To estimate an AI workload’s electricity cost, first define exactly what you are measuring, then measure or estimate energy for a fixed workload window, convert the result to kilowatt-hours (kWh), and multiply by the applicable electricity price. For example, an 800-watt workload running for 2.5 hours uses 2 kWh; at an assumed $0.15 per kWh, its energy charge is $0.30. Those inputs are illustrative, not a benchmark for a particular model or region.
The basic calculation: energy first, cost second
Energy is the total electricity consumed over a period. Power is the rate of consumption at a moment or averaged over an interval. A short run at high power can consume less energy than a long run at lower power, so an honest comparison reports both energy and runtime.
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Use one of these equivalent calculations:
- From average power: energy (kWh) = average watts × runtime in hours ÷ 1,000.
- From joules: energy (kWh) = joules ÷ 3,600,000, because 1 kWh equals 3,600,000 joules.
- From an electricity tariff: energy charge = workload energy in kWh × price per kWh.
Keep the measurement boundary, workload window and electricity rate attached to every result. “This model uses 2 kWh” is incomplete unless it says which devices were counted, what job was run, for how long and under what serving conditions.
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Choose the measurement boundary before collecting data
The same AI job can have several valid energy numbers. They answer different questions, so label the boundary rather than presenting one figure as universal.
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GPU-only energy
A GPU-only estimate attributes energy reported by the selected accelerators to the workload. This is useful when comparing GPU configurations or optimizing a kernel, but it excludes host CPUs, memory, storage, networking, fans, power-conversion losses and cooling. A GPU figure is not a whole-server or data-center figure.
GPU plus CPU energy
Some workload monitors can add CPU measurements to GPU energy. This better represents compute consumed by a training or inference process, but it still may omit memory, disks, network equipment and facility systems. State which CPUs and GPUs were included and whether other processes shared them.
Node, cluster or facility energy
A wider boundary can include memory, storage, networking, power-delivery equipment, cooling and other infrastructure. The cited Zeus documentation establishes GPU measurement and optional CPU measurement; it does not establish a universal factor for facility overhead. If you add wider-boundary measurements, document the instrumentation and calculation separately instead of applying an unexplained multiplier.
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Define the workload window
Record the exact start and end of the computation being attributed. Include the model, task, dataset or prompt set, precision, batch size, concurrency, input and output sizes, target latency or throughput, hardware and software versions. Decide whether initialization, data loading, compilation, checkpointing and idle periods belong in the window. Apply the same rule to every comparison.
Measure energy during a reproducible run
Software telemetry or a workload-level monitor is usually the most practical way to attribute energy to a defined training or inference job. The Zeus project documents GPU energy measurement, optional CPU energy measurement, workload timing and electricity-cost monitoring through its EnergyCostMonitor.
- Select the devices. Identify the GPU indices and, if applicable, CPUs that the job is allowed to use. Make sure the monitored indices match the devices actually executing the work.
- Fix the workload. Record model version, task, hardware, framework, precision, batch or serving settings, input and generated-output sizes, concurrency and stopping conditions.
- Start the energy window. Capture a baseline or initial counter immediately before the workload. If startup energy is intentionally excluded, state that choice.
- Synchronize GPU execution. Deep-learning frameworks can dispatch GPU operations asynchronously. Insert the framework’s synchronization operation at the measurement boundaries so the ending reading includes work that was queued but not yet completed when the code reached the boundary.
- Run the complete window. Avoid unrelated processes on the measured devices, or record them as part of the boundary. For serving, run enough requests to represent the intended traffic pattern rather than timing one unusually short request.
- Stop and record. Capture the ending energy counter, runtime, device list and any CPU reading. Save raw values as well as the converted kWh result.
- Repeat when variability matters. Keep the workload and environment fixed, report the number of runs and summarize the spread. A single run can be useful for a rough bill estimate but does not show run-to-run variation.
Measurement resolution and support vary by hardware and framework. Verify that the telemetry source supports the actual accelerators and that its sampling or counter behavior is suitable for the workload duration. A monitor that only samples occasionally may miss short bursts; a cumulative counter can be more reliable when its reset and scope are understood.
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Convert a reading into kWh and an electricity charge
Worked example from average power
Suppose a workload averages 800 W for 2.5 hours:
800 W × 2.5 h ÷ 1,000 = 2 kWh
At an assumed tariff of $0.15/kWh:
2 kWh × $0.15/kWh = $0.30
The 800 W, 2.5-hour runtime and $0.15 tariff are hypothetical inputs showing the arithmetic. They are not a measured AI benchmark or a claim about a particular utility.
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If the meter reports 7,200,000 J for the same boundary and window:
7,200,000 J ÷ 3,600,000 J/kWh = 2 kWh
Apply the same tariff only after the unit conversion. Do not multiply joules directly by a price quoted per kWh.
Choose and disclose the tariff
Use the price that applies to the workload’s geography, account and billing period. State the tariff date and whether the value is a marginal rate for additional consumption or a blended rate that includes fixed charges and other components. Time-of-use, demand, taxes and regional rules can change the effective price. Zeus’s cost monitor accepts an electricity-price provider; it does not supply one universal rate for every location.
An electricity energy charge is not the same as a cloud invoice. Compute rental, storage, data transfer, support, taxes, reserved-capacity commitments and other provider charges require separate accounting.
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Why AI energy estimates vary
Hardware and utilization
Different accelerators, memory configurations and power limits change both instantaneous power and the time required to finish. Two systems with the same nameplate rating can consume different energy if one spends more time waiting on memory, input pipelines or communication. Report actual measured average power when available; label nameplate or estimated values as estimates.
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Training configuration
Training energy depends on model size, sequence length, data volume, precision, batch size, number of devices, communication, checkpoint frequency and the number of steps or epochs. A training estimate should identify the completed workload, not just a model family. If you extrapolate from a partial run, show the measured interval and the assumption used to extend it.
Inference and serving behavior
Inference energy is shaped by model, task, hardware, prompt or input size, generated output, batch size, concurrency and latency or throughput target. Warm, continuously batched serving is a different workload from a cold single-request call. Compare like with like and report energy per request, per generated token or per batch only when the denominator and configuration are explicit.
Multimodal inputs can have different energy per token or per request from text-only inputs. The ML.ENERGY Benchmark v3.0 analysis reports such variation, so a figure from one modality should not be presented as a universal cost for every query.
Runtime versus power trade-offs
Reducing power can lengthen runtime; increasing power can shorten it. Because energy is the integral of power over time, the lower-power setting is not automatically the lower-energy setting. Measure both energy and completion time, then choose according to the deadline, throughput requirement and electricity price.
How to compare published benchmark numbers responsibly
Published figures are useful context only when their scope is preserved. The following numbers come with specific owners, dates and assumptions:
| Figure | What it describes | How to use it |
|---|---|---|
| 46 models, 7 tasks and 1,858 configurations | ML.ENERGY Benchmark v3.0 analysis, published January 29, 2026, using NVIDIA H100 and B200 GPUs | Use as the scope of that benchmark release, not as coverage of all models, providers or serving systems. |
| 1,287 MWh | One GPT-3 training run reported in the Zeus project’s 2023 paper context | Quote only with its original study assumptions and historical context; it is not a current general estimate for GPT-style training. |
| Approximately 70% | GPU share of server power during DNN training, presented in the Zeus project’s 2023 research overview | Treat it as a reported study statistic, not a universal ratio for every server or workload. |
| Up to 30% energy reduction with little throughput loss | Potential result described for Perseus on large-model training in the Zeus project overview | Present as that study’s result, not a promise for arbitrary hardware, models or deadlines. |
When quoting any benchmark, include the measurement boundary, device, workload, date and whether the value is measured, modeled or extrapolated. A benchmark count does not turn its average into a per-query constant.
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Estimating GPU inference energy per request
Per-request figures are easy to misstate because serving systems share work across requests. First measure a representative window containing a known number of requests, then divide the measured energy by that count. Keep batching, concurrency, prompt length, generated tokens, modality and latency target fixed.
For example, if a 30-minute serving window uses 1.5 kWh and completes 600 requests under a documented configuration:
1.5 kWh ÷ 600 requests = 0.0025 kWh per request
That value applies to the measured traffic mix and boundary. It should not be reused for a different prompt distribution, batch size, model, GPU or service. If the workload includes idle time between requests, say so; if the server is continuously busy, say that instead.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Training estimates: from a measured slice to a full job
For a long training run, measure a stable interval after startup and before shutdown, then multiply only when the remaining work is expected to have the same configuration and utilization. Keep separate energy for evaluation, checkpointing, data preprocessing and failed or restarted runs if the goal is total project energy.
Do not infer total training energy from GPU nameplate watts alone. Nameplate power is a limit or design value, not proof of average draw. If you must estimate before running, state the assumed average watts, runtime, device count and utilization, and replace each assumption with measured data as soon as it is available.
Optimization choices that affect energy and cost
The Zeus documentation describes power-limit and batch-size optimizers. A power-limit objective can target minimum energy, minimum energy subject to a slowdown limit, or a time-and-energy cost trade-off. This is a decision problem, not a single “greenest” setting.
When to favor lower energy
- There is schedule slack and the workload’s energy charge matters more than completion time.
- The power limit reduces energy without causing a disproportionate runtime increase.
- Lower power helps fit a site’s capacity constraint or avoids thermal throttling.
When to favor faster completion
- A service-level objective requires a fixed latency or delivery deadline.
- Keeping hardware busy longer would cost more in labor, reserved capacity or opportunity cost than the electricity saved.
- Queueing or synchronization overhead makes a low-power configuration inefficient.
Measure the candidate settings on the real workload. Compare total kWh, runtime, throughput and any quality change; do not select a setting from a synthetic benchmark alone.
Does a GPU estimate include the whole data center?
No. A GPU reading covers only the devices included by the measurement tool. Adding CPU telemetry expands the boundary, but it still does not automatically include memory, storage, networking, cooling or power-conversion losses. A facility-level estimate requires separately documented instrumentation or a clearly justified overhead model.
Similarly, multiplying workload kWh by a tariff yields an electricity energy charge, not a complete cloud bill. Keep infrastructure overhead and provider billing as distinct lines in a report so readers can see which costs were measured and which were excluded.
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Use a compact record for every result:
- Workload: model, task, dataset or request mix, precision, batch size, concurrency and input/output sizes.
- Hardware: GPU model and count, CPU scope, host or node identifier, and software or framework version.
- Window: start and end definition, runtime, warm-up treatment and whether idle, initialization or checkpoint periods were included.
- Measurement: telemetry tool, device indices, raw joules or watts, sampling behavior and synchronization method.
- Boundary: GPU-only, GPU plus CPU, node or facility, with explicit exclusions.
- Conversion: formula, resulting kWh and any extrapolation from a partial run.
- Price: electricity rate, currency, geography, tariff date and marginal or blended basis.
- Uncertainty: repeated-run spread, estimated inputs and known sources of variation.
This record lets another operator reproduce the arithmetic without mistaking a narrowly measured number for a whole-system claim.
Troubleshooting common measurement mistakes
The measured window reports almost no GPU energy
Check that the monitored GPU indices match the devices used by the process. Then synchronize GPU work immediately before the ending reading. Asynchronous dispatch can leave kernels running after the host code appears to have finished.
The same job gives different results on each run
Look for changing batch sizes, request mixes, clock or power-limit settings, background processes, thermal throttling and different warm-up treatment. Run a controlled repeated window and report the variation instead of silently averaging incompatible runs.
The cost seems too low for a cloud invoice
Verify that you are comparing an energy charge with the same boundary as the invoice. GPU electricity can be only one component; compute rental, storage, networking, taxes and other provider charges are separate.
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Nameplate watts are not average workload power. Label the estimate, measure actual draw where possible and document whether the value represents one GPU, all GPUs, the host or a wider system.
Quick Recap
Practical checklist
- Define the boundary and workload window before running the job.
- Record hardware, model, task, batch or serving conditions and runtime.
- Use telemetry that supports the actual devices and framework.
- Synchronize asynchronous GPU work at measurement boundaries.
- Convert joules or average watts to kWh with consistent units.
- Apply a local, dated electricity tariff and identify marginal versus blended pricing.
- Report energy and runtime together.
- Keep GPU or CPU energy separate from facility overhead and cloud billing.
- Preserve benchmark scope and historical assumptions when quoting published figures.
- Repeat measurements when variability could change the decision.
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