The only useful AI-inference hardware benchmark is one that reproduces the service you need to run. A peak token-per-second figure from a different model, prompt length, concurrency level, software stack, or quality setting cannot tell you whether a system will meet your latency target. Build a repeatable test around your exact model and request pattern, measure first-token and tail latency as well as throughput, and publish the conditions and formulas so another engineer can reproduce the result.
Start with the decision the benchmark must support
Write the decision down before selecting a benchmark tool. Different decisions require different load shapes and success criteria.
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Interactive response quality
For a chat, coding assistant, or agent, the user cares about how quickly output begins and how smoothly it arrives. Set a service-level objective (SLO) for time to first token (TTFT), a token-generation latency target, and an end-to-end response limit. Measure those at the concurrency or request rate you expect in production.
Maximum batch throughput
Offline summarization, embedding preparation, and other queued jobs may prioritize output tokens per second or completed requests per hour. A high-concurrency run can be appropriate, but it says little about interactive responsiveness.
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Capacity at a latency target
Many deployments need to answer a practical sizing question: how many requests can the system serve while a stated latency percentile remains below the SLO? This requires a load sweep, not a single maximum-throughput run.
Cost or energy efficiency
If the decision includes cost or energy, define useful work first: for example, output tokens delivered while meeting a latency and quality target. Collect power, price, or energy measurements during the same workload and measurement window. Do not infer efficiency from a speed number alone.
Describe the workload precisely
A model name by itself is not a workload. Prompt processing and token generation stress a serving system differently, so fixed, short test strings can hide differences that appear with long contexts or long answers. NVIDIA’s LLM inference benchmarking concepts article explains that TTFT includes prompt processing before the first token, while token-to-token measures describe generation after that first token.
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- Record the exact model identifier, revision or checkpoint, tokenizer, and any model-specific configuration.
- Record numerical precision and quantization, including calibration or lower-precision settings that can affect output quality.
- Define the minimum acceptable quality: an evaluation score, human-review threshold, task success rate, or other criterion relevant to the application. A faster configuration that falls below this floor is not an equivalent candidate.
- Record sampling parameters, stop conditions, maximum context, and maximum output settings. Deterministic decoding and sampled decoding can produce different output lengths and workloads.
Input and output distributions
Use distributions rather than one convenient pair of lengths. Record token counts for prompts and generated answers, including median and tail values when available. Include short and long contexts if both occur in production. Record how often requests stop early and how often they reach the configured maximum.
Arrival pattern and concurrency
Specify whether requests arrive at a fixed rate, in bursts, or as an open-loop stream, and state the request rate or concurrency used at every test point. Concurrency is the number of in-flight requests; request rate is the offered arrival rate. They are not interchangeable, especially when latency changes under saturation.
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Production traces and synthetic data
A permitted production trace is usually the best representation of real work. If privacy, security, or repeatability rules prevent that, create a synthetic dataset and document how its prompt lengths, output lengths, stop behavior, and topic mix represent the intended service. NVIDIA’s TensorRT-LLM material describes preparing a dataset, building an engine when applicable, and running throughput or low-latency tests with synthetic or custom data; see TensorRT-LLM Benchmarking and Performance Tuning with TensorRT-LLM.
| Workload field | What to record | Why it changes the result |
|---|---|---|
| Model | Exact revision, tokenizer, architecture and context limit | Implementations and memory use vary between revisions and architectures. |
| Precision | FP32, FP16, BF16, INT8, or another stated format; quantization method | Precision changes memory traffic, supported kernels and output quality. |
| Prompt lengths | Distribution, not only an average | Longer prompts increase prefill work and TTFT. |
| Output lengths | Distribution, stop behavior and maximum | Generation time and output-token throughput depend on tokens actually produced. |
| Load | Arrival process, requests per second and concurrency points | Queueing and batching change as offered load rises. |
| Quality floor | Task metric or acceptance rule | A speed gain is irrelevant if responses no longer meet the application requirement. |
Measure the user experience and system capacity
Report several metrics together. A single token-per-second value can describe either one user’s generation speed or aggregate system output; those are different quantities.
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| Metric | Definition to state in your report | Use |
|---|---|---|
| Time to first token (TTFT) | Elapsed time from request submission until the first output token is available; state whether queueing and prompt processing are included. | Perceived startup responsiveness. |
| Inter-token latency (ITL) or time per output token (TPOT) | Delay between generated tokens, or the average time per output token after the first; identify the convention used by the tool. | Smoothness of streaming output. |
| End-to-end latency | Elapsed time from submission until the final response token. | Total wait for a completed answer. |
| Output-token throughput | Generated output tokens per second across the measured requests, with the counting window defined. | Useful work delivered by the system. |
| Request throughput | Completed requests per second under the stated load and completion definition. | Capacity when requests, rather than tokens, are the unit of work. |
| Total-token throughput | Prompt-plus-output or other total-token quantity exactly as defined by the selected tool. | Comparisons where both prefill and generation work matter. |
Use median and tail percentiles, such as p95 or p99, when the tool reports them. A median can look acceptable while a queueing or batching policy creates unacceptable tail latency. Always distinguish per-request latency from system-wide throughput. NVIDIA documents these metric families and their formulas in LLM Inference Benchmarking: Fundamental Concepts and the TensorRT-LLM serving benchmark guide at Run benchmarking with trtllm-serve.
Declare the measurement boundaries
State whether timing begins at client submission or after a request has entered the server, and whether it includes queueing, network transfer, request generation, initialization, warm-up, and cooldown. Throughput windows differ between tools: NVIDIA describes different boundaries in GenAI-Perf and LLMPerf, so identically named results are not automatically comparable. Include the tool name and version, formula, start and end events, and all exclusions.
Hold hardware and software conditions steady
For every candidate, record the complete environment. Keep conditions identical unless the comparison is explicitly about a tuning choice.
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- GPU model, count, interconnect or topology, and relevant memory configuration.
- CPU, system memory, storage and network configuration when they can affect request handling.
- Driver, runtime, inference framework, serving server and benchmark-client versions.
- Model precision, quantization, batch or scheduling settings, context limits and other serving parameters.
- Power limits, clock policy, persistence behavior and thermal state.
- Whether other workloads, monitoring agents or background services were active.
These settings are not universal vendor advice. For NVIDIA GPUs, TensorRT-LLM documentation recommends consistent configuration when reproducibility is critical, including persistence mode, a consistent power limit and an appropriate dynamic-clock policy. Its wording is direct: “For rigorous benchmarking where consistent and reproducible results are critical, proper GPU configuration is essential.” See TensorRT-LLM Benchmarking. Apply vendor-specific guidance only to the hardware it covers; do not assume the same controls or recommendations apply to another GPU vendor.
Run a repeatable benchmark
- Freeze the specification. Save the model revision, tokenizer, precision, quality test, input and output distributions, sampling and stop settings, arrival process, concurrency or request-rate points, and target SLO.
- Prepare the dataset. Use a production trace where permitted, or label a synthetic dataset and document what it represents. Keep the same request set and random seed policy for every candidate.
- Deploy the same serving behavior. Use the same scheduling, batching, context limits, API path and software versions wherever the comparison allows. If a candidate requires a different implementation, record that difference rather than hiding it.
- Verify quality first. Run the application-quality check at the selected precision and decoding settings. Exclude configurations that fail the stated floor, even if their raw speed is higher.
- Warm the system. Load the model and execute warm-up requests. Exclude initialization and cache-filling effects from steady-state figures unless startup time is part of the decision.
- Sweep the operating region. Test low load, the expected production point, higher offered load and the onset of saturation. Vary concurrency or request rate deliberately, and log rejected, timed-out and queued requests.
- Measure steady state. Run enough requests for stable observations, keep the measurement window explicit, and repeat each point. Record medians and tail percentiles for latency plus aggregate throughput.
- Check for thermal or clock drift. Note temperatures, power and clocks when available. If later repetitions differ materially, investigate throttling or background activity rather than averaging blindly.
- Archive raw evidence. Keep request metadata, logs, version information, configuration files and result data. A published summary without these details cannot be independently checked.
The TensorRT-LLM serving guide describes warming, ramping to steady state and sweeping concurrency before profiling; consult the current instructions at Run benchmarking with trtllm-serve. The maintainers mark the benchmark suite as a work in progress, so commands and APIs can change; verify the current documentation before automating a run.
Choose load points that expose the trade-off
Plot latency and throughput against offered load instead of reporting only the best point. At low load, each request may receive abundant resources and show good per-user latency. As concurrency rises, batching can improve aggregate output while queueing and contention increase TTFT, ITL or end-to-end latency. The maximum-throughput point is an upper bound under that test’s conditions, not evidence that the system satisfies an interactive target.
| Load region | Questions to answer |
|---|---|
| Light load | What latency does an isolated or lightly contended request see? |
| Expected load | Does the system meet the production SLO at the forecast request rate or concurrency? |
| Capacity boundary | At what load does a latency percentile cross the SLO, and how many requests or output tokens are delivered just before that point? |
| Saturation | How do queue depth, failures, tail latency and throughput behave after the target is exceeded? |
Report the selected operating point explicitly: for example, “the highest offered rate whose p95 end-to-end latency remains within the SLO.” That is more actionable for sizing than the highest token rate observed at an unconstrained load.
Understand benchmark-tool and service boundaries
Engine or model microbenchmarks
A model-only test isolates execution of the model and can be valuable for diagnosing kernels, precision or batching. It does not represent the complete API path. Pre-processing, post-processing, network transfer, queueing, authentication and client behavior can add user-visible delay.
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Server-level benchmarks
A serving benchmark exercises scheduling and request handling, but inspect exactly what the client measures. NVIDIA’s Triton TensorRT-LLM backend documentation notes that its core-model latency can exclude pre- and post-processing performed by other libraries; see the TensorRT-LLM backend README. If your production path performs those operations, add them to an end-to-end test.
Standardized references
MLPerf Inference provides standardized scenarios, metrics, quality targets and an auditable submission process intended to make results comparable across systems. Its methodology is described in the MLPerf Inference Benchmark paper. Use a submission as a reference for a defined scenario, then inspect its exact hardware, software, model, rules and quality result. A standardized score cannot answer a workload-specific question when your model, sequence lengths or service target differ.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Compare two systems without misleading yourself
Run both candidates through the same workload matrix and compare at the same quality floor and service target. The following axes prevent a fast but irrelevant result from winning by default.
| Axis | Evidence to compare | Decision question |
|---|---|---|
| Latency profile | TTFT, ITL/TPOT, end-to-end median and tail percentiles at each load point | Which system keeps the user experience within the SLO? |
| Capacity | Output tokens per second and requests per second at the compliant operating point | How much useful work can it deliver before violating the target? |
| Workload fit | Model support, context and output lengths, precision options and quality result | Can it run the required service without changing behavior? |
| System and software | GPU count and topology, framework and serving versions, batching and scheduling settings | Is the advantage dependent on a configuration you cannot deploy? |
| Deployment constraints | Memory fit, power and cooling requirements, availability and directly measured cost | Can the system be operated in the intended environment? |
| Reproducibility | Dataset description, load, warm-up, duration, formulas, logs and exclusions | Can another team verify the result? |
Publish a results record that others can reproduce
A concise table can accompany the full configuration and raw logs. Do not present vendor or standardized figures as measurements you performed.
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| Report field | Required detail |
|---|---|
| System under test | Hardware model, GPU count and topology, host configuration and power or clock policy |
| Software | Driver, runtime, framework, serving stack, benchmark tool and versions |
| Model | Exact revision, tokenizer, precision or quantization and quality result |
| Dataset | Production trace or synthetic description, prompt and output distributions, stop behavior |
| Load | Arrival pattern, request rate or concurrency, ramp method and tested points |
| Timing policy | Warm-up count or duration, steady-state window, repetitions, startup and cooldown treatment |
| Definitions | TTFT, ITL/TPOT, end-to-end and throughput formulas, timing boundaries and token-counting rules |
| Results | Median and percentile latency, output-token and request throughput, failures and rejected requests |
| Artifacts | Configuration, raw logs, result files and any exclusions or deviations |
Include the date and region when availability, cloud pricing or environmental conditions affect the decision. If a value is not measured, label it as not measured rather than filling the cell with an estimate.
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Common mistakes and how to correct them
Using one peak token-rate number
Symptom: A chart shows a large output-token figure but no load or latency. Correction: Report the load sweep and identify the highest point that meets the required latency percentile.
Changing more than the hardware
Symptom: One candidate uses a different precision, model revision, prompt set or batching policy. Correction: Hold those factors constant, or label the run as a configuration comparison rather than a hardware comparison and include the quality result.
Confusing TTFT with generation speed
Symptom: A low inter-token time is presented as proof that answers start quickly. Correction: Publish TTFT separately; it includes prompt processing and queueing effects according to the selected definition.
Comparing incompatible windows
Symptom: Two tools report “throughput” with different startup, request-generation or cooldown boundaries. Correction: State each formula and boundary, then rerun with one tool or normalize only when the underlying events are available.
Measuring a warmed microbenchmark as the whole service
Symptom: The model kernel looks fast but users see slower API responses. Correction: Add network, queueing, pre-processing and post-processing to an end-to-end path, while retaining the microbenchmark for diagnosis.
Ignoring quality regression
Symptom: Quantization or aggressive serving settings improve speed but alter task results. Correction: Make the quality floor a pass/fail gate before ranking performance.
Stopping at one concurrency value
Symptom: A result looks excellent at an arbitrary load but fails during demand spikes. Correction: Sweep the expected operating region and record failure, queue and tail-latency behavior beyond it.
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Make the final hardware decision
Select the system that meets the defined quality and latency target at the required capacity under reproducible conditions. If two systems both pass, use directly measured deployment constraints—memory fit, power and cooling, availability, and cost or energy per unit of useful work—as tie-breakers. Keep standardized results as context, but base the purchase or deployment decision on the workload and serving path you will actually operate.
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