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Estimate electricity cost by multiplying energy use in kilowatt-hours (kWh) by the electricity rate in dollars per kWh. The difficult part is getting a useful kWh figure: a local meter measures the whole computer, cloud providers may not disclose per-request energy, and training estimates need power, runtime, and overhead assumptions. Keep the workload and measurement boundary clear so the result is not mistaken for a universal cost per AI query.

Start with the electricity-cost formula

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

If you know average power in watts and operating time in hours, calculate energy first:

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  1. Divide watts by 1,000 to convert to kilowatts.
  2. Multiply kilowatts by operating hours to get kWh.
  3. Multiply kWh by the applicable electricity rate.

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 calculation estimates the energy charge only. A bill may also include fixed charges, taxes, demand charges, or time-varying rates.

Choose a measurement boundary that matches the question

Before estimating, decide what electricity you want to count: the computer running a model, the provider’s servers serving a request, or the full data-centre facility. Those are different boundaries, so their kWh figures are not directly interchangeable.

Measure local inference at the wall

For a local model, a plug-in electricity usage monitor can measure the computer’s watts and accumulated kWh at the outlet. That reading includes the complete computer—such as the processor, graphics card, memory, storage, power-supply losses, and idle draw—not energy attributable only to the AI model.

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One example is a P3 Kill A Watt monitor; its manufacturer describes watt and cumulative-kWh readings. Check that the specific model is compatible with your outlet and the connected computer’s load. It cannot meter a remote cloud request or identify which software process used the electricity. P3 International: Kill A Watt.

For a workload-specific estimate, record energy over a representative period rather than relying on a brief instantaneous watt reading. Include the parts of the workload relevant to your use—such as warm-up, prompt processing, generation, and any idle time you intend to count. If you want to estimate the model’s additional energy rather than the whole-computer total, measure a baseline period under similar conditions and subtract it; state that you did so.

Estimate cloud inference from disclosed or modeled energy

You generally cannot meter the provider’s serving hardware yourself. If a provider publishes energy per prompt or query, check what hardware, workload, time period, and system boundary its figure represents. If those details are not available, an estimate can be built from accelerator power and throughput for the specified input and output, with utilization and other server or facility loads included only when supported by evidence. Label assumptions rather than presenting the result as a measured universal value.

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A 2025 arXiv preprint modeled frontier-scale workloads on an H100 node and reported median energy 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 reported a median of 4.32 Wh per query. These are outputs of the paper’s modeled deployments, not measured constants for every model, prompt, or provider. They illustrate how token volume, reasoning or agentic behavior, hardware, utilization, and serving efficiency can change the estimate. Energy Use of AI Inference: Efficiency Pathways and Test-Time Compute.

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A data-centre total divided by request count is not a reliable per-query estimate unless the workloads and allocation method are known. Likewise, an API’s fee is a service price, not the electricity cost of serving the request.

Estimate training from job energy and explicit overhead

A training estimate needs power over the training duration and a load factor that reflects actual use. 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 other server equipment and adds a factor for non-IT loads. These are elements of the IEA’s estimation approach, not default values for every training job. When available, use job telemetry or whole-system energy measurements instead. International Energy Agency: Energy demand from AI.

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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

Calculate fine-tuning separately from pretraining using its own runtime and energy data. Do not assign a foundation model’s training footprint to each inference query unless you explain how that energy is allocated and over what query volume.

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Account for data-centre overhead without double-counting

Accelerator electricity is not necessarily the same as total facility electricity. The IEA reports that servers account for around 60% of electricity demand in modern data centres on average, with variation by facility type. A facility-level estimate therefore needs a documented power-usage-effectiveness (PUE) factor or another supported method for including infrastructure loads. Identify what the factor covers, and do not apply PUE again if the source figure already represents whole-facility electricity.

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Energy estimates can vary with hardware, batch size, token volume, model behavior, utilization, idle draw, cooling, and facility design. 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 presents sensitivity and compounded-uncertainty scenarios. Its national projections are not a conversion factor for an individual model or query. LBNL: United States Data Center Energy Usage Report: 2025 Update.

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Use the electricity rate that applies to your situation

Use the rate for the deployment’s geography and customer class, preferably the rate on the applicable bill or tariff. As U.S. context, the Energy Information Administration’s 2025 annual-average retail electricity 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 retail context figures, not substitutes for your tariff. The EIA notes that locality differences reflect factors including fuel availability and costs and pricing regulation. A cloud provider’s electricity procurement cost is not necessarily a household retail rate, so do not use a residential average as a proxy for provider operating cost unless the result is explicitly a rough illustration. EIA: Prices and factors affecting prices; EIA: Electricity Data.

Compare estimates on equal terms

For a fair local-versus-cloud comparison, use the same task and a consistent measurement boundary. Compare estimated kWh for comparable input and output token volumes or task mixes, and document hardware, serving configuration, utilization assumptions, whether idle and non-IT facility energy are included, and the electricity price and geography used. Keep estimated electricity cost separate from service fees, such as an API charge.

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