Compare accelerators on the same workload, at the same quality and service target, and divide useful work by power measured at a clearly stated boundary. A throughput-per-watt figure for a GPU is not interchangeable with a whole-system wall-power figure, and neither is established by a product’s TDP. There is no universally most efficient GPU: the result depends on the job, configuration, software and measurement method.
Start by defining the work you need done
Performance per watt is meaningful only after defining both the work and the power measurement. For inference, useful work might be completed requests or output tokens. For training, a more useful comparison may be the time and energy required to reach the same target quality. The workload should reflect the model and operating conditions you expect to use.
- Model and task: Identify the model and whether the job is inference or training.
- Service conditions: For inference, specify input and output lengths, batch size or concurrency, and any latency or interactivity requirement.
- Quality target: Keep accuracy or other required output quality equivalent.
- Configuration: Record accelerator count, host system, memory, interconnect, cooling, precision and software or optimization settings.
A faster result does not represent more useful work if it serves a different model, misses the latency target, or delivers lower accuracy than the job requires. MLCommons discusses accuracy alongside performance in power-efficiency evaluation and has documented historical accuracy-efficiency trade-offs in earlier benchmark versions. Its March 2025 report described up to 50% lower energy efficiency when inference accuracy increased from 99% to 99.9% in earlier benchmark versions. That is a historical benchmark observation, not a forecast for current accelerators or a universal penalty.
Choose a metric that answers your question
For a throughput comparison, a simple ratio is useful throughput divided by average power. State the numerator and its units: for example, requests per second per watt or output tokens per second per watt. NVIDIA AIPerf defines metrics including request throughput per average GPU watt and output tokens per second per average GPU watt. These are GPU-level measures, not automatically whole-system efficiency. See NVIDIA’s AIPerf metric definitions.
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For a fixed job, total energy can be more informative than average power. Energy per completed task, or work per joule, captures both how much power a system draws and how long it takes to finish. Watts measure the rate of energy use; joules measure energy consumed over time. Performance per watt, energy per task and cost per token are therefore distinct measures, not interchangeable labels.
- Use throughput per watt when comparing sustained output under the same workload and service conditions.
- Include latency or time-to-train when responsiveness or reaching a target quality determines whether the output is useful.
- Use energy per task when the job has a defined completion point and total consumption matters.
Match the power boundary to the performance figure
Accelerator telemetry and wall power answer different questions. GPU telemetry can support an accelerator-level comparison. Measured wall power captures the complete system, including the CPU, memory, interconnect, storage, cooling and power-conversion losses. Do not divide whole-system throughput by GPU-only power, or GPU throughput by whole-system power, without clearly labeling the scope.
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For its Inference Edge power values, MLCommons specifies average AC power for the whole system, measured at the wall during the benchmark; those figures apply to that benchmark. Its power framework also highlights the importance of system interactions and shared resources. MLCommons explains its Power benchmark framework.
TDP and a power-supply rating are not measured workload power. Peak theoretical FLOPS divided by a TDP figure can describe a theoretical or rated comparison, but it does not establish application performance per measured watt. If you need a system-level reading for a compatible desktop PC, a plug-in electricity monitor may measure total draw at the wall; it will not isolate GPU power. Use equipment rated for the circuit and measurement need. MLCommons supports wall measurement as a system-level approach, but does not endorse a particular consumer meter or establish compatibility with server circuits.
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Use benchmark results without overreading rankings
Benchmarks can make comparisons more reproducible, but a result applies to its tested task, configuration and conditions—not automatically to every AI workload. MLPerf Inference v6.1 was announced on September 16, 2026. MLCommons describes it as an architecture-neutral, representative and reproducible way to measure system performance. Check the relevant task and entry details in the current result tables rather than relying on a vendor summary graphic. Read the v6.1 announcement.
When examining a benchmark entry, record the details that determine whether it is comparable to another result:
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- Benchmark version and task
- Division and submission status
- Submitter, hardware and accelerator count
- Software stack and configuration
- Whether the system is available or listed as preview
MLCommons’s Closed division aims to support same-model comparisons, while its Open division allows more flexibility. Published results may be modified, and averaging repeated runs does not remove all variance. Read the entry metadata and status before treating a ranking as a decision rule. Consult the MLPerf Inference Datacenter results.
A practical comparison checklist
- Write down the job. Name the model, task, input and output sizes, batch or concurrency, and required quality.
- Set the service target. Choose the throughput, latency, interactivity or time-to-train outcome that makes the result useful.
- Pick the ratio and boundary. Specify the numerator and whether power is accelerator telemetry or measured whole-system wall power. For a fixed task, consider total energy as well.
- Match configurations. Compare equivalent precision, accelerator count, host, memory, interconnect, cooling and software conditions, or disclose differences that prevent a like-for-like reading.
- Verify the evidence. Check benchmark version, division, submission status, entry metadata and system availability; do not substitute a vendor graphic for the detailed result.
- Compare the result against your workload. Treat a benchmark ratio as evidence for its tested scenario, not proof that the same accelerator wins elsewhere.
What published figures can—and cannot—tell you
In its March 2025 report, MLCommons said the MLPerf Power benchmark had 1,841 submissions to date. That total is anchored to the report date and is not a current cumulative count. The same report quoted Arun Tejusve (Tejus) Raghunath Rajan, Meta representative and MLCommons Power working-group co-chair: “We cannot improve what we do not measure.” The report provides the date and context for both figures.
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Neither that submission count nor the historical accuracy-efficiency observation identifies a universal winner. An efficiency result is useful when its model, quality, service target, configuration and power boundary resemble your own job. If those conditions differ, compare the systems under your workload rather than carrying a benchmark ranking over as a general claim.
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