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Repair common Windows errors and clear accumulated junk for a smoother, more stable PC - no reinstall needed.Free scan · no reinstallThere is no universal energy or water cost per AI prompt. A defensible estimate starts by defining the workload and measurement boundary: which model and task, how much input and output work it performs, what hardware serves it, how fully that hardware is used, whether idle capacity and host systems are counted, and whether “water” means on-site cooling water or a broader lifecycle footprint. State the date, geography when known, assumptions and an uncertainty range. If the provider does not publish request-level and facility data, present a scenario—not a falsely precise number.
What an estimate is actually measuring
“Running an AI model” can mean training a model once, serving millions of inference requests, or running a single request in an application. Those activities have different workloads and accounting boundaries. A text completion, image generation job and video render also exercise different amounts and types of hardware.
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Before calculating, write down five things:
- Activity: training, fine-tuning, batch inference or interactive inference.
- Task: text, image, video, speech or another workload.
- Work volume: number of requests, input and output tokens, images, seconds of video, or another reproducible unit.
- Boundary: accelerator only, IT equipment, or production serving including facility overhead.
- Scope and date: location or fleet, operating period, and whether water is operational cooling water or a lifecycle estimate.
Token counts are useful descriptors for text inference, but they do not determine energy by themselves. The model architecture, context length, batching, hardware, utilization and software all matter.
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Accelerator-only energy
The narrowest boundary counts the active GPU, TPU or other accelerator while it computes. It is useful for comparing a kernel or a hardware configuration, but it omits equipment needed to serve a production request. It can therefore understate the energy allocated to a user request, especially when utilization is low.
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IT energy for serving
A fuller IT estimate includes the accelerator plus host CPU and DRAM, networking and storage that support the service. It may also allocate provisioned machines that are powered on but waiting for work. How idle capacity is allocated is a consequential assumption: a busy shared cluster and a lightly loaded reserved cluster can do the same user work with different energy per request.
Facility energy
Data-center overhead includes cooling, power conversion, lighting and other non-IT loads. If your starting figure is IT energy, a simplified facility estimate is:
facility energy (kWh) = IT energy (kWh) × PUE
Do not multiply by PUE a second time when the published figure already includes facility overhead. PUE must match the same site, period and accounting boundary as the IT number.
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Electricity calculation: a repeatable method
- Define one accounting unit. For example, one completed text request with its measured input and output tokens, or one generated image at a stated resolution.
- Measure or obtain energy per unit. Identify whether the value is accelerator-only, total IT or facility-inclusive, and record hardware, utilization and time period.
- Scale to the workload. Multiply energy per request by the number of requests (or by the number of other work units).
- Convert units. If the result is in watt-hours, divide by 1,000 to obtain kilowatt-hours.
- Convert energy to money separately. Multiply kWh by the applicable electricity tariff, including demand or time-of-use charges only if your calculation is designed to include them.
- Publish the assumptions and a range. Use measured percentiles or low/base/high scenarios when utilization, token mix or facility factors are uncertain.
In compact form:
total kWh = (Wh per unit × number of units) ÷ 1,000
electricity cost = total kWh × your tariff ($/kWh)
For a service that reports only IT energy, apply PUE once and label the result. For a provider that reports facility-inclusive energy, use it directly and document that no further PUE adjustment was made.
What published figures show—and why they cannot be generalized
| Example | Reported value | Boundary and conditions | What it demonstrates |
|---|---|---|---|
| Gemini Apps median text prompt | 0.24 Wh | Google’s comprehensive method; median prompts measured in May 2025; includes active accelerator power, host CPU and DRAM, idle machines and data-center overhead. | A full-stack provider estimate can be higher than an accelerator-focused figure. |
| Gemini Apps median text prompt | 0.10 Wh | Google’s narrower method for the same May 2025 prompt population, excluding several components. | The boundary alone can more than double the reported result. |
| Frontier-scale model query | 0.34 Wh median; IQR 0.18–0.67 Wh | Microsoft Research bottom-up model for an H100 node under stated workload, utilization and PUE assumptions. | A modeled result is conditional on its hardware and operating assumptions. |
| Frontier-scale query with test-time scaling | 4.32 Wh median | Microsoft Research scenario using 15 times more tokens than a typical query. | Additional reasoning or token generation can materially raise energy per query. |
These values describe different systems and boundaries. They are examples, not a universal benchmark. A Gemini Apps median prompt is not interchangeable with an H100-node model, and neither value predicts every commercial model or prompt.
Worked electricity examples
Scaling a provider value to a monthly workload
Suppose you use the reported 0.24 Wh median value only as a scenario for 1,000 comparable text prompts:
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0.24 Wh × 1,000 = 240 Wh = 0.24 kWh
The monetary charge is then 0.24 × your electricity tariff. The calculation is valid only for prompts comparable to that measured population and only under the provider’s comprehensive boundary. It does not include a separate, unverified lifecycle allocation for manufacturing or electricity generation.
Showing a range instead of a single point
For the Microsoft Research modeled case, 1,000 queries would correspond to 340 Wh at the median, with an interquartile range of 180–670 Wh under the same H100-node assumptions. With the test-time-scaling scenario, the median would instead be 4,320 Wh for 1,000 queries. This contrast is a workload scenario, not evidence that every long answer uses exactly 4.32 Wh.
Estimating water: define WUE and the water scope
Water figures are especially easy to misread because “water used” can mean different quantities. Water Usage Effectiveness (WUE) is commonly reported as liters per kilowatt-hour:
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WUE (L/kWh) = annual water used for the stated purpose ÷ annual IT energy (kWh)
Microsoft defines its WUE as annual liters used for humidification and cooling divided by annual IT kWh. Its FY25 global WUE was 0.27 L/kWh for July 1, 2024 through June 30, 2025, covering data centers Microsoft fully owns and controls that operated for 12 months. That is a fleet-specific operational factor, not a universal value for another provider or location.
On-site cooling-water estimate
When the numerator and denominator have matching boundaries, a simple estimate is:
cooling water (L) = relevant IT energy (kWh) × WUE (L/kWh)
Use an IT-energy denominator with an IT WUE factor. Do not combine a facility-inclusive energy figure with a WUE defined against IT energy unless you adjust the accounting explicitly.
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Consumptive versus withdrawn water
Google’s prompt-water method uses a Category 2 freshwater WUE defined as consumptive use—water input minus returned water—to represent water evaporated and unavailable for reuse. Google reports 1.15 L/kWh for both 2023 and 2024 in that factor. A withdrawal figure, a discharge figure and a consumptive figure answer different questions and should not be substituted for one another.
Provider examples for water and carbon
| Reported result | Conditions | How to interpret it |
|---|---|---|
| 0.26 mL of water per median Gemini Apps text prompt | Google’s derivation using its 2024 average fleet WUE and the provider’s prompt-energy method. | An operational provider estimate for that prompt population; not a complete lifecycle water footprint. |
| 0.03 gCO₂e per median Gemini Apps text prompt | Derived with Google’s 2024 average fleet grid carbon intensity. | A related provider estimate; it does not turn the water number into a universal benchmark. |
| 1.15 L/kWh freshwater WUE Category 2 | Google factor reported for both calendar years 2023 and 2024. | A specific consumptive-use factor for the stated fleet and method. |
| 0.27 L/kWh global WUE; 1.17 global PUE | Microsoft FY25 reporting population: fully owned and controlled sites operating for 12 months. | Fleet-level factors with Microsoft’s stated definitions and fiscal-year boundary. |
For the Google prompt result, the paper’s method subtracts data-center overhead energy from total energy before multiplying by the selected freshwater WUE. That produces an operational prompt-water estimate, not a full account of water consumed to generate electricity or manufacture hardware.
Why water footprint estimates vary by place and time
On-site cooling is only one part of an AI system’s water footprint. Electricity generation can consume water, and semiconductor, server and facility manufacturing add embodied impacts. Local climate, cooling design, grid mix, season and the hour of operation can change results. The 2024 study Making AI Less “Thirsty” discusses these spatial and temporal differences and notes that public runtime and embodied-water data remain limited.
If you include indirect water, create a separate line item with its own geography, time period and factor source:
- Operational site water: cooling and humidification at the data center.
- Electricity-related water: water associated with the generation mix supplying the servers.
- Embodied water: manufacturing and construction impacts allocated to the hardware and facility.
Do not add a generic multiplier to a provider’s on-site figure. If an indirect factor is unavailable, say that the estimate excludes it.
Converting an estimate into a transparent worksheet
A spreadsheet or script should preserve the inputs instead of hiding them in one headline number. Use one row per workload scenario with columns such as:
| Input | Example entry | Why it matters |
|---|---|---|
| Model and task | Text inference | Different models and modalities have different compute patterns. |
| Requests | Measured count | Sets the scale of the estimate. |
| Input/output work | Token counts or image/video units | Captures workload mix; tokens alone are not an energy meter. |
| Energy per unit | Wh, with percentile or scenario | Core measured or modeled quantity. |
| Energy boundary | Accelerator, IT or facility | Prevents accidental comparisons of unlike values. |
| PUE | Only when starting from IT energy | Adds facility overhead once. |
| Electricity tariff | Your applicable $/kWh | Converts physical energy to money; tariffs vary by location and contract. |
| WUE | Matching L/kWh factor | Converts energy to the stated operational water quantity. |
| Water scope | Consumptive on-site, withdrawal, or lifecycle | Defines what the liters mean. |
| Date and geography | Measurement period and region | Factors and grid conditions change over time and place. |
Then calculate separate outputs:
total energy = energy per unit × work unitsfacility energy = IT energy × PUE(only when required)electricity cost = facility or IT kWh × tariff, depending on the selected boundaryoperational water = matching IT kWh × matching WUE
Keep low, central and high rows when the uncertain inputs are utilization, token volume, PUE or WUE. The resulting interval is more honest than adding decimal places to an uncertain average.
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Put the estimates side by side only after aligning the following axes:
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- same model class and task;
- similar input and output token volume or equivalent work;
- same serving hardware or a clearly described hardware model;
- similar utilization, batching and idle-capacity treatment;
- same inclusion of host CPU, memory and networking;
- same treatment of facility overhead and PUE; and
- same water definition, denominator, geography and time period.
If an axis cannot be aligned, retain both figures with their qualifications rather than ranking one as “more efficient.” A narrower accelerator-only result can look better simply because it omits production-serving components.
Common mistakes and how to correct them
Turning a token count into an energy claim
Mistake: assuming a fixed Wh-per-token value applies to every model and context.
Correction: use token counts to describe the workload, then obtain a model- and hardware-specific measurement or scenario that includes utilization and serving overhead.
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Applying PUE twice
Mistake: multiplying a facility-inclusive provider number by PUE again.
Correction: identify whether the source reports IT or facility energy and apply PUE once only to IT energy.
Mixing incompatible WUE numbers
Mistake: using a site’s cooling WUE with a fleet-wide energy figure, or treating consumptive liters as withdrawals.
Correction: match numerator, denominator, site population and period; label the water quantity explicitly.
Calling an operational figure a lifecycle footprint
Mistake: presenting cooling water as the total water cost of AI.
Correction: list operational, electricity-related and embodied components separately, and mark unavailable components as excluded.
Reporting a mean without checking the distribution
Mistake: using an arithmetic mean when a small number of long or unusually expensive prompts skews the distribution.
Correction: report the statistic that matches the source’s method. Google’s paper says it uses the daily median as its aggregate value because the distribution is skewed and the arithmetic mean is sensitive to extreme prompts. That is a statement about Google’s analysis, not a universal statistical rule.
Hiding uncertainty behind a precise decimal
Mistake: publishing a single number when provider-side runtime, facility or lifecycle inputs are missing.
Correction: show a range or named low/base/high scenarios and identify which unresolved input drives the spread.
What you can and cannot claim
You can claim that a defined workload consumed an estimated amount of energy under a stated boundary, or that multiplying matching IT kWh by a stated WUE gives an operational cooling-water estimate. You cannot claim that one published prompt value applies to all models, providers, regions or prompt lengths. Nor can an on-site WUE result be presented as a full lifecycle water footprint without additional electricity-generation and embodied-impact accounting.
No public source reviewed here supplies enough request-specific runtime, facility and lifecycle data to calculate an exact footprint for every commercial model request. When those details are unavailable, preserve the unknowns in the result instead of silently filling them with a generic factor.
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Use wording like this in a technical report:
“For [model/task], we estimate [low–high] Wh per [unit] for [date and location]. The boundary includes [accelerator/host/idle/facility items]; PUE was [value or not applied]. At [number] units, that is [kWh], and at [tariff] the electricity charge is [currency amount or formula]. Operational water is estimated at [liters] using [WUE] L/kWh, defined as [consumptive/withdrawal and scope]. Electricity-generation and hardware-embodied water are [included/excluded/unknown].”
This format keeps the number attached to the workload, boundary, date and uncertainty that give it meaning.
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