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The safest way to choose inference hardware is to benchmark the exact serving workload you plan to run, then compare latency, throughput, concurrency and cost at the same operating points. A peak tokens-per-second number is not enough: one system may deliver the fastest first token, another may sustain more aggregate traffic, and a third may meet your latency target at the lowest cost. Use the same model, precision, prompt and output distributions, software stack, benchmark client, warmup policy and timing window on every candidate. Measure time to first token (TTFT), inter-token latency (ITL or TPOT), end-to-end latency and aggregate output-token throughput as concurrency rises. Keep only systems that satisfy your user-facing latency objective, then compare their normalized throughput and stated cost basis.
Why should you benchmark the serving workload instead of relying on a vendor headline?
Inference performance depends on the interaction between a model, its precision, request lengths, batching behavior, software stack and concurrency. A hardware specification or a vendor’s maximum throughput can describe a different workload from yours. For example, a test using short prompts and short responses may favor a device that behaves differently when your application sends long context windows or streams lengthy answers.
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Benchmarking the real serving path exposes trade-offs that a single maximum cannot show. At low load, you can see how quickly one user receives a response. As more requests arrive, you can see when queueing begins, how much each user’s generation speed falls, and how much total capacity the system adds. The result is an operating curve, not one leaderboard number.
Use published vendor figures as supporting evidence only when their model, workload, software and measurement definitions match your intended deployment. If those conditions differ, reproduce the workload yourself before committing to hardware.
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What must be fixed before the first run?
Write a workload specification and apply it unchanged to every candidate. Changing any of these inputs between systems makes the comparison ambiguous.
Model and numerical precision
- Record the exact model revision, tokenizer and serving implementation.
- Record precision and quantization, such as the data type or quantization scheme used for weights and activations.
- Keep context limits, speculative-decoding settings, batching limits and other serving parameters aligned unless the comparison is explicitly about those settings.
Prompt and response distributions
Use the prompt-length and output-length distribution your application expects, not only an average. State minimum, typical and long cases when they materially affect queueing or memory use. Longer prompts can increase TTFT because prompt processing is part of the wait before the first token.
Request mix and streaming behavior
Define whether requests stream tokens or return a complete response. Specify the mix of request types, their arrival pattern and the concurrency levels to test. A chat service, a batch summarizer and an offline embedding-style job have different success criteria, so they should not share an unstated benchmark profile.
Software and hardware configuration
- Identify accelerator model and count, host CPU and memory, interconnect, and storage relevant to model loading.
- Record operating-system, driver, runtime, serving-server and kernel versions.
- Use the same model files, scheduling configuration and precision on all systems where the goal is a hardware comparison.
Which inference metrics answer different user questions?
Collect all four primary timing views. They describe different parts of the same request and should not be substituted for one another.
| Metric | What it measures | How to interpret it | Definition detail to report |
|---|---|---|---|
| TTFT (time to first token) | Time from request submission until the first non-empty output token or chunk is received. | Represents initial responsiveness. Queueing, prompt processing, first-token generation and network latency can all contribute. | State whether the clock includes client, network and queue time, and report the percentile used for your service target. |
| ITL or TPOT (inter-token latency or time per output token) | The pace of generation after the first token. | Lower is generally smoother for a streaming user, but tool formulas and token-count conventions differ. | State whether TTFT is excluded and which output-token count is used. NVIDIA AIPerf excludes TTFT and divides the remaining time by output-token count minus one. |
| End-to-end latency | Elapsed time from submission until the complete response is received. | Shows completion time for the tested response-length distribution. A fast first token does not guarantee a fast complete answer. | Define exactly what event stops the timer, especially for streamed responses. |
| System output-token throughput | Total output tokens produced per second across all active requests. | Measures aggregate capacity. It can rise as concurrency increases even while each user’s generation speed declines. | Document the measurement window and whether warmup, queue-draining or incomplete requests are included. |
Also record completed requests per second when request-level capacity matters. Keep that value separate from output-token throughput because different response lengths can produce the same request rate but very different token work.
How do you set latency targets before testing?
Choose user-facing objectives before looking at hardware results. Define an allowed TTFT, an ITL or TPOT objective for streaming, and an end-to-end completion objective. Attach a percentile to each target, such as the tail percentile your service-level objective enforces, rather than relying only on an average.
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The target should reflect the product’s interaction pattern. A low-latency assistant may prioritize first-token and streaming behavior; an offline pipeline may tolerate a longer wait if aggregate throughput is high. Google Cloud’s accelerator benchmarking guidance presents example latency thresholds as guidance, not universal standards, so do not copy a sample threshold without matching it to your application.
Write down what happens when a candidate misses a target. A practical rule is to reject that operating point even if its maximum throughput is attractive; capacity that violates the user contract is not usable capacity.
What is the repeatable benchmark procedure?
Run the following sequence for every hardware candidate. Keep the client, request data, intervals and measurement window constant so that only the candidate system changes.
- Freeze the workload. Load the specified model and precision, then prepare the same prompt and output-length distributions, request mix and streaming mode for every run.
- Record the environment. Capture hardware configuration, software versions, serving settings, model revision and benchmark-tool version before generating results.
- Define the measurement policy. Set the warmup procedure, request interval, concurrency schedule, start and stop events, token-count rules and percentile calculations. Do not change them between candidates.
- Run a low-load baseline. Start with a concurrency level low enough to expose single-user responsiveness. Measure TTFT, ITL or TPOT, end-to-end latency and output-token throughput after the defined warmup.
- Increase concurrency gradually. Add concurrent requests in planned steps. At each step, capture per-user latency distributions and aggregate output-token throughput, plus completed requests per second if relevant.
- Find the operating boundary. Continue until throughput saturates or the chosen latency target is breached. Record the last point that satisfies the target and the first point that does not; both are useful for capacity planning.
- Repeat and verify. Repeat runs with the same request order or randomized order policy, warmup and timing window. Investigate large variation instead of selecting the most favorable run.
Do not mix cold-start loading time into a steady-state serving comparison unless startup time is itself a requirement. If startup matters, report it as a separate metric with its own procedure.
How should concurrency results be read?
Plot or tabulate each measured concurrency rather than reporting only the maximum. A useful record has one row per concurrency level and columns for TTFT percentiles, ITL or TPOT, end-to-end latency, aggregate output tokens per second and completed requests per second.
| Concurrency point | Responsiveness | Streaming pace | Completion | Capacity | Decision |
|---|---|---|---|---|---|
| Measured level | TTFT percentile(s) | ITL/TPOT percentile or aggregation | End-to-end percentile(s) | Output tokens/s and optional requests/s | Pass, investigate or reject against the pre-set target |
Look for the point where added concurrency no longer produces meaningful aggregate throughput but continues to worsen per-user latency. That is a saturation signal. The best production setting is often below absolute saturation, where the latency objective still holds and some headroom remains for traffic variation.
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MLPerf Endpoints describes performance as a frontier across concurrency: system throughput is considered together with per-user speed and first-token latency. Its published frontier is a step function because it contains measured operating points; drawing a smooth interpolated curve can imply performance that was never tested. Present your own measured points in the same spirit and label any interpolation as an estimate.
How do metric definitions differ between benchmark tools?
Identical labels do not guarantee identical calculations. NVIDIA’s NIM Benchmarking Documentation warns: “Tool implementations vary, so compare results only when definitions align.” Treat the formula and timing window as part of every result.
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ITL and TPOT formulas
The 2025 NVIDIA article “LLM Inference Benchmarking: Fundamental Concepts” notes that GenAI-Perf and LLMPerf do not calculate ITL identically. GenAI-Perf excludes TTFT, while LLMPerf includes it. Therefore, a value called ITL or TPOT from one tool cannot be ranked directly against a value from another until you reconcile the formulas.
Throughput timing windows
Tools can start and stop throughput timing at different events. One may exclude warmup or wait for all requests to finish; another may count a fixed interval while requests are still active. Report the exact start and end events, whether queue-draining is counted, and how incomplete responses are handled.
Token accounting
State whether throughput counts generated output tokens only, how the first token is treated, and whether canceled or failed requests are excluded. For ITL, state the denominator explicitly; NVIDIA AIPerf’s documented calculation uses remaining time after TTFT divided by output-token count minus one.
How can you compare cost without hiding latency failures?
Apply cost normalization only after filtering for the latency objective. Google Cloud recommends measures such as throughput per accelerator (TPS/chip) and throughput per dollar (TPS/chip/$). Use the same workload and the same measured operating point for every candidate.
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| Economic view | Calculation concept | Required qualification |
|---|---|---|
| Throughput per accelerator | Measured output tokens per second divided by accelerator count. | Keep model, precision, concurrency and timing window identical; state whether the value is steady-state or another condition. |
| Throughput per dollar | Measured output tokens per second divided by the stated cost basis. | Identify whether cost is purchase price, rental price or another basis, and the time period used. |
| Capacity at target latency | Throughput at the highest measured concurrency that still meets every latency objective. | Do not substitute the system’s unconstrained peak if it breaches TTFT, ITL/TPOT or completion targets. |
There is no universal cost model for electricity, cooling, utilization, support or service operations in the cited guidance. State which costs are included and which are omitted. If comparing owned and rented systems, keep the accounting basis separate rather than presenting a false single price.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What details make a benchmark reproducible?
Publish a run sheet with enough information for another operator to recreate the test:
- Model revision, tokenizer, precision or quantization and context limit.
- Prompt and output-length distributions, request mix, streaming mode and arrival or concurrency policy.
- Accelerator and host configuration, interconnect, driver, runtime, serving server and relevant kernel versions.
- Benchmark client and version, including the exact command or configuration used.
- Warmup count or duration, request interval, concurrency sequence and measurement-window start and stop events.
- Definitions and formulas for TTFT, ITL/TPOT, end-to-end latency, token counts and throughput.
- Latency percentiles, aggregate throughput, completed requests per second where useful, failures and timeouts.
- Cost assumptions and the operating point selected for the comparison.
Keep raw per-request measurements as well as summary statistics. Summaries help decisions; raw records reveal whether a high percentile is caused by queueing, unusually long prompts, network delay or failed requests.
What common benchmark mistakes invalidate a hardware choice?
Comparing unlike workloads
Different prompt lengths, response lengths, precision or batching settings can overwhelm the hardware difference you intended to measure. Freeze those variables and show their distributions.
Using only peak throughput
Peak output tokens per second may occur at a concurrency that makes interactive latency unacceptable. Always pair throughput with TTFT, streaming pace and end-to-end latency at the same concurrency.
Mixing tool outputs by label
GenAI-Perf, LLMPerf, NVIDIA AIPerf and other clients can use different ITL formulas, token counts and timing windows. Re-run with one client where possible, or translate and document definitions before comparing.
Changing warmup or timing windows
A warm cache, a loaded model and a queue-draining interval can produce different results from a cold or partially loaded run. Use one policy for all candidates and report it.
Ignoring tail latency
An average can conceal a small group of requests that violate the user contract. Set the percentile target in advance and retain the full latency distribution.
Accepting unmatched vendor data
Published results are useful only when model, workload, software, hardware configuration and measurement definitions match your use case. Otherwise, treat them as context, not as a decision number.
How do you turn the results into a hardware decision?
- Remove every measured operating point that misses any pre-set latency objective.
- Among the remaining points, identify the highest sustainable concurrency and its aggregate output-token throughput.
- Check per-user generation speed and TTFT at that point; a high aggregate number can still feel slow to each user.
- Normalize throughput per accelerator and per dollar using the declared cost basis.
- Check reproducibility by rerunning the selected candidate and confirming that the result is stable under the same procedure.
- Choose the candidate whose measured operating point best fits the workload, latency contract and economic assumptions—not the one with the largest unqualified headline number.
This process produces a defensible choice even when hardware has different strengths. It also leaves a baseline for retesting after a model update, precision change or serving-stack upgrade.
Frequently Asked Questions
Should I benchmark with production traffic or a synthetic workload?
Use a controlled workload that preserves the important properties of production traffic: model, prompt and output-length distributions, request mix and streaming behavior. A synthetic generator is useful when those properties are documented and held constant, while a production trace can be used only with appropriate privacy and repeatability controls.
How many concurrency levels should a benchmark include?
Include a low-load point, several planned increases and points around the suspected saturation boundary. The exact count depends on the system, but stopping before throughput flattens or latency breaches its target leaves the operating limit unknown.
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Do longer prompts affect first-token latency even on the same hardware?
Yes. Prompt processing contributes to the time before the first output token, so longer prompts can raise TTFT. Keep prompt-length distributions identical across candidates or stratify results by length.
Can a system with lower tokens per second still be the better choice?
Yes, if it is the only candidate that meets the required TTFT, streaming pace and completion-time percentiles at the needed concurrency. Capacity outside the application’s latency envelope is not useful capacity.
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