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What you are actually measuring
“Accuracy” is not a universal property of an AI model. It is a result for a particular task, dataset, preprocessing pipeline, threshold and metric. A model can score well on one task and fail on another, or achieve a high aggregate score while making unacceptable errors for a minority class.
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Energy is also a property of a defined experiment, not a constant attached to a model name. A reading from an accelerator alone will differ from a node-level reading that includes the host, memory, storage, cooling and power-conversion losses. A multi-node training run also has network and interconnect consumption. Report both results together only when the task, evaluation set, workload and system boundary are matched.
- Correctness: how often, and in what ways, the outputs satisfy the task’s requirements.
- Energy: watt-hours consumed by the stated system boundary while completing a stated amount of work.
- Deployment context: latency, throughput, concurrency and request mix, when those affect the decision.
1. Define the task and the meaning of a correct answer
Write an explicit input-output contract
Before choosing a metric, document the input, expected output and consequences of failure. For a classifier, specify the classes and whether false positives or false negatives are more costly. For regression, define the target, acceptable error range and treatment of outliers. For clustering or recommendation, define what constitutes a useful grouping or ranking. A generative system may need a task-specific rubric, structured checks or human assessment because a single universal score is not established for open-ended output.
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ISO/IEC DIS 4213 addresses classification, regression, clustering and recommendation. It emphasizes that metric choice depends on the use case and domain constraints, and that the metric used for evaluation can differ from the metric optimized during training. The document also discusses statistical significance testing and cautions that functional correctness is not guaranteed in every observed circumstance.
Choose metrics that expose costly errors
| Task | Possible primary measures | What to specify |
|---|---|---|
| Classification | Accuracy, balanced accuracy, precision, recall (sensitivity), specificity, F1, area under a curve | Class prevalence, decision threshold, averaging method and the cost of each error |
| Regression | Mean absolute error, root mean squared error, mean or median absolute percentage error | Units, target scaling, treatment of zero values and outliers |
| Clustering | Internal measures or external agreement measures | Number of clusters, reference labels (if any) and the intended use of the grouping |
| Recommendation | Precision or recall at a cutoff, ranking measures, coverage or calibration | Candidate pool, cutoff, exposure policy and whether popularity or novelty matters |
| Structured or generative output | Schema validity, exact-match fields, task-specific tests and documented human or expert ratings | Rubric, evaluator instructions, sampling procedure and inter-rater treatment |
For a high-stakes classifier, overall accuracy alone can conceal harm when classes are imbalanced. Report sensitivity and specificity, or another pair that reflects the real decision costs, alongside the aggregate figure.
2. Build an evaluation set without leakage
Keep development and evaluation information separate
Use a representative test set that was not used to train the model, tune hyperparameters, select features, choose a prompt, set a decision threshold or repeatedly inspect errors. Information from outside the intended model input that enters development can produce an over-optimistic estimate. Leakage can occur through duplicate records, user or time overlap, preprocessing fitted on all data, labels encoded in filenames, or feedback that was fed back into model selection.
When a time-based or user-based split is appropriate, keep the split rule fixed and apply it before fitting data-dependent preprocessing. For a production estimate, make the evaluation population resemble the deployment population, including class prevalence, language, device type, geography and difficult cases that the model will actually see.
Record the evaluation configuration
- Dataset name, version or immutable identifier and date obtained.
- Number of examples, class counts and any exclusions.
- Preprocessing, tokenization, resizing, normalization and missing-value handling.
- Model version, weights, prompt or system instructions and decision thresholds.
- Metric implementation, averaging convention and treatment of invalid outputs.
- Random seeds and whether decoding is deterministic.
Do not call a validation score a final accuracy if the same score guided model selection. If you must iterate on the evaluation set, create a fresh holdout for the final estimate.
3. Quantify uncertainty and make comparisons fair
Use identical conditions for competing models
Compare models on the same task, examples, preprocessing, metric implementation and threshold policy. Keep request prompts, context limits, decoding settings, batch size and concurrency fixed for inference comparisons. A model that receives longer context or more sampling attempts is not being measured under the same workload.
Report variation, not just one number
Use confidence intervals, bootstrap intervals, repeated runs or an appropriate statistical test when sampling variation could change the conclusion. For paired predictions on the same examples, a paired analysis is generally more informative than treating the two scores as independent. ISO/IEC DIS 4213 includes discussion of significance tests for classification, regression, clustering and recommendation; select a method suitable for the metric and sampling design.
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Do not infer general superiority from one score on one dataset. State the population represented by the test set and any domains or languages that were not evaluated.
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4. Define the energy measurement boundary
Choose what is inside the system under test
Write the boundary before turning on the meter. Common choices are:
| Boundary | Includes | What it omits |
|---|---|---|
| Accelerator or device | Power reported by the processor or accelerator telemetry | Host CPU, memory, storage, cooling, power supply and other platform overhead |
| Node | All hardware and software resources on one server that affect performance or power | Other nodes and possibly facility systems outside the node’s power feed |
| Multi-node system | Every participating node and the interconnect carrying weights, updates or activations | Anything explicitly outside the declared facility or service boundary |
MLPerf Training defines the system under test from the hardware and software resources that affect performance or power and requires all nodes participating in a performance run to be measured. If an interconnect carries weights, updates or activations, its power must be measured or estimated under the applicable rules. The exact boundary changes the number, so never present a device-only reading as a model-only constant.
Record hardware, software and workload
- Processor and accelerator models, count, memory and power configuration.
- Host, memory, storage, network and power-delivery equipment included.
- Operating system, drivers, runtime, framework and model software versions.
- Training dataset, batch size, sequence or image dimensions, optimizer and number of steps or epochs.
- Inference request mix, prompt and output lengths, batch size, concurrency and rate.
- Whether the value is directly measured, partly estimated or entirely estimated.
Inference studies may omit end-user devices, data transfer and embodied hardware impacts. State those exclusions rather than implying a whole-life environmental result.
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Instrument the power feed
For a benchmark-style node-level measurement, MLPerf Training calls for power measurement at the power supply unit, with documented conversion assumptions where necessary, or an acceptable per-node power-distribution-unit measurement. A physical power meter is a defensible category when telemetry does not cover the declared boundary; select equipment rated for the voltage, current and server environment you are measuring.
The MLPerf rules target meter accuracy of 5%. They describe qualifying meter specifications or comparison against a reference meter under different loads. Treat that as a benchmark rule, not proof that every consumer meter is suitable for a server or multi-node installation.
Capture the timed interval
- Warm up the system using the same software and workload conditions as the measured run, but keep warm-up energy separate unless your protocol explicitly includes it.
- Start power sampling immediately before the timed training interval.
- Sample at least once per second throughout the timed portion. MLPerf Training specifies a minimum of 60 power samples for short runs.
- Start the training timer and record the start and end timestamps, step count and any interruptions.
- Stop sampling just after the timed run ends, and retain the raw timestamped readings.
- Sum the energy for every participating node and any measured interconnect or estimated component inside the boundary.
Integrate power into watt-hours
Power is a rate; energy is its integral over time. For readings taken at regular one-second intervals, calculate:
Energy (Wh) = Σ [power reading (W) × time represented by that reading (hours)]
For irregular timestamps, use the actual elapsed interval represented by each reading. Align the integration interval with the timed workload, and document whether the first and last samples represent a full interval or a partial one.
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Where a component’s power is estimated, MLPerf’s formula uses maximum specified power, run duration and a provisioning ratio. Label that portion as estimated and identify the assumptions. Do not combine a measured accelerator value with an assumed maximum for the rest of the server without showing the distinction.
6. Measure inference energy
Choose a denominator that matches the service
For a fixed request set, ITU gives this normalization:
Energy per inference (Wh) = total runtime energy (Wh) ÷ inference count
For tokenized models, it gives:
Energy per token (Wh) = [power (W) × inference duration (seconds)] ÷ [number of tokens × 3600]
Use the first denominator when each completed request is the unit customers care about. Use per-token energy when prompt and output lengths vary substantially, but report whether the token count includes input tokens, output tokens or both. Do not compare token figures produced with different generation limits, context lengths or sampling settings without explaining those differences.
Run a representative request mix
- Define the request distribution: prompt lengths, output limits, model routes, batch sizes, concurrency and arrival rate.
- Warm the service and separate initialization, model loading and cache-building energy from steady-state inference, or explicitly include them in an amortized calculation.
- Measure the declared hardware boundary for the full request interval, not just the accelerator kernel.
- Record completed requests, tokens, errors, latency and throughput alongside energy.
- Repeat the run enough times to show variation and report the aggregation method.
Inference can use less energy per unit activity than training while contributing substantially to total impact through frequent, long-term use. Real-world inference measurements remain uncommon; many published estimates assume a runtime, generic hardware or standard carbon-intensity factor. Mark such figures as estimates and list every assumption.
7. Put accuracy and energy on one decision frontier
Present the correctness result and energy result side by side only for matched conditions. A compact comparison table should include:
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| Model and version | Task and evaluation set | Correctness metric | Energy result | Boundary and workload | Latency/throughput |
|---|---|---|---|---|---|
| Version identifier | Same data, preprocessing and sample count for every model | Metric, threshold and uncertainty | Wh per run, inference or token; measured or estimated | Device, node or multi-node; hardware and software | Median or percentile latency and completed work per second |
An energy-efficiency ratio can summarize a specific comparison:
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Energy-efficiency score = model accuracy ÷ energy consumed
ITU lists this as an observed advanced metric, but it is not a universal cross-task score. Ratios can reward a small change in the numerator or hide a serious safety error. Keep the underlying metric and energy values visible, and use a ratio only after verifying that both quantities have compatible definitions.
When deployment matters, include latency and throughput. A lower-energy model that cannot meet the service-level latency target may not be the practical choice, while a model with slightly higher energy may reduce retries or serve more requests per hour.
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8. A reproducible reporting template
Copy this structure into a benchmark record or model card:
- Model and version: exact checkpoint, release or build.
- Task and evaluation data: dataset identity/version, split, sample count and exclusions.
- Correctness metric and result: definition, threshold, uncertainty and any significance test.
- Energy phase: training or inference.
- System boundary and hardware/software: included nodes, accelerators, hosts, interconnects, drivers and runtime.
- Workload and run conditions: steps, batch size, request mix, token lengths, concurrency and duration.
- Energy result and denominator: Wh per training run, Wh per inference or Wh per token.
- Measurement method and sampling: meter or telemetry source, sampling interval, calibration or accuracy and integration method.
- Estimated components and assumptions: any modeled power, runtime, utilization or carbon factor.
- Latency and throughput: measurement window, percentiles and completed work.
- Uncertainty and repeat runs: number of repetitions, variability and exclusions.
This record lets another practitioner distinguish a measured node result from an accelerator estimate and reproduce the correctness calculation without guessing hidden settings.
9. Common mistakes and how to correct them
Calling every metric “accuracy”
Problem: A single score hides class imbalance or application-specific costs.
Correction: Select the metric from the failure consequences and report complementary measures such as sensitivity and specificity where appropriate.
Evaluating on data used during development
Problem: Leakage inflates the apparent result.
Correction: Freeze a clean holdout, document the split and fit preprocessing only on development data.
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Problem: A power snapshot cannot represent a run whose load changes over time.
Correction: Sample across the timed interval and integrate watts over elapsed time to obtain watt-hours.
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Measuring only the accelerator
Problem: Host, memory, networking and conversion losses disappear from the number.
Correction: Declare the device boundary or measure the full node; include all participating nodes for distributed work.
Comparing unlike inference workloads
Problem: Prompt length, output length, batching and concurrency alter both energy and latency.
Correction: Match the request mix and disclose token accounting and service settings.
Turning an estimate into a measurement
Problem: Assumed runtime, generic hardware or a standard grid factor is presented as observed consumption.
Correction: Label estimates, list assumptions and separate measured and modeled components.
Ignoring the scope of standards
Problem: A method designed for four task families or benchmark training rules is treated as universal.
Correction: ISO/IEC DIS 4213 is a draft focused on classification, regression, clustering and recommendation; it does not cover every generative task. MLPerf rules are benchmark procedures, not mandatory instructions for every personal experiment.
10. Standards and regulatory context
ISO/IEC DIS 4213 provides the task-specific accuracy framework described above and notes that its approaches and methods are not necessarily comprehensive. ISO/IEC TS 42112:2026 is a published technical specification on factors affecting machine-learning training efficiency and optimization approaches; it can inform efficiency analysis but does not replace measuring the energy of a particular run.
IEEE P7100 is an active standards project proposing harmonized measurement of AI environmental indicators, including energy and associated impacts. It is not a published standard, so cite its status when discussing it.
For general-purpose AI providers covered by the EU AI Act, European Commission service-desk guidance says known or estimated energy consumption must be documented. Unknown consumption may be estimated from computational resources, and the Commission can set more detailed methodologies by delegated act. The Model Documentation Form and Code of Practice provide interim guidance. This is EU regulatory guidance, not a universal measurement method for every practitioner or jurisdiction.
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A defensible report might say: the model achieved a named task metric on a versioned holdout with a stated confidence interval; training consumed a stated number of watt-hours at a node or multi-node boundary measured at a documented sampling rate; or inference consumed a stated number of watt-hours per request for a specified prompt/output distribution. It would identify omitted stages, estimated components, hardware, software, workload and repeat-run variation.
The central discipline is to keep denominators and boundaries visible. Correctness answers whether the system performed the task; energy answers what the declared system consumed to perform a defined amount of work. Neither number is meaningful without the conditions attached to it.
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