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What are you trying to decide?
Start with the deployment decision, not an accelerator specification. Are you choosing capacity for an interactive assistant, comparing options for batch inference, or checking whether a retrieval-augmented generation (RAG) pipeline meets its service target? The answer determines which model, request pattern, latency objective and cost denominator matter.
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Write down the workload before selecting instances. Include:
- Model name, version, weights and tokenizer.
- Input and output length distributions, not just a single convenient example.
- Request arrival pattern, concurrency and any batching policy.
- Whether requests are single-shot, multi-turn, interactive, RAG-based or part of another multi-stage pipeline.
- Warm-up procedure and what counts as a measured request.
- Minimum acceptable quality, accuracy or task success, with the evaluation method.
- The latency objective and expected production operating point.
For a pipeline, measure both its end-to-end result and the individual stages if component-level diagnosis is useful. A fast model call does not establish that a retrieval-and-generation application is fast: retrieval, preprocessing, network transfer and post-processing can all affect the user-visible result.
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Which metrics should you compare?
There is no single score that answers every serving question. Pair each metric with the workload, request pattern, concurrency and quality threshold that produced it. Do not treat an offline throughput result as equivalent to an interactive latency result.
| Metric | What it tells you | What to report with it |
|---|---|---|
| Throughput | How much work the system completes in a stated period. | Units (such as requests or tokens), measured interval, concurrency or arrival pattern, and quality level. |
| Latency distribution | How long requests take, including how slow requests affect users. | Percentiles when available, the service-level threshold, request lengths and load pattern. Averages alone can hide tail latency. |
| Time to first token (TTFT) | For streaming language-model responses, time from request submission to the first generated token. | Input length, concurrency, streaming setup and the percentile or summary statistic. |
| Inter-token latency | For streaming output, the time between successive generated tokens after the first. | How it is calculated, output length and the same serving conditions used for TTFT. |
| Total response latency | Time until the response is complete. | Input and output lengths, concurrency, and whether the measure covers the full application path. |
| Cost per useful unit | Spend for completed work that meets the stated quality and latency requirements. | The useful-unit definition, observed throughput, price basis, region, billing assumptions and observation date. |
For LLM serving, useful units commonly include input tokens, output tokens or total tokens; report which one you use. Also state accelerator count. A per-accelerator result, a whole-instance result and a whole-system result answer different questions. Include energy only when it is measured with a stated boundary and method; MLPerf’s published power methodology measures at the wall for the full system, and other power descriptions are not equivalent validated measurements.
How do you make the comparison fair?
Hold the workload and quality floor constant
For a comparison intended to isolate platform differences, use the same model weights, tokenizer, precision or quantization, dataset, preprocessing, request generator, serving settings and quality threshold on each candidate. Record any deviation. A candidate that produces more tokens per second but falls below the required quality floor is not a faster equivalent deployment.
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Decide whether you are comparing accelerators or complete systems
Inference performance depends on the whole system, not just the chip. Record accelerator model and count, host CPU and memory, relevant interconnect and networking, storage, virtualization or partitioning, instance type and cloud region. Also record the OS image, driver, compiler, framework, inference runtime, libraries, container and application-level settings such as precision, batching and parallelism.
If a provider’s optimized stack requires a different implementation, it may still be a useful real-world comparison—but label it as a system-level comparison. Do not describe it as an isolated accelerator comparison. Keep the configuration consistent within each result set and disclose vendor-specific optimizations. Pin images and package versions where possible, and capture the test date and instance availability.
Separate official suite results from application results
MLPerf Inference provides standardized workloads and scenarios for comparisons where its workload matches the question. Its rules define the system under test broadly as hardware and software resources that influence runtime; a benchmark run includes pre- and post-processing as well as latency and quality requirements. The standard load generator uses a defined request pattern, and scenarios have their own metrics, datasets and quality targets.
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In MLPerf’s Datacenter division, Closed results keep the reference model for apples-to-apples comparison. Open results allow model changes or retraining and should be identified as Open, rather than presented as directly equivalent to Closed results. For any official result, record suite version, division, scenario, model, dataset, quality target and relevant result metadata. State plainly whether a result is an official MLPerf submission or an in-house measurement.
Workloads evolve. MLCommons announced MLPerf Inference v6.1 in September 2026; that release added end-to-end RAG and edge agentic tests and speculative-decoding support for specified interactive benchmarks. Its versioned workload roster spans areas including language, recommendation, vision, speech and vision-language models, but a workload’s inclusion in a suite does not make it a match for every application. Check the active release, rules, change log and result metadata when planning a comparison.
How do you run controlled trials?
- Freeze the test definition. Save the model and dataset identifiers, request mix, quality evaluation, load pattern, configuration manifest and software versions before testing candidates.
- Prepare each instance consistently. Record the cloud, region, instance and full system configuration. Confirm that the intended accelerator count and software stack are active.
- Warm up separately. Define which initialization and warm-up work is excluded from measurement, and apply the same policy to every candidate.
- Measure the production-like operating point. Run the expected request mix and concurrency using the same standard load generator or a documented client. Collect throughput, latency distributions, quality and any application-specific measures.
- Test capacity when needed. If the decision involves headroom or maximum serving capacity, increase offered load systematically and record where latency or quality stops meeting the target. Keep this saturation test distinct from the production-like operating point.
- Repeat runs and retain evidence. Repeat trials, report variation rather than only the best run, and preserve raw logs and manifests. MLPerf rules require reproducibility and prohibit benchmark detection and input-content-specific optimizations.
Use the same procedure across candidates. If a run cannot be reproduced, treat its result as unverified rather than selecting it as the winner. For measurements affected by noisy neighbors, instance variation or changing availability, retain the trial-level results and note the conditions instead of silently discarding inconvenient runs.
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How do you compare cloud GPU cost per token?
Calculate cost from the same operating point used for the performance result, and define what counts as a useful token. A transparent formula is:
Cost per million useful tokens = (instance price per hour + explicitly included ancillary cost per hour) ÷ measured useful tokens per hour × 1,000,000.
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For example, specify whether useful tokens are input, output or total tokens, and whether the measured output met the quality and latency requirements. Include the cloud region, instance type, accelerator count, utilization, billing term, discounts, any included ancillary costs and the date the price was observed. Prices and availability change; an undated figure or a price from a different region or billing arrangement cannot support a like-for-like ranking.
Keep cost, speed and efficiency distinct. The cheapest instance-hour may not have the lowest cost per useful token if its throughput is lower, while a high-throughput result may fail the latency or quality target. Historical benchmarks such as DAWNBench treated inference cost as well as latency as relevant dimensions. Vendor-published cost claims should be attributed to the publisher and checked against the underlying benchmark’s workload and configuration before being used to support a cross-vendor conclusion.
What should a reproducible report contain?
Publish a row or result card for every workload and candidate, with enough detail to interpret the numbers:
- Workload, model/version, dataset, input/output lengths, request pattern, concurrency and quality threshold.
- Benchmark suite and version, division, scenario and status as an official result or in-house test.
- Provider, region, instance type, accelerator model and count, host CPU and memory, interconnect, storage and virtualization or partitioning.
- OS, driver, framework, runtime, libraries, container, precision, batching, parallelism and other material application settings.
- Warm-up policy, measurement interval, repetitions, load-generator configuration and run-to-run variation.
- Throughput, latency distribution, quality result, cost calculation and, if measured, energy boundary and method.
- Price observation date, billing assumptions, instance availability and any deviations from the common configuration.
Compare candidates on the same scenario and quality floor. The useful axes are latency at a stated load, throughput at the same quality, cost per useful output at the same operating point, model and context capacity, accelerator count, software-stack maturity and reproducibility, plus region and availability. MLPerf results expose software, system and accelerator details alongside code or metadata; NVIDIA’s benchmarking guidance likewise advises evaluating infrastructure software, cloud platforms and application configurations in addition to GPUs.
Benchmark results can change after publication: the MLPerf Datacenter overview notes that results may be modified or invalidated. Treat suite version and result status as part of the result, not as optional footnotes. As one example of why version context matters, MLCommons reported that its best per-accelerator DeepSeek R1 server-scenario result in v6.1 was 5.7× better than the v5.1 result one year earlier. That is a specific MLCommons round-to-round comparison for one test—not a general speedup claim for cloud accelerators or other workloads.
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