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To estimate an AI workload’s energy and water use credibly, first define exactly what the estimate covers, then measure or model power for the hardware and time involved, allocate shared infrastructure to a clear unit such as a training run or request, and apply location- and time-specific emissions and water factors. Training, development, fine-tuning and production inference produce different profiles, so one “energy per prompt” number cannot represent them all. State the accelerator and host hardware, utilization, workload mix, geography, period, cooling and data-center assumptions, and every exclusion. Where telemetry is missing, publish a range or an unresolved input instead of false precision.
What does an AI workload energy estimate actually measure?
An estimate is only meaningful after you define its measurement boundary: the activities and equipment included in the metric. Two analyses of the same model can differ by an order of magnitude when one counts only active accelerators and the other adds host systems, reserved idle capacity and facility overhead. As Cooper Elsworth and coauthors put it in their 2025 Google paper, “The measurement boundary is a definition of the activities that are included in metrics.”
Choose a functional unit before collecting numbers. Typical units are energy per training run, per processed token, per fine-tuning job, per development experiment, or per inference request. A total for a month or year is useful for infrastructure planning, while a per-unit figure helps compare workloads. Publish both when readers need to understand scale and efficiency.
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Training
Training usually runs accelerators for long, predictable periods, but checkpointing, evaluation, failed runs and hyperparameter experiments can materially change the total. Record every run that belongs to the project, not just the final successful run, if the goal is a project or model-development footprint.
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Development and fine-tuning
Development includes exploratory notebooks, ablation studies, prompt tests, data preprocessing, fine-tuning and discarded experiments. These jobs may be shorter than final training yet numerous enough to dominate. An ICLR 2025 life-cycle study estimated that development represented about half of the final-training impact for the model series it examined; that result is specific to those models and accounting choices, but it demonstrates why development should not be silently omitted.
Inference
Inference estimates depend on request and output length, model routing, batching, concurrency, cache use, latency targets and the time period. A short median request can coexist with a small number of very long requests that consume most of the energy. Capture the full request distribution and explain whether your result is a median, mean or aggregate total.
Which activities belong inside the boundary?
For an operational estimate, start with the accelerator and its host, add capacity kept available for the workload, and account for data-center overhead. Then list exclusions explicitly. The following boundary map makes the choice auditable.
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|---|---|---|
| Active accelerators | Direct compute power during execution | Include for every operational estimate |
| Host CPU and DRAM | Feeds data, handles orchestration and may stay busy while accelerators wait | Include when measuring a complete server or fleet |
| Idle machines reserved for availability | Provisioned capacity consumes power even when a request is not running | Allocate over a representative production period |
| Data-center overhead | Cooling, power conversion and other facility loads | Include through a disclosed PUE or equivalent measurement |
| Network | Moves data between users, services and storage | Include only if the stated boundary covers it |
| Storage | Holds model weights, datasets, checkpoints and logs | State whether storage energy is included |
| End-user devices | Phones, laptops or desktops consume energy while displaying results | Usually excluded from a data-center estimate; disclose the exclusion |
| Training and development | Can be a large share of a model’s life-cycle impact | Include for a project or life-cycle total, not for an inference-only metric |
| Hardware manufacturing | Embodied emissions and resource use occur before deployment | Report separately unless a defensible allocation is available |
Google’s 2025 production-inference methodology illustrates a broad operational boundary: active accelerators, host CPU and DRAM, workload-assigned idle machines and PUE overhead. That analysis explicitly excluded training, storage, external network and end-user devices. Its figures are therefore not interchangeable with an active-GPU-only benchmark.
How should I describe the job before measuring it?
Create a workload record that another analyst could reproduce. Include the model and software version, hardware configuration, start and end times, utilization, input and output sizes, and the unit you will report.
- Classify the activity. Mark it as training, development experimentation, fine-tuning or inference. For a project total, identify which categories are included.
- Record the hardware. List accelerator type and count, host CPU and memory, node count, interconnect and any other machines assigned to the job.
- Describe the workload. For training, capture dataset size, sequence length, batch size, epochs, checkpointing and evaluation. For inference, capture request and output-token distributions, batching, concurrency, routing and cache behavior.
- Set the period. Use the complete job duration for a run, or a representative day or longer for a production service. Include warm-up, retries and failed work when they belong to the service being assessed.
- Choose the functional unit. Examples include kilowatt-hours per run, watt-hours per million tokens, or watt-hours per request. Define whether the unit covers only successful responses or all capacity consumed.
How do I measure or model the electricity?
Direct power measurement integrated over time is the strongest starting point. If you cannot measure the equipment, use a calibrated, workload-specific model and publish its assumptions. A hardware nameplate or thermal design power (TDP) is a rating, not a measurement of what the job actually draws.
Use telemetry when you control the equipment
- Measure power at the highest practical boundary, such as a server or rack power-distribution unit, rather than reading accelerator utilization alone.
- Synchronize the meter with job start and end times, and sample often enough to capture batching, checkpoint spikes and idle intervals.
- Integrate power over time to obtain watt-hours or kilowatt-hours. Keep the raw interval data so unusual periods can be investigated.
- Subtract nothing silently. If the meter includes networking, storage or neighboring jobs, document the allocation method.
A rack PDU with energy monitoring or a suitable power meter can help when you operate the hardware. Such a meter does not, by itself, allocate shared idle capacity, cooling, water, electricity-generation impacts or embodied hardware to one AI task.
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For cloud or fleet workloads, request provider telemetry or build a model calibrated against measured runs on the same architecture. The model should respond to utilization, batch size, memory activity, communication and duration rather than multiplying a TDP by elapsed time.
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A 2025 study of intensive eight-GPU H100 training workloads measured demand averaging 76% of the system’s 10.2 kW TDP. Its calibrated H100-node model reported 11.4% mean absolute percentage error, compared with 27%–37% for the TDP-based approaches tested. Those figures apply to the tested H100 node, workloads and study method; they are not correction factors for every accelerator or cloud instance.
Use an explicit accounting identity
For an operational estimate, document the following structure:
Workload energy ≈ measured active-compute energy + allocated host energy + allocated idle-capacity energy + facility-overhead energy.
Apply each term to the same period and workload. Do not hide a generic overhead multiplier in the result. If you use PUE, identify its source, geography and time period, and say whether it represents the measured facility or an assumption.
How do I allocate shared energy to requests, tokens or runs?
Shared production systems require an allocation rule. Measure total energy attributed to the service during a representative interval, then divide by the requests, tokens or other units completed in that same interval.
- Choose a representative interval. A day may be adequate for a stable service; longer periods can capture weekly traffic, maintenance and seasonal changes.
- Attribute the machines. Include active machines and capacity reserved for availability. Explain how multi-tenant nodes or shared clusters are divided.
- Match numerator and denominator. Do not divide a month of energy by one week of requests, or count only successful responses while energy includes retries and failed work.
- Describe the workload mix. Report model routing, input and output sizes, batching, concurrency and cache behavior so another service cannot mistake the result for a universal constant.
- Select a summary statistic. Use a median when a typical request is highly skewed, and provide totals or averages when the question concerns aggregate demand.
Google’s 2025 production paper allocated total model energy to prompts over a day or longer. Its method shows why a benchmark that reports only active-GPU power is not equivalent to a full serving footprint that includes hosts, provisioned idle machines and facility overhead.
How do I estimate emissions from the energy result?
Multiply electricity by an emissions factor that matches the location and period of consumption. State whether the factor is location-based (reflecting the grid where electricity is used) or market-based (reflecting contractual instruments and supplier choices). Do not combine a current electricity total with an unrelated historical or national-average factor without saying so.
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Keep embodied hardware emissions separate unless you have a defensible allocation. If you include them, describe the manufacturing data, expected service life, hardware refresh cycle and allocation to the workload. A life-cycle total should identify operational electricity, development, training and embodied hardware as separate components so readers can see what drives the result.
Google reported 0.03 gCO₂e for the median Gemini Apps text prompt in May 2025 under its comprehensive production methodology. The number is provider-specific, point-in-time and not independently verified by the cited paper; it is not a value to apply to another model, provider, geography or request distribution.
How do I estimate water use?
Water accounting must distinguish at least three quantities: onsite consumptive cooling water, onsite water withdrawal, and water associated with electricity generation. Consumption is water not returned to the immediate source; withdrawal is water taken from a source and may be returned. A data-center water figure that does not identify which quantity it uses is incomplete.
Separate the water pathways
- Onsite cooling consumption: water evaporated or otherwise consumed by the facility’s cooling system.
- Onsite withdrawal: water drawn for cooling or related operations, including portions that may be discharged or returned.
- Electricity-related water: water consumed or withdrawn at power plants that generate the electricity used by the workload.
Estimate each pathway with a site- and time-relevant water factor where available. The result depends on server efficiency, utilization, cooling design, climate, grid water intensity, infrastructure, idle capacity and hardware refresh cycle. A global average can conceal local water stress and large differences between facilities.
A 2025 review by Lawrence Berkeley National Laboratory Energy Technologies Area researchers found more than 10,000-fold variation in workload-level data-center water use across the conditions it analyzed. The spread reflects differences in water intensity per kilowatt-hour and workload efficiency, among other determinants. It is a warning against presenting a single global milliliter-per-request constant.
Water use for a pathway = allocated workload energy × the pathway’s water factor. Report the factor’s geography, season or time period, cooling technology and whether it measures consumption or withdrawal. If those inputs are unavailable, publish a range or mark the result unresolved rather than converting electricity to water with an unexplained multiplier.
What do the published Gemini figures show about boundaries?
Google’s May 2025 figures are useful as an illustration of how methodology changes a result, not as a universal benchmark.
| Metric for one median Gemini Apps text prompt | Google’s comprehensive production methodology | Narrower “existing approach” in the same paper |
|---|---|---|
| Energy | 0.24 Wh | 0.10 Wh |
| Emissions | 0.03 gCO₂e | 0.02 gCO₂e |
| Water | 0.26 mL | 0.12 mL |
| Period and scope | Median text prompt in May 2025; provider telemetry and accounting factors | Same prompt and period under a narrower boundary |
The difference is evidence that scope and allocation choices matter, not a fixed multiplier that can be applied to another service. Google notes that the figures are point-in-time and not representative of every prompt or future performance. The request mix, model, location, hardware and accounting factors must be matched before any comparison.
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How should I compare two AI workload estimates?
Put estimates on matched axes before deciding that one workload or provider is more efficient. A lower number may simply exclude more equipment or use a different water or grid factor.
| Axis to match | Questions to ask |
|---|---|
| Activity | Are both estimates for training, development, fine-tuning or inference? |
| Model and request | Are model size, input and output lengths, token counts and routing comparable? |
| Hardware and software | Are accelerator generation, host systems, drivers and software versions comparable? |
| Utilization | Do batch size, concurrency, memory use and idle capacity match? |
| Boundary | Are both active-only, server-level or full-stack with PUE, network and storage? |
| Geography and time | Do the facilities, climate, grid mix and measurement periods match? |
| Emissions accounting | Are the factors market-based or location-based, and are embodied impacts included? |
| Water definition | Do both report onsite consumption, withdrawal, electricity-related water or the same combination? |
| Statistic | Are you comparing medians, means, totals or a tail of unusually large requests? |
When an axis cannot be matched, label the comparison directional and explain the limitation. A side-by-side table with “not stated” is more honest than filling an unknown value with a guessed average.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What should I do when provider telemetry or site factors are missing?
Use an uncertainty-aware estimate. Separate measured values from assumptions, and show which unresolved input could change the result most.
- Use a low, central and high case for utilization, idle capacity or facility overhead when the provider supplies no interval data.
- Keep TDP-based output provisional and identify it as nameplate-derived rather than observed.
- For water, provide a range across plausible cooling and grid-water factors, and identify whether the range covers consumption, withdrawal or both.
- Do not imply independent verification for provider-reported figures. Identify the provider, product, period and methodology.
- Refresh the estimate after a hardware change, model-routing change, major traffic shift, cooling change or grid-factor update.
Precision should reflect evidence. Two decimal places do not make an estimate accurate when utilization, geography or boundary is unknown.
What mistakes make AI energy and water estimates misleading?
Multiplying TDP by runtime
TDP is not observed job power. Measure the system or calibrate a model against the tested architecture and workload.
Counting only the accelerator
Host CPU and memory, reserved idle machines and facility overhead can be material in production. State whether they are included.
Using one prompt number for every request
Request and output lengths, batching, routing and concurrency change demand. Report the distribution and period used for allocation.
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Name the water quantity and pathway. Onsite cooling water and electricity-generation water are different accounting categories.
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Adding a generic PUE or water multiplier
A multiplier without a source, site and date conceals the largest assumptions. Use measured or site-relevant factors where possible.
Ignoring development and hardware life cycle
A final-training-only result answers a narrow question. A project or life-cycle result must identify development experiments and any embodied hardware allocation.
What should a publishable estimate report?
Use this checklist with every result:
- Workload category and model or service name.
- Functional unit and total period.
- Accelerator, host hardware and software configuration.
- Input and output sizes, batch size, concurrency, utilization and request distribution.
- Measurement source, sampling interval and integration method, or the calibration method used.
- Active, host, idle-capacity and facility-energy components.
- PUE source and scope, if applied.
- Network, storage, end-user, training, development and manufacturing inclusions or exclusions.
- Location, grid period and market-based or location-based emissions factor.
- Water pathway, consumption-versus-withdrawal definition, cooling system and water factor.
- Median, mean, total or range, with uncertainty and unresolved inputs.
- Collection date and conditions, so the estimate can be refreshed when the workload or infrastructure changes.
This format lets a reader reproduce the arithmetic, compare like with like and see where better telemetry would improve the answer.
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Frequently Asked Questions
Are water withdrawal and water consumption interchangeable in an AI report?
No. Withdrawal is water taken from a source, while consumption is the portion not returned to that source, often because it evaporates. A report should label both quantities separately and identify whether the figure covers onsite cooling, electricity generation, or both.
How often should a production AI footprint estimate be updated?
Update it whenever the model, accelerator fleet, routing, batching, cooling system or traffic mix changes materially. Even without an infrastructure change, refresh the estimate when the electricity or water factors and the representative request period no longer describe current operations.
Can a power meter on my laptop measure the energy of a cloud-hosted AI request?
It measures the laptop’s local energy, not the remote accelerators, hosts, idle capacity or data-center cooling serving the request. Cloud-side telemetry or a provider allocation is required for the service footprint; the laptop reading can be reported separately as an end-user-device boundary.
Why can two estimates for the same model differ so much?
They may count different hardware and facility loads, use different request distributions, run on different accelerators or locations, or apply different emissions and water factors. Comparing the boundary, period, utilization and accounting definitions usually explains more than the model name alone.
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