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Benchmark AI inference hardware with the model, token lengths, traffic pattern, quality target, and serving software you actually plan to run. A peak tokens-per-second number from a different prompt, batch size, or benchmark tool cannot tell you whether a system will meet your users’ latency target.
The reliable method is a controlled load sweep: define the decision and quality floor, build a representative dataset, hold hardware and software conditions constant, warm the service to steady state, measure first-token and generation latency plus throughput, and publish enough detail for someone else to reproduce the result.
What an inference benchmark must answer
Start with the operational decision, not with a favorite metric. Write one sentence that states what the test will decide:
- Interactive responsiveness: Can users receive a first token quickly and continue streaming at an acceptable pace?
- Capacity planning: How many requests per second can the system serve while meeting a latency service-level objective (SLO)?
- Maximum batch throughput: What is the highest useful output-token rate when latency is secondary?
- Deployment sizing: How many GPUs or servers are needed for a specified request pattern and quality floor?
- Cost or energy efficiency: How much useful work is delivered per dollar or watt under the same service target?
State the minimum acceptable output quality as part of the decision. A faster quantized model is not a valid winner if it fails the application’s accuracy, safety, or instruction-following requirement. Standardized inference methodology likewise treats scenario, quality target, and metric rules as part of a meaningful comparison, rather than as optional metadata (MLPerf Inference methodology).
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Describe the workload precisely
A model name alone is not a workload. Record the exact model revision and tokenizer, precision or quantization, context and output lengths, decoding settings, and traffic pattern. Prompt processing (prefill) and token generation have different performance characteristics, so a single short prompt can hide bottlenecks that appear with long documents or long answers.
Model and quality specification
- Model architecture and exact checkpoint or release.
- Tokenizer version and special-token behavior.
- Numerical precision (for example, FP16, BF16, or an explicitly named quantization scheme).
- Sampling parameters, stop sequences, maximum output length, and whether responses are streamed.
- Quality evaluation and the minimum score or pass criteria that every candidate must meet.
Token-length distributions
Measure input and output lengths as distributions, not just maxima. For example, retain the median and upper-tail prompt length, the median and upper-tail generated length, and the proportion of requests that hit a maximum. A test with 512 input tokens and 64 output tokens answers a different question from one with 16,000 input tokens and 1,000 output tokens.
Arrival pattern and load
Specify whether requests arrive at a fixed rate, in bursts, or as an open-loop trace. Also record concurrency (the number of in-flight requests) and the request-rate points you will test. An open-loop rate sweep and a closed-loop concurrency sweep can produce different queueing behavior; label which one you used.
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Use a permitted production trace when privacy and security rules allow it. Otherwise create a synthetic dataset that mirrors the real length, topic, and arrival distributions. Synthetic inputs are valuable for controlled comparisons, but label them as synthetic and explain what they represent. TensorRT-LLM’s benchmarking documentation covers preparing a dataset, building an engine when applicable, and running throughput or low-latency tests (TensorRT-LLM Benchmarking); its tuning guidance also discusses synthetic and custom datasets (TensorRT-LLM performance tuning).
Use metrics that map to user experience
Report a metric set rather than one speed number. The following definitions are common for streaming LLM services, but a tool’s exact boundaries can differ, so include the tool, version, and formula in your report.
| Metric | What it measures | Why it matters |
|---|---|---|
| Time to first token (TTFT) | Elapsed time from request submission until the first output token is available. It includes prompt processing and any queueing included by the tool. | Perceived start-up responsiveness for an interactive user. |
| Inter-token latency (ITL) or time per output token (TPOT) | Delay between generated tokens, or the average time per output token after the first token. State which convention is used. | How smoothly a response streams after generation begins. |
| End-to-end latency | Elapsed time from request submission to the final response token. | Total wait for a complete answer. |
| Output-token throughput | Generated output tokens divided by the defined measurement window, usually aggregated across requests. | Useful-work capacity for generation-heavy services. |
| Request throughput | Completed requests per second over the stated window. | Capacity planning when requests, rather than tokens, drive demand. |
NVIDIA’s metric guide defines TTFT, TPOT/ITL, end-to-end latency, total-token throughput, and output-token throughput, and explains why the definitions must be stated (LLM Inference Benchmarking: Fundamental Concepts). Report at least a median and a tail percentile when the tool provides them. A per-user token rate is not interchangeable with system-wide output-token throughput: the former describes an individual stream, while the latter aggregates useful work across requests.
Make the measurement window explicit
Write down whether initialization, model loading, warm-up requests, request generation, queueing, and cooldown are included. For a steady-state serving result, exclude initialization after documenting it separately. For a cold-start or autoscaling decision, run a separate test that intentionally includes startup. Two tools can report different throughput for the same run simply because one starts its clock before request generation and the other starts after the workload is ready.
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Hold hardware and software conditions steady
For every candidate, capture the complete configuration. Differences outside the accelerator can dominate the result.
- GPU or accelerator model, count, memory capacity, and interconnect or topology.
- Host CPU, system memory, storage, and network path when they can affect serving.
- Driver, firmware, runtime, serving framework, benchmark tool, and exact versions.
- Model precision, quantization, maximum context, batching, and scheduler settings.
- Power limit, clock policy, cooling state, and any thermal throttling observed.
- Tensor or pipeline parallel settings and which device owns each shard.
For NVIDIA systems, TensorRT-LLM documentation recommends deliberate GPU configuration when consistent and reproducible comparisons are important. Its guidance discusses persistence mode, allowing dynamic clock behavior instead of blindly locking maximum clocks, and applying a consistent power limit (TensorRT-LLM Benchmarking). These are NVIDIA-specific instructions, not universal settings for every accelerator vendor.
“For rigorous benchmarking where consistent and reproducible results are critical, proper GPU configuration is essential.”
The TensorRT-LLM benchmark suite is maintained as a work in progress and may contain breaking API changes. Check the current documentation before relying on a particular command or option (TensorRT-LLM Benchmarking).
A repeatable benchmark procedure
1. Define the pass/fail rule
Before starting, set the latency SLO, quality floor, minimum throughput or request capacity, and any power or cost boundary. Decide which percentile governs the SLO. For example, you might require a specified tail TTFT and end-to-end latency at a stated request rate, while also recording total output-token throughput. Do not optimize a metric that has no relationship to the deployment decision.
2. Freeze the test manifest
Create a manifest or spreadsheet containing the model revision, tokenizer, precision, dataset hash, input and output length distributions, sampling settings, arrival pattern, concurrency or rate points, software versions, hardware topology, power policy, warm-up count, measurement duration, and repetition count. Give every candidate the same manifest.
3. Prepare the dataset
Build the request set from a permitted trace or a documented synthetic generator. Preserve realistic length correlations—for example, long prompts may also request longer answers. Record whether prompts are repeated, because prefix caching can materially change results. If caching is part of production, test it deliberately and report cache-hit assumptions; if it is not, disable or randomize it consistently.
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4. Bring up the service and verify correctness
Load the model and run functional checks before measuring speed. Confirm that tokenization, stop conditions, streaming, and output quality match the intended application. A fast result from a truncated response or a failed request is not capacity.
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Send warm-up traffic until model kernels, memory allocation, compilation, and serving queues reach normal behavior. Keep warm-up requests out of the steady-state statistics unless startup is the subject of the test. The TensorRT-LLM serving guide describes warming and ramping to steady state before profiling and demonstrates sweeping concurrency values (run benchmarking with trtllm-serve).
6. Sweep concurrency or request rate
Run a sequence from light load through the operating region and toward saturation. At each point, allow the service to stabilize, then collect enough requests to observe queueing and tail behavior. A single maximum-throughput run cannot show where the latency SLO is violated.
7. Repeat each point
Repeat runs with the same configuration and report variability. Investigate outliers caused by thermal throttling, background jobs, network interruptions, or failed requests rather than silently deleting them. Record whether the reported value is a mean, median, or another summary.
8. Validate quality and failure behavior
Run the quality checks at every precision or quantization used in the comparison. Count timeouts, errors, retries, and truncated outputs. A candidate that reaches a token target only by dropping requests does not meet that target.
How to compare two systems fairly
Use a side-by-side matrix with identical workload points. Compare latency at the load your service will actually face, then report the throughput achieved while the latency and quality rules still pass.
| Comparison axis | Questions to answer |
|---|---|
| Latency profile | What are TTFT, ITL/TPOT, end-to-end latency, and tail percentiles at each load? |
| Capacity | How many output tokens and requests per second are delivered while meeting the SLO? |
| Workload fit | Does the system support the model, context lengths, output lengths, precision, and quality floor? |
| System and software | Are GPU topology, serving stack, versions, batching, and scheduler settings equivalent? |
| Deployment constraints | Does it fit memory, power, cooling, availability, and measured cost limits? |
| Reproducibility | Are dataset, load, warm-up, clock or power policy, measurement window, and raw logs disclosed? |
When cost or energy matters, measure it under the same useful-work definition as performance—for example, energy per output token while meeting the SLO. Do not infer cost efficiency from a higher token rate alone; a faster system can require more accelerators or higher power.
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Read the load curve instead of chasing the peak
Increasing concurrency often raises aggregate throughput until the service saturates, but it can also increase queueing and reduce each user’s streaming speed. The useful operating point is where the required latency and quality targets still pass, not necessarily the rightmost point on a throughput graph.
Plot load on the horizontal axis and show TTFT, end-to-end latency, and output-token throughput on separate charts or clearly separated scales. Mark the SLO boundary and identify the highest load that remains compliant. Treat maximum throughput as an upper bound under that benchmark’s conditions, not proof that the system is suitable for interactive use.
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Interpret the two generation phases
TTFT includes prompt processing and request-path effects before the first token. ITL or TPOT describes the interval during generation after that first token, according to the tool’s definition. A system can have excellent generation speed but poor TTFT on long contexts, or the reverse. Report both so a hardware choice does not optimize the wrong phase.
Check metric boundaries
Read the benchmark implementation rather than assuming two identically named metrics are calculated identically. NVIDIA documents differences between GenAI-Perf and LLMPerf throughput windows, including what request-generation and completion intervals include (LLM Inference Benchmarking: Fundamental Concepts). A model-only microbenchmark can also omit application work: Triton’s TensorRT-LLM backend documentation notes that its core-model latency excludes pre- and post-processing performed by other libraries (Triton TensorRT-LLM backend README). If networking, authentication, retrieval, serialization, or application code sits outside the timer, measure an additional end-to-end path.
Standardized benchmarks versus your deployment test
Standardized results are useful reference points because MLPerf defines scenarios, rules, quality targets, and an auditable submission process (MLPerf Inference paper). They help establish a common vocabulary and reveal how systems behave under a specified workload.
They do not replace a local test. A published result is conditional on its hardware, model, software version, scenario, and benchmark rules. Verify the exact submission and scenario before carrying a number into a purchase or capacity decision, then run the intended model and traffic in your own deployment environment. Keep standardized figures visually separate from measurements made on your system.
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What to publish so others can reproduce it
A concise results table should include:
- System identifier, accelerator count and topology, host configuration, and power or clock policy.
- Model revision, tokenizer, precision or quantization, context limit, and serving framework with versions.
- Dataset source or synthetic-generation method, dataset size or hash, and input/output length distributions.
- Arrival pattern, request-rate or concurrency sweep, warm-up policy, test duration, request count, and repetition count.
- Definitions and formulas for TTFT, ITL/TPOT, end-to-end latency, token throughput, and request throughput.
- Median and reported tail percentiles, plus error, timeout, retry, and truncation counts.
- Whether startup, request generation, queueing, pre/post-processing, networking, and cooldown are inside each timer.
- Raw logs or result files, with a note distinguishing vendor or standardized figures from local measurements.
Include the benchmark tool name and version. If a tool changes its API, preserve the exact configuration used for the published result and identify the documentation version or access date.
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Common benchmark mistakes and fixes
Using one short prompt and one output length
Problem: The result says little about long-context or long-generation traffic.
Fix: Use measured input and output distributions, and publish the bins or percentiles.
Reporting only peak tokens per second
Problem: The peak may occur at a concurrency that violates the latency SLO.
Fix: Show the full load sweep and the throughput achieved at the compliant operating point.
Mixing per-request and aggregate rates
Problem: A per-user streaming rate is compared directly with system-wide throughput.
Fix: Label the scope and denominator of every rate.
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Changing software or power settings between candidates
Problem: The comparison measures configuration differences rather than hardware.
Fix: Freeze versions, topology, batching, scheduler settings, and power policy; record thermal state.
Including warm-up in one result but not another
Problem: Initialization dominates a supposedly steady-state number.
Fix: Exclude and document warm-up for steady-state tests, or run a separate cold-start benchmark for startup decisions.
Trusting a tool’s label without checking its timer
Problem: “Latency” or “throughput” includes different phases across tools.
Fix: State the formula, measurement boundaries, tool version, and whether request generation and cooldown are included.
Ignoring correctness, errors, and quality
Problem: A high rate is achieved with invalid, truncated, or low-quality responses.
Fix: Apply the quality floor and count failures as part of the pass/fail decision.
A practical reporting template
Use this outline for each benchmark publication or internal decision record:
- Decision: state the deployment goal, latency SLO, quality floor, and capacity requirement.
- Workload: identify model and tokenizer, precision, token-length distributions, sampling, stop behavior, and arrival pattern.
- System: list accelerators, count, topology, host, drivers, framework versions, serving settings, power, clocks, and cooling conditions.
- Procedure: document correctness checks, warm-up, ramp, load points, duration, repetitions, and exclusions.
- Metrics: define TTFT, ITL/TPOT, end-to-end latency, output-token throughput, request throughput, and percentile calculations.
- Results: show the load curve, compliant operating point, variability, errors, and quality measurements.
- Reproduction: provide configuration files, dataset description or hash, tool version, raw logs, and links to the relevant documentation.
This format makes a benchmark useful beyond a single headline number: a reader can determine whether the tested scenario matches their own service and can repeat the measurement before committing to hardware.
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