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Measure an AI model’s correctness and energy consumption as two separate outcomes, then relate them under the same task and workload. Accuracy is meaningful only when the input, expected output, evaluation data and metric are defined. Energy is meaningful only when the measured system boundary, hardware, software, workload and sampling method are disclosed.
For a defensible result, keep evaluation data separate from training and tuning, select a task-appropriate correctness metric, measure all hardware that contributes to the run, integrate power over time, and report energy per completed inference or token for serving workloads. The procedure below gives you a repeatable method for training and inference, plus a report format that lets other people reproduce or fairly compare the result.
Start with a measurement question, not a model score
Write the decision you are trying to make before collecting data. “Which model is better?” is too vague. A useful question identifies the task, the cost of failure and the operating workload, such as:
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- Which classifier detects a rare safety event with the fewest missed cases?
- How many watt-hours does one production request consume at a specified prompt and response length?
- What is the energy cost of completing a training run that reaches a defined validation target?
Correctness and energy answer different parts of that decision. A model can score well while using too much energy for a device, or use little energy while failing the cases that matter. Record both results and any latency or throughput requirement instead of collapsing them into one headline number.
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Define what “accuracy” means for the task
Choose a metric that matches the error cost
There is no universal accuracy metric. ISO/IEC DIS 4213 discusses classification, regression, clustering and recommendation, and states that metric selection depends on the use case and domain constraints. The metric optimized during training may not be the metric you should use for acceptance.
| Task | Possible correctness measures | Important qualification |
|---|---|---|
| Classification | Accuracy, precision, recall (sensitivity), specificity, F1, area under a chosen curve | Report class prevalence and decision threshold; a single overall percentage can hide poor performance on a minority class. |
| Regression | Mean absolute error, root-mean-square error, or another domain-approved loss | State the target units and whether large errors are penalized more heavily. |
| Clustering | A task-appropriate internal or external clustering measure | Explain how clusters are labelled or matched to reference groups. |
| Recommendation | Ranking or retrieval measures selected for the user action | Define the candidate set, relevance rule and cutoff used for the score. |
For a high-stakes classifier, report sensitivity and specificity alongside any aggregate score when false negatives and false positives have different consequences. For an open-ended generative system, do not present a generic “accuracy” number as if it were covered by the four task families in the draft ISO document. Define a task-specific protocol, such as a rubric with independently checked reference answers, and state what the protocol can and cannot establish.
Specify the expected output and failure costs
Document the model input, the expected output, acceptable tolerances and the consequence of an incorrect result. For a regression, an error of 2 units may be acceptable in one application and unsafe in another. For a classifier, the operating threshold changes the trade-off between sensitivity and specificity. For a recommendation system, define what counts as relevant and how many results are evaluated.
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Evaluate on data that was not used to train, select, prompt-tune, calibrate or otherwise tune the model. Information from outside the intended model input that enters development can produce an over-optimistic estimate. Leakage can be direct, such as duplicated records, or indirect, such as a preprocessing step fitted on the complete dataset.
Record the evaluation setup
- Dataset name, version or snapshot date.
- Number of examples and the class or target distribution.
- Preprocessing, normalization, tokenization and filtering.
- Decision thresholds, decoding settings and post-processing.
- Metric implementation and software version.
- Any exclusions, deduplication rules or missing-value handling.
Keep a locked test set for the final comparison. Use a separate validation set for model selection, and do not repeatedly inspect the test results while changing the system. If the data changes over time, identify the time window and explain whether the test represents current or future operation.
Quantify variation instead of over-reading one score
Run repeated evaluations or use an appropriate confidence interval when sampling variation could change the decision. ISO/IEC DIS 4213 includes discussion of statistical significance tests for classification, regression, clustering and recommendation comparisons. Use the same examples, preprocessing and metric for every model in a comparison. A one-point difference on one test set does not establish general superiority.
Define the energy measurement boundary
Energy is a property of a measured system performing a workload, not a permanent constant attached to a model. State exactly what is inside the boundary:
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|---|---|---|
| Accelerator or device | Power drawn by a processor or accelerator | Host CPU, memory, storage, cooling, power conversion and network equipment. |
| Host or node | Device, host processors, memory and local platform overhead | Other nodes, external storage, facility overhead and some interconnect power. |
| Multi-node system | All participating nodes and the interconnect carrying the workload | Anything outside the declared cluster or facility boundary. |
MLPerf Training defines the system under test as the hardware and software resources that affect performance or power. Its rules require every node participating in a performance run to be measured. Interconnects that carry weights, updates or activations must be measured or estimated under those rules. If you use a narrower boundary, label it clearly rather than presenting the result as model-only energy.
For inference, disclose whether the result excludes the end-user device, data transfer, storage, cooling or embodied hardware impacts. The ITU’s treatment of AI energy notes that these exclusions are common and can materially change interpretation. A device-only reading and a node-level reading are not interchangeable.
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Measure training energy step by step
1. Fix the training run and its stopping rule
Define the model version, dataset, batch size, optimizer, precision, number of steps or epochs and the target used to decide that training is complete. If you compare models, keep the task and target outcome equivalent. Record restarts and failed runs; do not silently omit energy consumed before a successful run.
2. Instrument every participating node
For a benchmark-style node measurement, MLPerf Training calls for power measurement at the power-supply unit, with documented conversion assumptions when necessary, or an acceptable per-node power-distribution-unit measurement. Include all nodes used during the timed portion. If part of the system is estimated, identify that component and the estimation formula.
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3. Verify the meter and sampling rate
MLPerf Training targets meter accuracy of 5 percent and describes qualifying meter specifications or comparison against a reference meter under different loads. Its rules call for at least one power reading per second during the timed portion and a minimum of 60 power samples for short runs. These are benchmark rules, not a universal legal requirement, but they provide a clear reproducible baseline. A consumer plug-in meter may be suitable for an individual device; it is not automatically suitable for a server node or a multi-node installation.
4. Start and stop around the timed run
Begin measurement just before the defined training interval and stop just after it. Use synchronized timestamps for all nodes. Exclude unrelated jobs, idle periods and warm-up work only if your protocol defines them as outside the timed run; otherwise include them and explain why.
5. Integrate power into energy
For each interval, multiply the measured power by the elapsed time represented by that reading, then sum all intervals and all participating systems:
Measured energy = Σ (power reading × time interval)
Convert watt-seconds to watt-hours by dividing by 3,600. If a component’s power is estimated under MLPerf’s rules, its formula uses maximum specified power, run duration and a provisioning ratio. Label estimated and directly measured portions separately, then provide the combined total with the assumptions.
Measure inference energy per useful unit
Energy per inference
For a serving workload, count completed inferences and divide the total runtime energy by that count:
Energy per inference (Wh) = Total runtime energy (Wh) ÷ inference count
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Define what one inference means. A batch of 16 requests may be one accelerator operation but 16 inferences for reporting. State whether failed, cancelled or retried requests are included.
Energy per token
For a tokenized model, ITU gives this relationship:
Energy per token (Wh) = (Power × inference duration) ÷ (number of tokens × 3,600)
Use the same token definition throughout: prompt tokens, generated tokens or both. Report prompt and output lengths, because generation settings change the denominator and the runtime. A long response can have a different energy profile from a short response even when the prompt is identical.
Describe the request workload
- Hardware model, number of devices and memory configuration.
- Model version, quantization or precision and serving software.
- Batch size, concurrency and request arrival pattern.
- Prompt length, output limit and decoding settings.
- Warm-up policy and whether compilation or cache-building energy is included.
- Run duration, inference count and treatment of failed requests.
Measure a workload that resembles deployment. A single isolated request can underuse a device; a saturated batch can improve throughput while increasing latency. Report both energy and the operating conditions rather than comparing a low-concurrency result with a high-concurrency result as if they were equivalent.
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Separate measurement from estimation
Real-world inference energy studies are uncommon. ITU notes that many published estimates assume a runtime, generic hardware and standard carbon-intensity factors. Mark those values as estimates and list every assumption. A measured wall-power value is not interchangeable with an estimate derived from a chip’s maximum rated power.
For an estimate, identify:
- Which power values are measured and which are assumed.
- The utilization, runtime and request volume used.
- The hardware generation and number of devices.
- Whether host, network, cooling or storage overhead is excluded.
- The grid or carbon-intensity factor, if environmental impact is calculated.
Do not turn a benchmark rule or a maximum specification into a claim about typical model energy. The MLPerf statement that a score represents the energy consumption to accomplish a given task applies to its defined benchmark system and procedure.
Compare models on an accuracy–energy frontier
Make comparisons only when the task, evaluation set, preprocessing, correctness metric and workload are matched. Put the underlying figures next to each other:
| Comparison axis | What to hold constant or disclose |
|---|---|
| Functional correctness | Same examples, labels, tolerance, threshold and metric implementation. |
| Energy | Same phase, request mix, duration, denominator and measurement boundary. |
| System | Hardware, software, drivers, precision, number of nodes and interconnect. |
| Service behavior | Latency, throughput, concurrency, batch size and failure/retry policy. |
| Evidence quality | Meter accuracy, sample rate, repeat runs, uncertainty and estimated components. |
Plot or tabulate accuracy against energy per completed task. A model that is slightly less accurate but dramatically cheaper may be preferable for a low-risk application; a high-stakes application may reject it because the error cost is unacceptable. Make that trade-off explicit instead of ranking models by a single ratio.
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ITU lists an observed advanced metric, “energy efficiency score = model accuracy ÷ energy consumed.” Use such a ratio only for a narrowly defined, matched comparison. It has no universal meaning across different tasks, metrics, token lengths or system boundaries, and it must not replace the component accuracy and energy values.
Include latency and throughput when deployment matters
Energy alone can reward a system that is too slow for users. ISO’s discussion of performance treats processing speed, resource use, energy efficiency, latency and throughput as relevant performance dimensions, while functional correctness concerns whether outputs are correct. Report:
- Latency percentile or other agreed latency statistic.
- Completed inferences or tokens per second.
- Concurrency and batch size at which those values were measured.
- Whether queueing, preprocessing and post-processing time are included.
Keep the correctness test separate from the load test when necessary, but use the same model and serving configuration. A throughput increase achieved by changing batch size is not a fair energy comparison unless the workload change is stated.
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Copy the following fields into a benchmark record or model card:
- Model and version: name, checkpoint, quantization or precision.
- Task and evaluation data: task definition, dataset identity/version, sample count and preprocessing.
- Correctness metric and result: metric, threshold or tolerance, score and uncertainty.
- Energy phase: training or inference.
- System boundary and hardware/software: devices, hosts, nodes, interconnect, drivers and serving or training stack.
- Workload and run conditions: batch, concurrency, request pattern, prompt/output lengths, steps and stopping rule.
- Energy result and denominator: watt-hours per training run, inference or token.
- Measurement method and sampling: meter location, accuracy, sample interval, timestamps and integration method.
- Estimated components and assumptions: every estimated load, utilization, runtime and conversion.
- Latency and throughput: definitions, percentiles and operating point.
- Uncertainty and repeat runs: repeats, intervals, failed runs and variation.
Preserve the raw power readings, timestamps, request counts and evaluation outputs. A summary number without those records cannot be audited or recalculated when the denominator or boundary is questioned.
Know what standards do—and do not—cover
Accuracy guidance
ISO/IEC DIS 4213 is a draft focused on classification, regression, clustering and recommendation. It emphasizes task-specific metrics, leakage prevention and statistical significance, and notes that its approaches are not necessarily comprehensive. It does not provide a universal evaluation method for every open-ended generative task, so document a domain-specific protocol for those systems.
Training-efficiency context
ISO/IEC TS 42112:2026 is a published technical specification on factors affecting machine-learning training efficiency and optimization approaches. It can inform how you describe training efficiency, but it does not substitute for measuring the energy of a particular run.
Environmental-indicator work
IEEE P7100 is an active standards project proposing harmonized measurement of AI environmental indicators, including energy and related impacts. It is not a published standard; do not describe it as a mandatory method.
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For general-purpose AI providers under the EU AI Act, Commission service-desk guidance says known or estimated energy consumption must be documented, and unknown consumption may be estimated from computational resources. The Commission may set more detailed methodologies by delegated act; interim guidance points to the Model Documentation Form and Code of Practice. This is EU regulatory guidance for the covered providers, not a universal measurement rule for every practitioner or geography.
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Common mistakes and how to correct them
Reporting “accuracy” without a task
Problem: A percentage is presented without labels, tolerance, threshold or class distribution.
Correction: Name the task, metric, data version and decision rule, then include the error measures that reflect the application.
Testing on tuned data
Problem: The test set influenced prompts, thresholds or model selection.
Correction: Lock a separate evaluation set and record every development use of data.
Measuring only the accelerator
Problem: Host and interconnect energy are omitted while the result is called total model energy.
Correction: Expand the boundary or label the figure as device-only and list exclusions.
Using maximum rated power as measured energy
Problem: A specification is multiplied by runtime without measuring utilization.
Correction: Meter the relevant boundary, or label the calculation as an estimate and disclose the assumed power.
Comparing incompatible inference numbers
Problem: Token energy is compared across different prompt lengths, output limits, batching or hardware.
Correction: Match the workload and report all generation and serving conditions.
Ignoring uncertainty
Problem: A tiny score or energy difference is treated as decisive.
Correction: Repeat runs, report variation and use a significance or interval method appropriate to the task.
Make the result reproducible
Publish the protocol with the model result: evaluation files or an access description, preprocessing code, metric implementation, configuration, power logs, sampling interval and integration script. State which parts are directly measured and which are estimated. Record software and driver versions, because updates can change both correctness and power. When a full facility boundary is unavailable, say exactly what is excluded rather than implying a whole-life figure.
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The most useful conclusion is a paired one: “Under this task, test set, system boundary and workload, the model achieved this correctness result and consumed this much energy.” That statement is narrow enough to verify and informative enough to guide deployment.
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