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Benchmark GPU infrastructure against the work it must do: for training, measure wall-clock time to a specified quality target; for inference, measure throughput and latency under a defined request workload. Use MLPerf for a standardized reference, then run repeatable tests with your own model, software stack, and service requirements. A peak-throughput number without its quality target, request shape, latency constraint, and system details is not a fair basis for choosing GPUs.
Start with the decision the benchmark must support
Training and inference answer different performance questions, so define the decision before choosing a metric.
- Training: How long does the system take to reach the required model quality?
- Offline inference: How many inputs or output tokens can the system process in a fixed period?
- Interactive inference: Does it meet response-time targets for the expected mix of prompts and completions?
- Capacity planning: How does performance change as concurrency or request rate rises, and where does the system saturate?
- Cost efficiency: What performance does the deployment deliver at the utilization and operating cost your team can sustain?
Choose the model and required quality or accuracy first. A faster run that misses the target is not a faster way to deliver the same result.
Use MLPerf as a controlled reference point
MLPerf Training
MLCommons defines MLPerf Training as measuring how fast systems train models to a target quality metric. Each benchmark is tied to a dataset and quality target, and the reported measure is wall-clock time to that target—not simply step speed. The official benchmark page lists v6.0 for several current workloads, including language-model and image-generation examples; verify the current suite rules for the specific workload before comparing submissions.
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MLCommons says repeated measurements discard the highest and lowest runs and average the rest. It also cautions that averaging does not remove all variance: its benchmark page gives rough variability estimates of ±2.5% for imaging benchmarks and ±5% for other benchmarks. Those are suite-specific rough estimates, not universal confidence intervals or predictions for a locally designed test.
MLPerf Inference: Datacenter
This suite measures how quickly systems process inputs and produce results with a trained model. Its standard load generator applies defined scenarios, and each benchmark has its own prescribed metric, dataset, and quality target. Compare results only after checking the applicable rules and constraints, including the scenario and latency limit—not just the summary throughput.
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Interpret divisions and system readiness
In the Closed division, MLPerf uses the reference model for an apples-to-apples comparison. The Open division permits a different model or retraining, so treat those results separately rather than as directly interchangeable. Also check the system’s availability category: MLCommons describes Available systems as components available for purchase or cloud rental, while Preview and RDI systems have different readiness. Published result rows can be changed or invalidated after release, so review the result change log before quoting a ranking.
For LLM inference, report what each metric measures
Metric names alone are not enough: tools may calculate similarly named values differently. State the tool and calculation method alongside the result. NVIDIA’s GenAI-Perf guidance distinguishes the following measures:
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| Metric | What to report it as |
|---|---|
| Time to first token (TTFT) | Elapsed time until the first generated token. In the described measurement model, it includes queueing, prefill, and network effects; longer prompts can increase prefill work and TTFT. |
| End-to-end request latency | TTFT plus the time spent generating the rest of that request’s output. |
| Inter-token latency (ITL) | Average interval between generated tokens after the first. GenAI-Perf’s definition excludes the first token when calculating the decoding interval. |
| System output tokens per second | Aggregate output-token throughput across concurrent requests. GenAI-Perf and LLMPerf use different timing windows, so name the tool and calculation. |
| Tokens per user | Per-user output rate; useful for describing individual experience, but not a substitute for aggregate system throughput. |
| Requests per second | Completed-request throughput. Report it alongside request shape and latency, not as a stand-in for token throughput. |
Input and output lengths affect different parts of the service. Longer inputs increase prefill and KV-cache demand and can raise TTFT; longer outputs require more generation work and memory and can affect ITL. As concurrency increases, aggregate throughput may rise until compute saturates, then flatten or fall. Per-user throughput can decline as latency increases even while aggregate system throughput is still rising. Use representative distributions of prompt and completion lengths rather than a single arbitrary token count.
Performance benchmarking measures model-level behavior such as throughput and latency. Load testing checks behavior under concurrent, real-world traffic, including capacity, autoscaling, network latency, and resource utilization. Production readiness generally calls for both views.
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Run a reproducible workload test
- Specify the goal and pass criteria. Record whether the test is for time-to-quality, offline throughput, interactive latency, capacity, or cost efficiency. State the model, target quality or accuracy, and any latency or throughput service target.
- Fix the workload shape. Record the dataset or request set; input and output length distributions; batch size; precision; concurrency or request rate; cache state; and serving configuration. Sweep the relevant load levels so the result shows the throughput-latency curve and saturation point, not just one favorable operating point.
- Establish repeatable conditions. Use a stable baseline and, where possible, consistent clock and power behavior. Record temperature and throttling, GPU utilization and memory, host-device transfers, driver mode, synchronization behavior, and framework and runtime versions.
- Repeat runs and disclose variability. State the warm-up period, measurement window, number of repetitions, outlier handling, and summary statistic. Averaging does not eliminate training variance; if observed differences are within run-to-run noise, do not claim a precise ranking.
- Profile after measuring. First establish an unoptimized baseline. Then use an appropriate framework or device profiler to inspect layer behavior, transfers, memory, and bottlenecks. In TensorRT contexts, relevant options include
trtexec, CUDA events and wall-clock timing, built-in profiling, and NVIDIA Nsight Systems. Tool support and exact options depend on the installed versions. - Publish enough detail to repeat the run. Include GPU type and count, system and interconnect, network mode, model and tokenizer, dataset or request profile, precision, cache state, load pattern, target quality, software and container versions, and metric definitions.
Compare systems on the dimensions that affect deployment
| Comparison axis | Evidence to compare |
|---|---|
| Correctness and quality | Whether each system reaches the same quality or accuracy target under the stated benchmark rules. |
| Training time | Wall-clock time to target, including run spread and scale. |
| Inference service | Throughput and latency under the same scenario, request-length distribution, and load. |
| Scaling | Performance as GPU count grows and across multi-node layouts, with interconnect, network, and software stack identified. |
| Capacity | Model fit, memory use, batch and concurrency headroom, and cache behavior. |
| Reproducibility | Whether another team can reconstruct the model, environment, controls, and measurement window. |
| Availability and economics | Whether the system is currently purchasable or rentable, plus your own cost, utilization, and operational constraints. MLPerf availability categories help classify readiness; they are not a complete cost model. |
Keep standardized Closed-division results, Open-division results, vendor-published claims, and application-specific tests in distinct comparison groups. Each answers a different question. A standardized result helps control the comparison; a local workload test tells you how your chosen stack behaves; neither alone establishes the economics or operational fit of a deployment.
A compact benchmark report template
For each result, publish one record that lets another team understand the conditions and reproduce the comparison:
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- Purpose and target: training or inference objective, quality or accuracy target, and service thresholds.
- Workload: model and tokenizer, dataset or request profile, input/output lengths, batch size, concurrency or request rate, and cache state.
- System: accelerator model and count, host, interconnect, network, storage, and multi-node configuration if applicable.
- Software: framework, backend, precision, drivers, runtime, container, serving configuration, and relevant versions.
- Method: tool, metric definitions, load scenario, warm-up, measurement window, repetitions, outlier treatment, and summary statistic.
- Observed conditions: utilization, memory, transfers, clocks, power behavior, temperature, throttling, and synchronization conditions where relevant.
- Result: time to quality for training, or clearly defined throughput and latency for inference, with spread and constraints shown.
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