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Estimate an AI workload’s footprint by defining exactly what is included, measuring or modelling power for the time the workload runs, allocating shared infrastructure, and then applying location- and time-appropriate factors for water and emissions. The answer differs substantially between training, development experiments, fine-tuning, and production inference. A defensible result states the hardware, utilization, geography, period, accounting method, and exclusions; it does not present one provider’s per-prompt number as a universal constant.
Start with a functional unit—such as kilowatt-hours per training run, watt-hours per million tokens, or watt-hours per request. Then report the boundary and uncertainty beside the number. Without those details, two technically correct estimates can differ by orders of magnitude.
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What exactly are you estimating?
Define the workload and the unit before collecting data. “AI energy use” can mean only accelerator electricity, the full server, a share of idle capacity kept available for a service, or a complete life-cycle footprint including manufacturing. Those are different questions and must not be mixed.
Choose a functional unit
- Training: energy per complete run, per training step, or per processed token. Include restarts, evaluation passes, and checkpointing if they are part of the run you are reporting.
- Development and fine-tuning: energy per experiment, successful run, or model release. Log failed runs and hyperparameter searches instead of counting only the final run.
- Inference: energy per request, per generated token, or per million input and output tokens. Record prompt length, output length, routing, batching, concurrency, and the measurement period.
A workload-level total is useful for capacity planning, while a per-request or per-token figure helps compare services. For a highly skewed request distribution, publish the median for a typical request and also provide totals or averages for aggregate demand.
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Why does the measurement boundary change the answer?
The measurement boundary is the list of activities included in the metric. Cooper Elsworth and coauthors wrote in their 2025 Google production-inference paper that “the measurement boundary is a definition of the activities that are included in metrics.” The same paper notes that a lack of agreement about included activities has produced published estimates for similar AI tasks that vary by an order of magnitude.
| Scope choice | What it includes | What to disclose |
|---|---|---|
| Accelerator-only | Power reported by GPUs, TPUs, or other accelerators while active | Device model, utilization, sampling rate, and whether host power is excluded |
| Server energy | Accelerators plus host CPU, memory, fans, storage and power conversion | Server configuration and method for allocating shared components |
| Operational facility energy | Server energy plus allocated idle machines and data-center overhead such as cooling, represented by PUE or measured facility data | Idle-capacity rule, PUE source and period, and whether networking is included |
| Broader operational footprint | Operational electricity plus network, storage, and end-user devices | Which network, storage, and client activities are counted and how they are allocated |
| Life-cycle footprint | Operational impacts plus manufacturing, transport, replacement and disposal of hardware | Embodied-emissions database, allocation period, utilization assumption and refresh cycle |
Google’s 2025 production analysis illustrates the issue. It includes active accelerators, host CPU and DRAM, machines assigned to the workload but idle, and PUE overhead. It excludes training, storage, external network, and end-user devices. A benchmark that reports active-GPU power cannot be compared directly with that full serving boundary.
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Write a short workload record that another analyst could reproduce. The record is as important as the meter reading because changing request sizes, batch behavior or routing can change energy without changing the model name.
Training runs
- Model version, dataset size and number of tokens or examples processed.
- Accelerator type and count, host configuration, interconnect and storage.
- Wall-clock duration, utilization, power-capping settings and interruptions.
- Checkpoint frequency, validation passes, restarts and failed runs included in the total.
Development and fine-tuning
Track exploratory runs, ablations, hyperparameter searches, data-processing jobs and evaluations. One ICLR 2025 life-cycle study estimated that development represented about half of final-training impact for the model series it examined. That result is study-specific, but it shows why counting only the final successful training run can understate the work needed to produce a model.
Inference services
- Input and output token distributions, not just a nominal prompt size.
- Model routing, quantization, speculative decoding and other software paths.
- Batch size, concurrency, queueing and accelerator utilization.
- Autoscaling policy and the idle capacity reserved for latency or availability targets.
- Measurement interval long enough to capture ordinary traffic, maintenance and low-demand periods.
How do you measure or model electricity use?
Use direct telemetry when possible, integrate power over the job, and use a calibrated model when direct measurement is unavailable. Rated thermal design power (TDP) is a specification, not a measurement of what a workload actually draws.
- Choose the measurement point. For equipment you control, use accelerator and server telemetry, a rack PDU with energy monitoring, or an appropriately rated power meter. Record sampling interval, units and whether readings are at the device, server, rack or facility boundary.
- Capture the complete interval. Begin before workload initialization and end after final writes and shutdown if those activities belong to the job. For inference, collect a representative period rather than a short peak sample.
- Integrate power over time. Convert each power reading to energy and sum the intervals. In simplified form,
energy = ∫ power(t) dt. Keep the raw series so spikes, throttling and idle periods can be audited. - Add host and shared capacity. Attribute CPU, DRAM, fans and other server loads, then allocate machines reserved for the service but not actively processing a request. Do not silently treat idle capacity as zero.
- Apply facility overhead. Add measured overhead or a documented PUE factor for the same site and period. State whether PUE is an annual average, a design value or a real-time measurement.
- Calibrate models when meters are unavailable. A model should reflect the tested hardware, software, utilization and workload rather than simply multiplying TDP by runtime. Report its error or uncertainty.
A practical accounting identity is workload energy ≈ measured active compute energy + allocated host energy + allocated idle-capacity energy + facility overhead energy. It is an accounting structure, not permission to use a generic overhead multiplier without explaining its source.
What does the H100 evidence show?
A 2025 study of intensive eight-GPU H100 training workloads measured average demand at 76% of the system’s 10.2 kW TDP rating. Its calibrated, architecture-specific model had an 11.4% mean absolute percentage error, compared with 27%–37% for the TDP-based approaches tested. These figures apply to the equipment and workloads in that study; they are not correction factors for every accelerator or cloud instance.
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How do you allocate shared energy to requests or tokens?
For a fleet, measure total energy attributed to a model or service over a representative interval, then divide by activity recorded over that same interval. The allocation rule must explain how shared servers, idle machines and facility overhead are assigned.
For example, an inference service can report allocated energy per request = service energy during interval ÷ requests served during interval. If token accounting is more stable, use total input and output tokens as the denominator and publish the token definition. Keep request mix, model routing and batching attached to the result; otherwise a change in traffic can look like a hardware improvement.
Means are useful for forecasting total electricity, but a few long prompts can distort a typical-request value. Report the median when the distribution is skewed, and provide percentiles if latency classes or request sizes differ materially. Never divide a peak-power reading by a daily request count: the numerator and denominator must cover the same period and scope.
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How can you estimate water use without confusing different quantities?
Separate onsite consumptive cooling water, water withdrawn and returned, and water associated with electricity generation. They are not interchangeable. A site can withdraw a large volume and return much of it, while consuming less; electricity generated elsewhere can add an indirect water component even when the data center itself uses little water.
Onsite cooling
Obtain cooling-system data for the facility and allocate consumption to the workload’s server energy or operating time. Cooling technology, climate, seasonal conditions, humidity controls and use of reclaimed water can change the result. A global average cannot describe local water stress or a particular cooling plant.
Electricity-related water
Apply a water-intensity factor for the electricity source, grid region and period when such a factor is available. The factor should identify whether it represents consumption or withdrawal and which generation technologies are covered. Keep this component separate from onsite water so a reader can see which assumption drives the total.
Why ranges are essential
A 2025 review by researchers in the Lawrence Berkeley National Laboratory Energy Technologies Area found more than a 10,000-fold variation in workload-level data-center water use across the conditions it analyzed. The drivers included water intensity per kilowatt-hour, server efficiency, utilization, cooling, infrastructure, climate and idle capacity. When any of these inputs is unknown, publish a range or mark the unresolved input instead of reporting milliliters to false precision.
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How do you convert energy into emissions?
Multiply operational electricity by a grid-emissions factor that matches the location and time period. State whether the factor is location-based (reflecting the grid serving the site) or market-based (reflecting contractual electricity instruments). Do not mix a real-time workload measurement with an annual or global factor without explaining the mismatch.
Embodied hardware emissions require a separate allocation. If a server’s manufacturing impact is spread over its expected service life, disclose the service-life years, utilization, replacement cycle and share assigned to the workload. If those data are unavailable, report operational emissions alone and say that manufacturing is excluded rather than inserting an unsupported estimate.
Development can also be a material life-cycle category. The ICLR 2025 study’s estimate that development was about 50% of final-training impact is evidence for tracking development runs, not a universal ratio. Keep operational electricity, embodied hardware and development activity as separate line items when that makes the assumptions clearer.
What do published inference figures demonstrate?
Google reported the following values for the median Gemini Apps text prompt in May 2025. They are provider-specific, point-in-time figures based on Google telemetry and accounting factors, not independently verified universal constants.
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|---|---|---|---|---|
| Comprehensive production methodology | 0.24 Wh | 0.03 gCO₂e | 0.26 mL | Includes active accelerators, host CPU/DRAM, assigned idle machines and PUE; excludes training, storage, external network and end-user devices |
| Narrower “existing approach” | 0.10 Wh | 0.02 gCO₂e | 0.12 mL | Narrower accounting approach described in the same paper |
The difference between the two rows is a demonstration of boundary sensitivity, not a universal multiplier. Google notes that the figures describe a median text prompt in one month and are not representative of every prompt or future performance. A long-context request, a different model route, a different region or a different accounting period can produce another result.
How should you compare two AI energy or water estimates?
Put the estimates on matched axes before deciding that one system is more efficient. A lower number can simply reflect fewer included activities or a different traffic mix.
| Axis | Questions to align |
|---|---|
| Task | Are both measurements training, development, fine-tuning or inference? |
| Workload | Are model version, input/output sizes, token distribution, routing and request mix comparable? |
| Hardware and software | Are accelerator, host, precision, batch size, compiler and serving stack comparable? |
| Utilization and idle capacity | Do both include queueing, autoscaling reserves and machines assigned but idle? |
| Boundary | Are host systems, storage, networking, PUE and end-user devices included in both? |
| Geography and period | Do measurements use the same facility region, climate and time window? |
| Emissions accounting | Are grid factors location-based or market-based, and do they use the same year? |
| Water definition | Are figures for consumption or withdrawal, and onsite or electricity-related water? |
| Life cycle | Are embodied hardware and development impacts included and allocated on the same basis? |
What should you do when telemetry or location data is missing?
Classify the result as provisional and show which missing inputs could change it most. Use a sensitivity range for utilization, PUE, grid factor and water factor rather than selecting a single convenient default. Keep measured values, provider-reported values and assumptions in separate columns of your calculation sheet.
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- Known: job duration, accelerator count, measured power, request count or token count.
- Provider-reported: cloud energy telemetry, regional PUE, cooling-water data or allocation methodology.
- Assumed: utilization between observations, embodied-emissions lifetime, grid factor or water intensity.
- Excluded: training, storage, network, clients or manufacturing that you did not measure.
If a cloud provider will not disclose a needed value, ask for telemetry at the smallest available scope and preserve the provider’s definition. If no credible factor exists, say “not established for this workload and location” and provide the operational subtotal that can be supported.
What are the most common estimation mistakes?
Multiplying TDP by runtime
TDP does not equal observed workload power. Use it only as a clearly labelled provisional bound or input to a calibrated model, and validate against telemetry when possible.
Counting only active accelerators
Host CPUs, memory, fans, power conversion, idle reserved machines and cooling can be material in production. State the exclusions if you intentionally publish an accelerator-only result.
Dividing a short sample by a daily request total
The numerator and denominator must cover the same period and traffic. Otherwise low-demand idle energy or peak bursts are misallocated.
Calling withdrawal “water use”
Label consumption, withdrawal and return flows separately, and identify onsite versus electricity-generation water.
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Using a global emissions or water factor
Location, season and grid mix matter. Match factors to the facility and measurement period, or publish a range.
Ignoring development and failed runs
Log experimentation, evaluations, restarts and failed jobs. A final-training-only total can omit a substantial part of the model’s operational history.
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What should a publishable estimate report?
Use a compact methods box beside every headline number. It should let a reader reproduce the denominator, identify the boundary and understand uncertainty.
- Workload: training, development, fine-tuning or inference; model and software version; token or request distribution.
- Hardware: accelerator and host configuration, count, utilization and power-capping settings.
- Period: start and end dates, duration and whether the interval is representative.
- Energy boundary: active compute, host systems, idle capacity, facility overhead, network, storage and clients included or excluded.
- Allocation: request, token or run denominator; batching and routing treatment; median, mean, percentiles and totals.
- Water: onsite consumption, withdrawal, return flows and electricity-related water, each with its factor and geography.
- Emissions: electricity factor, location- or market-based method, and any embodied hardware or development allocation.
- Uncertainty: measured versus modelled inputs, calibration error, ranges and unresolved data.
A defensible estimate is therefore not a single impressive decimal. It is a traceable calculation whose workload, boundary, allocation rule and location are explicit. That transparency lets readers compare like with like, update the factors as conditions change and avoid treating a provider-specific result as a law of AI computing.
Frequently Asked Questions
Can I use a GPU’s TDP as the energy figure?
Use TDP only as a provisional assumption, not as an observed measurement. Real demand changes with utilization, memory traffic, power limits, cooling and the rest of the server; a workload-calibrated model or telemetry is preferable.
Is water consumption the same as water withdrawal?
No. Withdrawal is water taken from a source, while consumption is the portion not returned to that source, often through evaporation. Report both when available and identify whether the number covers onsite cooling, electricity generation, or both.
Should embodied hardware emissions be added to every per-request result?
Only when you have a defensible allocation over the hardware’s service life, utilization and replacement cycle. Otherwise show operational emissions separately and label manufacturing as outside the reported boundary rather than assigning an arbitrary share.
How often should an inference estimate be updated?
Recalculate when model routing, hardware, batching, utilization, cooling conditions, grid mix or request distribution changes materially. A fixed calendar update can miss a major change in workload or facility operations.
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