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There is no single, provider-neutral energy figure for “an AI prompt.” Energy depends on the model, input and output lengths, serving setup, workload, and what the measurement includes. To get a useful result, define that boundary, measure a representative workload, and report energy alongside task quality and latency. Then test changes such as batching, quantization, or a different model against the same workload.
What counts as energy used by AI inference?
Inference is the work a trained model performs to respond to a request. Its energy can be reported at different boundaries, and those values are not interchangeable:
- Accelerator energy: energy used by a GPU or other accelerator. It excludes other server components.
- Whole-server energy: energy for the server, potentially including accelerators, host CPU, memory, and power-supply losses. Check the measurement method to see what is actually counted.
- Facility energy: energy attributed to data-center operations, potentially including cooling and other infrastructure. Allocation is especially important when the facility or server also serves non-AI work.
The IEEE Standards Association’s P7100 project describes a framework for reporting environmental indicators for AI training and inference, including separating AI-specific compute from general-purpose data-center compute. P7100 is an active project, not a completed published standard; its page identifies PAR approval on 2024-06-06.
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How to measure energy per inference or token
The International Telecommunication Union (ITU) gives the basic relationship as total runtime energy divided by inference count. For tokenized models, it gives energy per token as power multiplied by inference duration, divided by token count and 3,600. The divisor converts watt-seconds to watt-hours.
Energy per inference (Wh) = Total runtime energy (Wh) / Inference count
Energy per token (Wh) = (Power (W) × inference duration (seconds)) / (Number of tokens × 3,600)
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These formulas are only interpretable when the numerator and denominator refer to the same workload and boundary. Say whether “tokens” means generated output tokens, input plus output, or another defined count. For a service handling different kinds of requests, report energy per completed request as well as a clearly defined per-token measure.
A useful benchmark record includes:
- Model name and version, task set, and input and output lengths.
- Hardware and serving software, numerical precision, batch size, and concurrency.
- Measurement duration, energy instrument or telemetry source, and whether values are measured or estimated.
- Included components: accelerator, host CPU, memory, idle capacity, power-supply losses, and cooling.
- The exact denominator: completed requests, output tokens, or another stated unit.
- For emissions, the grid-intensity source, location and time basis, and whether accounting is location-based or market-based where applicable.
The ITU’s 2025 report, Measuring what matters: How to assess AI’s environmental impact, warns that assumptions about runtime, generic hardware, and generic carbon-intensity factors can add significant uncertainty. A measured result and an estimate based on assumed operating conditions should therefore be identified separately.
How much energy does an AI prompt use?
There is no single provider-neutral, directly measured figure established for an AI prompt. Prompt and response length, model, serving system, batching, idle capacity, and measurement boundary all affect the result. One provider’s reported number should not be treated as a universal average.
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For context, Google Cloud reported 0.24 Wh per median Gemini Apps text prompt in May 2025, along with 0.03 gCO2e and 0.26 milliliters of water. These are Google’s service- and method-specific figures, not a measurement of all AI prompts. Google says its emissions figure uses its 2024 average fleetwide grid-carbon intensity; its comparison baseline was a median text-generation prompt in May 2024. See Google’s explanation of its inference methodology.
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Google also reported that its data-center energy emissions fell 12% while electricity consumption grew 27% year over year in 2024. Those are company-wide, provider-reported figures, not measurements of inference energy alone. Google’s discussion of its methodology and company-level figures is at Our approach to energy innovation and AI’s environmental footprint.
How to reduce inference energy without losing useful output
Start with a baseline from the real task mix. Track energy per completed task and per token, then record latency and a task-quality measure. A change that makes answers shorter is not an improvement if it lowers usefulness or causes retries that increase total work.
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- INSTALLS IN CIRCUIT PANEL of most homes with clamp-on sensors. Supports Single phase, Single-split phase, and 2-wire systems. 3-wire systems; 3-phase, 4-wire Wye systems with earthed (TN or TT) neutral (no-Delta) are supported with an additional 200A sensor (sold separately).
- 24/7 ENERGY MANAGEMENT AND MONITORING: Automate, manage and control your home's real power anywhere, anytime to prevent costly repairs, conserve energy, and save costs. Monitor solar / net metering. PROTECTED BY A 1-YEAR WARRANTY.
- LOWER YOUR ELECTRIC BILL: Configure settings in the Emporia Energy App to automate energy management for time of use, peak demand, excess solar, and rewards programs. You can even see live reporting and invaluable savings opportunities instantly. Gauge real-time spending and get actionable notifications and automated energy management to help you reduce costs.
- REAL-TIME ENERGY DATA: REQUIRES 2.4 GHz WIFI WITH AN INTERNET CONNECTION to monitor energy use with iPhone / Android / Web app. Vue sensors collect energy data and are accurate from ±2%. The Vue is UL and CE Listed for your safety. 1 second data is only available in the app (when actively open) and retained 3 hours. Minute and hour data are retained in the cloud. 1 minute data is retained 7 days, 1 hour data is retained indefinitely. Export cloud data whenever you want in the app.
Test batching
Batching can change how efficiently hardware serves requests, but results depend on workload and concurrency. Compare realistic batch settings using the same task mix, and record throughput and latency as well as energy. An isolated benchmark may not reflect production batching behavior.
Test quantization and serving configuration
Lower-precision representations and other serving choices may affect energy, speed, and output quality. Evaluate the exact configuration on the target workload and establish acceptable quality and latency limits before rollout; do not assume an efficiency gain transfers unchanged to another model or service.
Compare models and architectures on equivalent tasks
Model size and architectural characteristics can affect inference energy. A fair comparison uses the same tasks and output-quality criteria, then checks energy per completed task and per output token, latency, and throughput. A lower energy figure per token alone does not establish that a model is more efficient for the task.
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A 2024 IEEE Access study introduced EnergyMeter, a Python tool for profiling software energy, and experimentally examined model characteristics, batch size, quantization, energy, and latency. Its findings apply to its test configurations, not as production-wide constants. See Measuring and Improving the Energy Efficiency of Large Language Models Inference.
Use production-scale assumptions for estimates
When direct measurement is unavailable, model throughput and operating conditions as closely as possible to the real service. Microsoft Research’s September 2025 analysis found that non-production estimates and assumptions could overstate energy use by 4–20× in the scenarios it studied. That range is not a correction factor for every published estimate; it is a reason to examine the assumptions behind a projection. Read Energy Use of AI Inference: Efficiency Pathways and Test-Time Compute.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Keep energy and carbon results separate
Energy is measured in units such as joules or watt-hours. Carbon estimates require an electricity emissions factor and therefore depend on where and when electricity is consumed, the factor’s source, and the accounting basis. Report those assumptions with the emissions result; a carbon figure is not simply another unit for energy.
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What to include in an energy comparison
When comparing two models or serving configurations, put the values side by side only when they use a consistent measurement basis. Include:
- Energy per completed task and energy per output token, with token-count basis stated.
- Quality on the relevant task and the latency users experience.
- Throughput, batch size, and concurrency assumptions.
- Measurement boundary, instrument or estimation method, and material uncertainty.
- Carbon intensity for the deployment location and time, if comparing emissions.
If a published result omits a key boundary or workload detail, treat it as context rather than a directly comparable benchmark. The InferenceCarbon methodology, updated 2026-10-01, discusses estimation assumptions and uncertainty: How we estimate the carbon of LLM inference: methodology v1.0.0.
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