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Freedom251 › Blog › AI

How to Estimate the Electricity Cost of Running AI Models

By Freedom251 Team · · 5 min read

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To estimate the electricity cost of running an AI model, first estimate the energy used in kilowatt-hours (kWh), then multiply it by the electricity rate that applies to the deployment. The arithmetic is straightforward; the important part is defining what the energy figure includes. A reading for a whole computer, an estimate for a cloud query, and a training-job total are different measurements, so there is no universal cost per AI prompt.

The basic electricity-cost formula

Electricity cost = energy consumed (kWh) × electricity price ($/kWh).

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If you know average power and operating time, convert watts to kilowatts by dividing by 1,000, then multiply by hours. For a system averaging P watts for t hours, energy is (P ÷ 1,000) × t kWh. At a rate of r dollars per kWh, the estimated energy charge is (P ÷ 1,000) × t × r.

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This estimates the electricity charge for the energy included in your measurement. It does not necessarily equal the complete bill, which can include fixed fees, taxes, demand charges, or time-varying prices.

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Estimate local AI use with a wall measurement

For a model running on your own computer, the most practical measurement is usually accumulated energy for the whole computer at the wall during a representative workload. A plug-in electricity usage monitor, such as a compatible P3 Kill A Watt power meter, can report watts and cumulative kWh. Check the selected meter’s outlet and load compatibility before using it.

A wall reading includes the computer’s other components and its idle draw; it does not isolate the model or identify which software process used the electricity. To estimate the additional energy associated with a workload, measure a baseline period and a representative inference period under similar conditions, then subtract the baseline. State whether you have made that subtraction.

Use accumulated energy over enough representative runs rather than relying on a brief watt reading. Include the parts of the workload relevant to your use case, such as warm-up, prompt processing, text generation, and any idle time you intend to count. Multiply the resulting kWh by your own electricity rate.

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Estimate cloud inference without a meter on the server

When you send a request to a cloud model, you generally cannot measure the provider’s serving hardware at the wall. If the provider publishes an energy figure, check what workload, hardware, measurement boundary, method, and date it covers before applying it to your own usage.

If no suitable provider figure is available, a bottom-up estimate can use accelerator power and throughput for a specified input and output workload. Any estimate should identify assumptions about utilization and other server or facility loads where those are included. Avoid dividing a data-centre electricity total by a query count unless the workloads and allocation method are known.

A 2025 arXiv preprint models frontier-scale inference workloads on an H100 node and reports a median of 0.34 Wh per query, with an interquartile range of 0.18–0.67 Wh. In a test-time-scaling scenario with many more tokens, it reports a median of 4.32 Wh per query. These are outputs for the paper’s modeled deployments, not measured constants for every model or prompt. The difference illustrates why token volume, reasoning or agentic behavior, hardware, utilization, and serving efficiency matter. See the paper and its stated modeling assumptions.

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Estimate training from the job’s energy boundary

A training estimate needs power over the job’s duration and a load factor that represents actual operation. Prefer job telemetry or whole-system energy measurements when available. If estimating from equipment specifications, distinguish accelerator power from total server and facility electricity.

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The International Energy Agency’s 2025 Energy and AI report describes a dataset-based method that multiplies maximum training power by estimated duration and an 84% load factor. Its method also scales GPU power to account for server equipment beyond the GPUs and adds a factor for non-IT load. These are elements of the report’s estimation approach, not default values for every training job. The IEA notes that AI model training and deployment occur mainly in data centres; its discussion of energy demand is available at Energy demand from AI.

Calculate fine-tuning as its own workload using its own runtime and energy data. Do not assign a foundation model’s training energy to each inference query unless you explain how that energy is allocated and across how many queries.

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  • Upgraded LCD display: With large screen size 2.36 inch x 1.85 inch, clearer monitor backlit, our electrical usage monitor can display the data clearer and more visible no matter day or night. 180°full wide viewing angles is great for reading and recording the data in any angles. No need to stand on the front of the display and bend over to read the numbers
  • Adjustable Backlight Time: Our upgraded watt meter has 5 options of backlight time. The default backlight time duration is 10 minutes(bL-0). If you want to change the backlight time, you can press and hold "UP" and "DOWN" button at the same time to enter backlight time setting, then press "UP" and "DOWN" to select the backlight time (bL-0 =10 minutes, bL-1=1 hour, bL-2=4 hours, bL-3=8 hours, bL-4=always on), finally press the "COST" to save the backlight time settings
  • Overload protection: When the power of the appliance exceeds the overload power, the LCD will display “OVERLOAD” to warn the user. All the buttons will quit working and can only be workable when you lower or remove the load power. The default overload power is 3680W and is adjustable from 0 to 3680W. In general, you need to set the overload power to 1800W before using. Just press the "function" button for more than 3 seconds to enter the setting
  • Data Memory Function: The wattage meter will record your power consumption data when you remove it from socket, or remove appliances from the electricity monitor. You can directly see the last data when you use it next time. This function can also automatically save the data when there is a sudden power failure
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Decide whether data-centre overhead is included

Accelerator or server electricity is not automatically the same as total facility electricity. Data centres also use power for associated infrastructure. The IEA reports that servers account for around 60% of electricity demand in modern data centres on average, with variation by facility type.

For a facility-level estimate, use a documented power-usage-effectiveness (PUE) factor or another source-supported overhead method, and explain which loads it covers. Do not add PUE again if the energy figure already represents the whole facility. The IEA’s account of data-centre energy demand provides context for the distinction between server and broader facility use.

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Results can differ with hardware, batch size, token volume, model behavior, utilization, idle draw, cooling, and facility design. The Lawrence Berkeley National Laboratory’s 2025 U.S. data-centre report uses a bottom-up model involving equipment shipments, per-device energy, cooling simulations, facility types, and locations, and includes sensitivity and compounded-uncertainty scenarios. Its national projections are not a conversion factor for an individual model or query; see the 2025 update.

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Use a relevant electricity price

For your own local system, use the rate and pricing structure on your electricity bill or tariff. For context, the U.S. Energy Information Administration’s 2025 annual-average retail prices were:

U.S. customer category 2025 annual-average retail price
Residential 17.30¢/kWh
Commercial 13.41¢/kWh
Industrial 8.62¢/kWh
All customer types 13.63¢/kWh
State range, all customer types 8.20¢–35.72¢/kWh

These are national and state context figures, not substitutes for a specific customer’s tariff. EIA notes that local prices vary with factors including fuel availability and costs and pricing regulation. Check its explanation of electricity prices and electricity data for definitions and data.

A cloud provider’s electricity procurement cost is not necessarily the retail price a household pays. Do not use a residential average as a proxy for provider operating cost unless you explicitly intend only a rough illustrative calculation. Keep electricity cost separate from an API or subscription fee: the service price is not itself the electricity used.

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Make comparisons on the same basis

To compare two models or a local setup with hosted service, estimate kWh for the same kind of task and use the same measurement boundary. Record the assumptions that can change the result:

  • Input and output token counts or the task mix.
  • Hardware, serving configuration, and utilization assumptions.
  • Whether idle energy, non-GPU server equipment, and non-IT facility energy are included.
  • The electricity price and geography used to convert kWh into cost.

Report estimated energy per comparable task separately from service fees. That makes clear whether a difference comes from electricity use, the rate applied, or the provider’s billed price.

Product prices and availability are accurate as of the date/time indicated and are subject to change. Any price and availability information displayed on Amazon at the time of purchase will apply.

AICloud Computingdata centerselectricity costenergy use

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