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To compare cloud accelerators fairly, benchmark the serving system against a defined workload—not a chip’s peak specifications. Set a minimum quality target, keep the model and request conditions consistent, document the complete cloud and software configuration, and measure latency and throughput at the load your service must handle. Then report cost at that same operating point, repeat the runs, and publish enough detail for someone else to reproduce them.
Use a relevant MLPerf Inference workload as a standardized reference when one fits. Add a separate application benchmark for the real serving pipeline, especially when it includes retrieval, multiple turns, or application-specific processing. A standardized score helps with comparability; a representative test helps you decide whether a system fits your deployment.
What decision should the benchmark answer?
Start with the deployment choice you need to make. Name the model and version, the users or service it supports, the latency objective, and the request mix. A benchmark for offline batch processing answers a different question from one for an interactive assistant, even if both use the same model.
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Describe the workload before choosing a metric or machine. Record:
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- Model weights and tokenizer, including the model version or revision.
- Input and output length distributions, not just one convenient prompt length.
- Request arrival pattern, expected concurrency, and batch policy.
- Whether requests are single-shot, multi-turn, retrieval-augmented, or part of another multi-stage pipeline.
- Warm-up policy and the quality or accuracy threshold the service must meet.
- The latency objective that matters to users or downstream systems.
For a pipeline with retrieval, preprocessing, generation, and post-processing, measure the end-to-end request as well as individual stages. Component timings can help diagnose bottlenecks; only the end-to-end result shows whether the complete application meets its target. MLPerf Inference v6.1 includes an end-to-end retrieval-augmented generation (RAG) test covering corpus ingestion and query answering, and an edge agentic workload representing multi-turn requests.
Which metrics should I compare?
Report a metric together with the workload, request pattern, and operating conditions that produced it. A throughput number without concurrency or a latency number without a request mix is difficult to apply to another service.
| Metric | What to report | Why it matters |
|---|---|---|
| Throughput | Completed requests or useful tokens per unit of time, at stated concurrency and request conditions | Shows serving capacity at a defined load. |
| Latency | Distribution or percentiles, including tail latency where available, plus the service-level threshold | Shows how quickly requests are served and whether slower requests breach the target. |
| Quality | Evaluation method, measured result, and the acceptance threshold | A faster result is not useful if it falls below the required quality floor. |
| Cost efficiency | Cost per declared useful unit, calculated from measured throughput at the reported operating point | Connects cloud spend to the service delivered, rather than to peak capacity alone. |
| Energy, if measured | Measurement boundary, method, and whether the figure covers the accelerator or the full system | Prevents accelerator-only and whole-system energy figures from being mistaken for equivalent measures. |
For language-model serving, useful application-level measures include input tokens per second, output tokens per second, time to first token, inter-token latency, total response latency, concurrency, and cost per million tokens. Define each measure in the report. There is no single universal metric set established across cloud providers, so choose measures that match the application and do not imply that a custom metric is an official MLPerf result.
Do not treat offline throughput and interactive latency as interchangeable. MLPerf uses a standard load generator with scenario-specific request patterns and metrics; each benchmark also has a dataset and quality target. Match the scenario to the question you are asking.
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How should I use MLPerf Inference?
Choose the closest official workload and scenario, then record the suite version, division, scenario, model, dataset, quality target, and result metadata. MLPerf Inference v6.1, announced by MLCommons in September 2026, lists workloads spanning language, recommendation, vision, speech, vision-language models, RAG, and edge agentic tasks. The roster changes between versions, so identify the version rather than treating the suite as a permanent definition of AI inference.
Use Closed results for a controlled reference
In MLPerf’s Closed division, the reference model is maintained to support apples-to-apples comparisons. This is the more suitable choice when you want a standardized baseline and the official workload matches your question.
Label Open results as system-level alternatives
MLPerf’s Open division permits model changes or retraining. That can be useful for evaluating optimized implementations, but it is not the same comparison as a result using the unchanged reference model. Label the division and describe the changes.
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If no official workload represents your application, run a documented in-house test alongside—not in place of or blended into—the official result. Keep the two result types distinct. Preserve the application’s relevant stages and request mix so the test remains useful for the deployment decision.
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MLCommons reported that the best per-accelerator DeepSeek R1 server-scenario result in v6.1 was 5.7 times better than the v5.1 result one year earlier. That is MLCommons’ round-to-round comparison for that specific test, not a general speedup claim for other models, cloud instances, or workloads. The v6.1 announcement also reported submissions from 30 organizations; participation describes the round, not the performance of any one system.
What must stay constant—and what may differ?
For a comparison intended to isolate platform differences, use the same model weights and tokenizer, precision or quantization, dataset and preprocessing, quality threshold, software settings, and request generator across candidates. Hold the load pattern, warm-up, and measured interval consistent too.
Sometimes a provider’s optimized software stack requires a different implementation. You can compare the resulting systems, but describe the exercise as a system-level comparison rather than an accelerator-only test. Identify vendor-specific optimizations and rerun the benchmark after changing software or settings. Keep configurations consistent within each result set.
MLPerf defines the system under test broadly: hardware and software resources that influence runtime. Its run definition includes pre-processing and post-processing as well as latency and quality requirements. Its rules require reproducibility and prohibit benchmark detection and input-content-specific optimizations. An accelerator’s advertised peak figure does not capture these system effects.
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What cloud and software details should I record?
Report enough configuration to explain what ran and let another engineer recreate the setup. Pin images and package versions where possible, and record the date and instance availability because cloud offerings can change.
- Cloud placement: provider, region, instance type, and test date.
- Compute and topology: accelerator model and count, host CPU and memory, interconnect, relevant networking, storage, and any virtualization or partitioning.
- Base software: operating system or image, driver, compiler, framework, inference runtime, libraries, and container version or image identifier.
- Serving configuration: model and revision, precision or quantization, batching, parallelism, and other application or runtime settings.
- Benchmark details: workload, dataset and preprocessing, request generator and pattern, concurrency, warm-up, measured duration, quality target, and whether the result is official MLPerf or an in-house test.
- Evidence: configuration manifest, raw logs, and result metadata, including any tuning that could affect the outcome.
Compare the full infrastructure and application configuration, not just the GPU name. NVIDIA’s benchmarking guidance likewise advises evaluating infrastructure software, cloud platforms, and application configurations in addition to GPUs. Provider-published performance or cost claims should be attributed to the publisher and checked against the underlying benchmark’s configuration before being used for a cross-provider conclusion.
How do I run a controlled comparison?
- Fix the test plan. Write down the workload, quality floor, scenario, concurrency, request pattern, and measurements before testing candidates.
- Prepare equivalent systems. Apply the same model, data, client behavior, serving settings, and software policy wherever possible. Document any necessary system-specific changes.
- Warm up, then measure. Keep warm-up separate from measured samples. Use a standard load generator or document the client and its request behavior.
- Test the relevant operating point. Measure at the expected production concurrency. If capacity planning matters, also measure a saturation curve; label it separately from the production-like result.
- Repeat the runs. Run multiple repetitions and report variation, not only the best result. Preserve the raw logs and configuration manifest.
- Check the quality floor. Evaluate quality under the same acceptance rule for each candidate. Exclude or clearly flag results that do not meet the threshold rather than presenting them as equivalent service.
- Recheck after changes. A change to the model, runtime, driver, batching, or other relevant setting creates a different configuration; rerun and record it.
MLPerf states that results that cannot be replicated are not valid, and its rules prohibit benchmark detection and input-based optimization. Reproducibility is therefore part of the measurement, not just documentation added after the run.
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Use measured useful throughput at the same latency and quality operating point you are comparing. A practical calculation is:
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Cost per million useful tokens = (instance price per hour + explicitly included ancillary cost per hour) ÷ measured useful tokens per hour × 1,000,000.
State whether “useful tokens” means input tokens, output tokens, or both. Report the instance and region, measured throughput, concurrency, latency and quality point, billing basis, discounts, utilization assumption, any included ancillary charges, and the date the price was observed. Use equivalent assumptions for each candidate. If the billing basis or measured operating point differs, show the difference rather than presenting the figures as directly comparable.
Cost per token is not a standalone ranking: a low figure can reflect a different quality threshold, latency, token mix, utilization, or billing assumption. Historical benchmark work such as DAWNBench measured inference cost alongside latency. NVIDIA has published cost-per-token claims attributed to SemiAnalysis InferenceX as of April 2026; those are vendor-published claims, not a neutral current cloud price comparison. Cloud rates and availability vary by region and date, so gather the rates applicable to your own test rather than treating a dated claim as a current provider ranking.
How should I present the results?
For every workload and scenario, put the result and its conditions together. A useful report lets the reader distinguish raw speed from efficiency, accelerator-level results from whole-instance results, and standardized scores from application-specific measurements.
- Show throughput, latency distribution, and quality against the stated target for the same workload.
- Show cost per useful unit at that operating point, with its price date and billing assumptions.
- Identify accelerator count and whether any energy figure covers the accelerator or the complete system.
- Describe model or context capacity, software-stack maturity, and reproducibility where they affect the choice.
- Include cloud region, instance availability, and the full system configuration.
Before publishing or relying on an official result, check the active MLPerf release, rules, results, and repository version. MLPerf’s datacenter overview includes a change log and notes that results can be modified or invalidated. Record the applicable suite version and result metadata so a later change does not silently alter what your comparison means.
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