup.
- Run a low-load baseline. Start with a concurrency that introduces little or no queueing. Capture TTFT, ITL or TPOT, end-to-end latency, output-token throughput, and completed requests per second. This establishes the candidate’s near-idle behavior.
- Increase concurrency gradually. Add concurrent requests in controlled steps. At each step, collect both per-user latency and system output-token throughput, not just one aggregate score.
- Hold each step for a defined window. Use the same request interval and measurement duration for every candidate. Record exactly when throughput timing begins and ends; NVIDIA’s benchmarking guidance notes that tools use different timing windows.
- Stop at the decision boundary. Continue until throughput saturates or the latency target is breached. Keep the last passing point and the first failing point; both show where the useful operating range ends.
- Repeat the run. Re-run the same sequence with the same client, warmup, request interval, and measurement window. Investigate large variation rather than averaging it away without explanation.
- Preserve raw context. Store the configuration, request mix, concurrency, metric definitions, tool version, and measured points with the results so another operator can reproduce the comparison.
How should you compare candidates across concurrency?
Plot or tabulate the measured points for each concurrency level. The best choice is usually not the system with the highest unconstrained throughput; it is the system that satisfies your latency objective while delivering the required capacity at a sustainable operating point.
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Find the usable operating range
For each candidate, identify the highest concurrency at which the selected TTFT, ITL or TPOT, and end-to-end latency percentiles still pass. Then check the aggregate output-token rate at that point. A system that wins only after violating the latency objective is not a valid winner for an interactive workload.
Distinguish saturation from a temporary knee
As concurrency rises, throughput may flatten while queueing and per-user latency continue to grow. Treat the flattening region as saturation evidence, but report the measured points rather than drawing a smooth curve through them. The exact knee depends on the workload and serving stack.
Use completed requests per second carefully
Requests can have different output lengths, so completed requests per second can favor a system serving shorter responses. Pair it with output tokens per second and publish the response-length distribution. For token-heavy generation, aggregate output-token throughput is often the more comparable capacity measure.
What comparison axes belong in a hardware decision?
Use the same axes for every candidate and keep the definitions visible in the report.
| Axis | What to report | Decision question |
|---|---|---|
| Responsiveness | TTFT and the chosen percentile at each concurrency | Does a request begin producing visible output quickly enough? |
| Streaming experience | ITL or TPOT, formula, token-count rule, and aggregation method | Does generation remain smooth after the first token? |
| Completion time | End-to-end latency for the tested response-length mix | How long does a complete answer take? |
| Capacity | Output tokens per second and, where useful, completed requests per second | How much work can the system serve at each load? |
| Operating point | Latency and throughput together across measured concurrency levels | Where does the candidate meet both service and capacity requirements? |
| Economics | Throughput per accelerator and throughput per dollar, with cost assumptions | What qualifying capacity are you buying for the stated cost? |
| Reproducibility | Model, software stack, workload, hardware configuration, and benchmark-tool version | Can another operator repeat the result? |
How do benchmark tools and timing windows affect results?
Tool labels are not universal definitions. NVIDIA’s NIM Benchmarking Documentation warns that tool implementations vary and results should be compared only when definitions align. Before placing two charts side by side, inspect their formulas and timing windows.
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NVIDIA’s 2025 article explains that GenAI-Perf excludes TTFT from its ITL calculation while LLMPerf includes it. AIPerf’s metrics reference describes an ITL calculation that excludes TTFT and divides the remaining time by output-token count minus one. These choices can materially change the reported value even when the underlying requests are identical.
The same guidance identifies different throughput timing windows across tools. One harness may start timing when requests are issued; another may use a later or narrower interval. Copy the actual formula and start/stop events into your report instead of comparing the metric name alone.
How should you handle a benchmark-tool version?
Record the exact tool and version, then retain its configuration. A later release may alter metric definitions, warmup handling, or timing behavior. Re-run both candidates with the same version when you need a controlled comparison.
How do you normalize performance to cost?
Apply cost normalization only after latency filtering. Google Cloud recommends measures such as throughput per chip and throughput per chip per dollar (TPS/chip and TPS/chip/$). Use the same workload and the same measured operating point for every candidate.
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State what “dollar” means: purchase price, a rental rate for a defined period, or another basis. Include the number of accelerators used and specify whether the calculation covers only accelerator charges or also the other costs relevant to your decision. The cited guidance does not define a universal model for electricity, cooling, utilization, or service operations, so disclose which of those you included.
Do not divide a peak throughput number by price if that peak violates your latency objective. The meaningful numerator is qualifying throughput at the operating point your application can actually use.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What details should a benchmark report publish?
A reproducible report lets a reader understand exactly what was measured and why the conclusion applies to the intended service.
- Model name, version or checkpoint, precision, quantization, and context limits used.
- Accelerator model, count, memory configuration, host configuration, and interconnect details relevant to serving.
- Inference server, runtime, driver, framework, parallelism, batching, and other serving settings.
- Prompt-length and output-length distributions, request mix, streaming mode, and client behavior.
- Concurrency levels, request interval, warmup policy, measurement-window start and end, and repetition policy.
- Definitions and equations for TTFT, ITL or TPOT, end-to-end latency, throughput, and percentiles.
- Measured per-user and system metrics at every concurrency point, including the passing and failing boundary.
- Cost basis and the components included in throughput-per-dollar calculations.
- Benchmark-tool name and version, plus enough configuration to reproduce the run.
Vendor benchmark data can be useful evidence when its model, workload, software, and measurement conditions match your intended use. If those conditions differ, treat the result as background information rather than a direct ranking of your candidates.
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Using one peak throughput number
Peak throughput omits the concurrency and latency conditions that produced it. A system can post a larger aggregate number only by making each user wait longer. Always show the latency-throughput relationship.
Mixing metric definitions
Calling two values “ITL” does not make them equivalent when one includes TTFT or uses a different token denominator. Put the formula beside each result and align definitions before comparing.
Changing the workload between systems
Different prompts, response lengths, precision, streaming behavior, or batching settings turn the test into a collection of unrelated experiments. Keep the request mix constant.
Ignoring tails
An average can look acceptable while a high percentile fails the user-facing objective. Select the percentile before testing and enforce it consistently across candidates.
Hiding the timing window
Throughput can change depending on whether timing includes request submission, queueing, draining, or only a steady-state interval. Publish the exact start and stop events.
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Comparing unmatched vendor results
Vendor figures are not interchangeable when the model, prompt and output lengths, software stack, concurrency, or metric formulas differ. Reproduce the workload locally before using a vendor number to make a purchase decision.
Optimizing cost before service quality
A cheaper accelerator with attractive tokens-per-dollar is not a bargain if it misses the latency requirement. First discard failing operating points, then normalize the qualifying results.
How can you turn the measurements into a final hardware choice?
Create a candidate scorecard with one row per measured concurrency point. Mark whether each latency objective passes, record aggregate output-token throughput, and calculate normalized cost only for passing points. The preferred system is the one that meets the service envelope with adequate headroom and the best economics under your stated assumptions.
If two candidates pass, choose based on the requirement that matters most: lower TTFT for immediate interaction, lower ITL for a smoother stream, shorter end-to-end latency for complete answers, or higher aggregate capacity for many simultaneous users. Keep the raw measurements so the decision can be revisited when the model, request mix, or traffic profile changes.
Frequently Asked Questions
Do longer prompts always make inference slower?
They can increase TTFT because the server must process more input before producing the first token, but the effect depends on the serving system and workload. Measure the prompt-length distribution you expect rather than extrapolating from a short-prompt result.
Can I compare a benchmark that uses tokens per second with one that uses requests per second?
Only with care. Requests can produce different numbers of output tokens, so requests per second may reward shorter responses; include the response-length distribution and compare output-token throughput when generation work is the main concern.
Should warmup requests count toward my production capacity result?
Decide that before running the test and apply the same policy to every candidate. Publish whether warmup was excluded or included, because inconsistent warmup treatment can change both latency and throughput measurements.
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