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The most defensible estimate follows three steps: measure energy for a clearly defined training or inference window, convert the reading to kilowatt-hours (kWh), and multiply by the electricity price that applies to that workload. If you only have average power, use energy (kWh) = average watts × runtime in hours ÷ 1,000. If your meter reports joules, divide by 3,600,000 to obtain kWh.
That arithmetic is simple; the difficult part is defining what the number covers. Label whether the result is GPU-only, GPU plus CPU, a whole server, or a wider facility boundary. Record the model, task, hardware, input and output conditions, batch or concurrency, runtime, synchronization method, tariff, geography and date. A figure without those assumptions cannot be compared reliably with another workload.
What an AI energy estimate actually measures
Energy is the amount of electricity consumed over a period. Power is the rate of consumption at a moment or averaged over an interval. A run drawing 1,000 watts for 30 minutes uses 0.5 kWh; a run drawing 500 watts for two hours uses 1 kWh. The lower-power run is not automatically the lower-energy run because it lasts longer.
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For an AI job, the measurement window should match the work you want to attribute. A training estimate might start when the first batch enters the accelerator and end after the final synchronized step. An inference estimate might cover a fixed number of requests, a defined token count, or a serving interval at a specified concurrency and latency target. Startup, data loading, checkpointing, idle periods and shutdown may be included or excluded, but the choice must be stated.
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Energy telemetry is also a boundary question. A GPU reading is not a whole-server reading, and a server reading is not automatically a data-center reading. Cooling, power-conversion losses, storage, networking and other facility loads require additional instrumentation or documented overhead assumptions.
Define the boundary and workload before measuring
Write down the scope before starting a run. These fields prevent a precise-looking number from being misinterpreted:
- Job: training, fine-tuning, batch inference, interactive serving, evaluation or another task.
- Model and software: model version, framework, precision, quantization and relevant kernels.
- Hardware: GPU model and count, CPU, memory, node type and any accelerator partition.
- Workload shape: input modality, prompt or input size, generated output, batch size, concurrency, throughput target and latency target.
- Window: start and end events, number of steps or requests, and whether warm-up, data loading or idle time is included.
- Boundary: components whose energy is counted.
- Price assumption: electricity rate, currency, geography, tariff date and whether the rate is marginal or blended.
| Boundary | What it can answer | Important limitation |
|---|---|---|
| GPU only | Energy attributable to the selected accelerator devices during the window | Excludes CPU, memory, disks, networking, power-conversion losses and cooling |
| GPU plus optional CPU | Accelerator energy plus CPU energy when both are instrumented for the same window | Still does not establish whole-node or facility energy |
| Whole server or node | Energy drawn by the measured server boundary | Requires separately documented server-level instrumentation beyond GPU and CPU telemetry |
| Facility or data center | Broader operational electricity, potentially including cooling and infrastructure | Requires facility data and an explicit allocation method; a device reading alone cannot provide it |
When comparing two results, keep the boundary the same. A GPU-only result should not be compared with a whole-node result as if they measured the same thing.
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Use workload-level telemetry when attribution matters
Software telemetry or a workload-level monitor is appropriate when the goal is to assign energy to one training or inference job rather than to an entire machine’s mixed activity. The Zeus project documents GPU energy measurement, optional CPU energy measurement, workload timing and energy-cost monitoring. Its EnergyCostMonitor accepts an electricity-price input and reports cost for the measurement window.
Choose a monitor that supports the actual accelerator, operating system and framework. Check whether it reports instantaneous power, integrated energy, or both; whether it can select the intended device indices; and whether it records timestamps and runtime. A reading from the wrong GPU or from the entire host can silently produce the wrong attribution.
Synchronize asynchronous GPU work
Deep-learning frameworks commonly dispatch GPU operations asynchronously: the CPU can continue while kernels remain queued on the device. If the measurement ends immediately after the dispatching call, the code window may close before the GPU has completed the work. The resulting number can omit part of the computation.
Place a framework-appropriate GPU synchronization operation immediately before starting the measurement and immediately after the workload, before stopping it. In pseudocode:
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synchronize_gpu()
start_energy_measurement()
run_training_or_inference_window()
synchronize_gpu()
stop_energy_measurement()The exact synchronization call depends on the framework and accelerator. Document the call and the devices it covers so another person can reproduce the boundary.
Control the window and repeat runs
- Record the selected device IDs and confirm that no unrelated job is using them.
- Synchronize the devices, then begin the energy and time measurement at the same logical event.
- Run a fixed number of training steps, samples, tokens or requests under fixed batch and concurrency settings.
- Synchronize again before ending the measurement.
- Save integrated energy, elapsed time, average power if available, workload count and configuration metadata.
- Repeat enough times to reveal run-to-run variation, and report the mean or distribution together with the number of runs.
If a warm-up phase is excluded, run it before the timed window and say so. If a serving process remains alive between requests, decide whether idle draw between requests belongs to the service estimate or to a separate standby category.
Convert readings to kWh
Use one of these equivalent conversions:
- From joules:
kWh = joules ÷ 3,600,000. - From average watts and hours:
kWh = watts × hours ÷ 1,000. - From watt-hours:
kWh = watt-hours ÷ 1,000.
Keep the type of input attached to the result. “800 W” could be a measured average, a short-term reading, or a nameplate limit; those are not interchangeable.
Worked arithmetic example
Suppose a workload averages 800 W for 2.5 hours. The estimated energy is:
800 × 2.5 ÷ 1,000 = 2 kWh
At an assumed electricity price of $0.15 per kWh, the energy charge is:
2 × $0.15 = $0.30
These wattage, duration and tariff values are hypothetical inputs showing the calculation; they are not a measurement of a particular model, provider or region.
Convert an integrated joule reading
If the monitor reports 7,200,000 J for the same boundary and window:
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7,200,000 ÷ 3,600,000 = 2 kWh
Applying the same assumed tariff gives $0.30. Do not combine a GPU joule reading with a CPU runtime or a different time window; the energy and price calculation must use matching boundaries.
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The basic charge is:
energy charge = workload kWh × applicable electricity price per kWh
Use the rate that actually applies to the workload’s location and billing arrangement. State:
- country, region or utility territory;
- currency and tariff date;
- whether the value is a marginal energy rate, a time-of-use rate, or a blended average;
- any demand, minimum, capacity or subscription components that are outside the energy charge.
A cloud invoice can contain compute rental, storage, network transfer, licensing, taxes and other line items. Multiplying kWh by an electricity tariff estimates the electricity-energy component, not the complete cloud bill. If a provider supplies a measured energy price, document how its meter boundary maps to your workload and whether idle allocation or shared infrastructure is included.
Why two valid estimates can differ
Energy is workload- and configuration-dependent. The following variables can change both instantaneous power and total energy:
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| Variable | How it changes the estimate | What to hold constant for comparison |
|---|---|---|
| Accelerator model and count | Different devices have different power behavior, memory systems and throughput | GPU model, count, clock or power-limit policy and partitioning |
| Model and task | Architecture, sequence length, sparsity and kernel mix alter computation and runtime | Model version, task definition, precision and software stack |
| Batch size and concurrency | Can improve utilization but may increase power, latency or queueing | Batching policy, concurrency and throughput or latency target |
| Input modality | Text, image, audio and mixed inputs can produce different work per request or token | Modality, input dimensions, prompt length and generated output |
| Serving behavior | Warm-up, idle intervals, dynamic batching and request arrival patterns change energy per request | Measurement interval, traffic pattern and treatment of idle time |
| Runtime and power policy | A lower power limit may lengthen the run; a higher limit may shorten it | Power limit, clocks, thermal conditions and deadline |
| Measurement boundary | GPU-only, CPU-inclusive and node-level values answer different questions | Components included and allocation method |
Compare both energy and runtime. A configuration that minimizes watts may not minimize kWh, while a configuration that minimizes kWh may miss a service-level deadline.
Use benchmark figures only within their stated scope
The ML.ENERGY Benchmark v3.0 analysis, published January 29, 2026, covers 46 models across 7 tasks and 1,858 configurations on NVIDIA H100 and B200 GPUs. Those counts describe that benchmark release; they do not represent every model, accelerator, provider or serving pattern. Quote a result only with the matching model, task, hardware and configuration.
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The same analysis reports that multimodal inputs can have different energy per token. A per-token value therefore needs its modality and token definition attached. It should not be turned into a universal “energy per query” claim for an unspecified service.
For inference comparisons, match all of the following:
- model and precision;
- hardware and number of devices;
- input modality and input size;
- prompt or input tokens and generated output tokens;
- batch size and concurrency;
- latency or throughput target;
- warm-up, idle and queueing treatment;
- energy boundary and measurement window.
Training figures need historical context
A Zeus project research overview, in the context of a 2023 paper, cites 1,287 MWh for one GPT-3 training run. That is a historical paper figure tied to the original study’s assumptions. It is not a current estimate for every GPT-3-like run, nor a substitute for measuring your own hardware, duration and boundary.
The same overview reports approximately 70% of server power attributed to GPUs during DNN training. The page presents this as a reported study statistic, not a universal ratio for every server or training workload. Applying it to a new estimate without checking the server configuration can misstate CPU, memory and infrastructure energy.
The overview also describes potential energy reductions of up to 30% with little throughput loss for the Perseus approach on large-model training. That is a study result for the evaluated setup, not a promise for arbitrary workloads. Treat it as optimization evidence and reproduce the relevant conditions before using the figure in a planning document.
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When selecting instrumentation, evaluate each option against these questions:
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- Coverage: Does it measure GPU, CPU, memory, whole node or facility loads?
- Temporal resolution: Can it isolate the workload window, or does it report a coarse host interval?
- Hardware and framework support: Does it recognize the actual devices and software stack?
- Synchronization: Does it wait for asynchronous GPU work before stopping?
- Price source: Can you provide the applicable electricity tariff and preserve the rate used?
- Idle and overhead treatment: Are idle draw, cooling, conversion losses and shared infrastructure included or excluded?
- Repeatability: Can it export timestamps, device IDs, workload counts and repeated measurements?
The cited Zeus materials document GPU measurement with optional CPU measurement and electricity-cost monitoring. Whole-server and facility boundaries require separately documented instrumentation; do not infer them from a GPU telemetry value.
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Choose an optimization target instead of chasing one “best” setting
Energy and performance objectives can conflict. A power-limit or batch-size optimizer can target minimum energy, minimum energy subject to a permitted slowdown, or a combined time-and-energy cost. Select the objective that reflects the workload:
- Deadline-bound training: constrain slowdown first, then minimize energy within that limit.
- Cost-sensitive batch processing: optimize the product of runtime and applicable electricity price, while checking queue capacity.
- Interactive inference: preserve the latency target and evaluate energy per request at the required concurrency.
- Capacity planning: compare total kWh, runtime and peak power because peak demand can affect infrastructure even when the energy total is similar.
Measure the baseline and the optimized configuration with the same synchronization, workload window and boundary. A lower reported value caused by a shorter measurement window is not an optimization.
Cloud workloads require explicit allocation
For a cloud GPU job, request or collect provider telemetry that identifies the assigned devices and the job interval. Record whether the provider’s value is device energy, host energy or an allocated share of a larger system. If no energy telemetry is available, an estimate based on rated or observed power can still be useful, but label the wattage as measured, averaged or nameplate/estimated and include the uncertainty.
Keep electricity cost separate from the provider’s compute price unless the provider explicitly combines them. A cloud GPU rate may include hardware, operations and margin rather than a pass-through electricity tariff. Report both quantities when possible: measured or estimated kWh, and the billed compute amount.
Report the result so another person can reproduce it
A concise report should contain:
- Workload identity: model, task, software version, precision and dataset or request definition.
- Hardware: GPU model and count, CPU and node information.
- Window: start and end events, elapsed time, steps, samples, tokens or requests.
- Boundary: GPU-only, GPU plus CPU, node or facility, including any allocation rule.
- Synchronization: the framework operation used before and after the measured work.
- Measurement: integrated joules or average watts, telemetry source, device IDs and repetitions.
- Conversion: the equation used to obtain kWh.
- Price: tariff, currency, geography, date and marginal or blended status.
- Exclusions: cooling, conversion losses, idle periods, storage, networking, cloud billing and other omitted components.
- Uncertainty: run-to-run spread, estimated inputs and any unmeasured overhead.
For a per-request or per-token figure, publish the denominator and its definition. “0.02 kWh per request” is incomplete unless the request’s modality, input, output, batching, concurrency and hardware are stated.
Troubleshoot common estimation failures
The number is implausibly low
Check that the measurement stopped after GPU synchronization, that the correct device indices were selected, and that integrated energy—not a momentary power sample—was recorded. Verify that the workload actually executed during the window and that a warm-up or queued kernel was not omitted.
The number changes between identical runs
Look for background jobs, changing request lengths, dynamic batching, thermal throttling, clock changes and different warm-up treatment. Fix the workload shape, isolate devices, record environmental conditions and report repeated measurements rather than a single run.
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Confirm that the kWh figure is being compared with the electricity-energy line rather than the full cloud charge. Recheck tariff geography, time-of-use period, taxes, demand charges, minimums and whether the meter includes shared infrastructure.
Two teams report incompatible results
Align the boundary, hardware, model, task, modality, token counts, batch or concurrency, runtime window and synchronization method before comparing totals. If those fields differ, present the results as separate measurements instead of forcing a ranking.
Practical decision rule
For a defensible AI energy-and-cost estimate, measure the narrowest workload boundary that answers your question, synchronize asynchronous device work, convert the integrated reading to kWh, apply a local and dated electricity rate, and publish every assumption that could change the result. Treat benchmark and historical figures as configuration-specific evidence—not universal per-query or per-model constants.
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