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The most defensible estimate combines measured workload energy, data-centre overhead and a time-and-location-specific electricity carbon factor. For a basic operational result, calculate total system energy in kilowatt-hours, multiply by power usage effectiveness (PUE), then multiply by grid emissions intensity in kilograms of CO2e per kWh. Training and inference need different activity data and functional units, and GPU TDP alone is only a rough proxy.
Use a reproducible record of model, hardware, region, runtime, utilization, token volume, PUE, grid-factor method and uncertainty. If you claim a full footprint rather than operational electricity emissions, add embodied hardware, networking, storage, cooling, water and end-of-life impacts where credible data exists.
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What is the basic formula for an AI workload’s carbon footprint?
For an operational-only estimate, use:
CO2e (kg) = total system energy (kWh) × PUE × grid factor (kg CO2e/kWh)
Total system energy should cover the IT equipment serving the workload, not just the accelerator. Include CPUs, memory, networking and storage when your measurement or allocation method can capture them. PUE then accounts for facility energy such as cooling and power distribution. Finally, apply an emissions factor that matches the data-centre location, time period and accounting method.
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For a fuller software-carbon calculation, the Green Software Foundation’s SCI for AI reference expresses the result as:
SCI = (E × I + M) / R
Here, E is operational energy, I is carbon intensity, M is embodied emissions and R is the functional result. A result might be grams of CO2e per training run, per completed request or per 1,000 output tokens. State the denominator explicitly; changing it changes the apparent footprint.
How should I define the scope and functional unit?
Write down what the number represents before collecting data. ITU-T L.1801 calls for the function, quantity, duration and quality to be specified so that two measurements describe the same service.
Choose a unit that matches the decision
- Training: kWh or kg CO2e per completed training run, checkpoint or fine-tuning job.
- Inference: Wh or gCO2e per request, with model version and response quality recorded.
- Token accounting: gCO2e per 1,000 input tokens, output tokens or a clearly defined combined token total.
- Service operations: gCO2e per user task, document processed or image generated when that business function is stable and measurable.
Do not compare a short benchmark request with a production request that has a long prompt, a long answer and multiple retries. Record input and output token counts, sequence length, batching, latency target and quality or accuracy constraints.
Set the boundary
An operational boundary covers electricity consumed while the workload runs, plus facility overhead represented by PUE. A life-cycle boundary can also include manufacturing of servers and accelerators, transport, storage, networking, cooling and water, and end-of-life treatment. Label an operational result as operational-only; it is not a full life-cycle footprint.
What activity data should I collect?
Direct measurements are preferable to generic estimates. Keep the raw data and the allocation rule so another person can reproduce the result.
For training and fine-tuning
- Accelerator model and count.
- Node, rack or cluster identifier.
- Start and end timestamps, including restarts and checkpoint work.
- Accelerator power or node energy telemetry.
- Utilization, memory use and any periods of throttling or idle capacity.
- CPU, memory, network and storage energy, or a documented allocation for shared infrastructure.
- Training tokens, examples, epochs, sequence length and final quality target.
Scheduler logs and cloud usage exports can establish runtime and resource allocation, but they may not reveal actual power. Metered node or rack energy is stronger evidence. If several jobs share a node, allocate energy by a declared rule, such as measured job intervals or accelerator-time weighted by utilization.
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For inference
- Model name, exact version, architecture and precision.
- Hardware type and number of accelerators or CPUs.
- Requests completed, failed requests and retries.
- Input and output tokens for each request or a representative distribution.
- Prefill and decode time, batch size and throughput.
- Idle capacity and utilization during the measurement interval.
- Energy for serving infrastructure, including CPUs, memory and networking where available.
Inference energy is sensitive to sequence length and batching. A single average request can conceal large differences between a short prompt and a long context with a lengthy generated answer.
How do I estimate training energy when I lack a meter?
A practical approximation quoted by the International Telecommunication Union is:
Energy (kWh) = GPU power draw (kW) × training time (h) × utilization rate.
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Use the accelerator’s actual average draw when available. If you only have a rated value, identify it as a proxy and report the resulting uncertainty. GPU TDP is a ceiling or design rating, not necessarily the draw of your workload, and it excludes other IT equipment.
Expand the estimate to the whole system
- Estimate accelerator energy from measured average power, runtime and utilization.
- Add CPU, memory, networking and storage energy from telemetry or a documented allocation.
- Include all nodes and support services that run for the job, including data loading and checkpoint storage when they are inside your boundary.
- Sum the IT energy in kWh before applying PUE.
Do not multiply measured energy by utilization a second time. The utilization factor belongs in a power-times-time estimate when the power value represents a rated or nominal draw. If a meter already records the workload’s actual energy, use that measured value directly.
What does recent H100 evidence show?
A 2025 Lawrence Berkeley National Laboratory Energy Technologies Area study measured intensive workloads at 76% of the 10.2 kW TDP rating. Its architecture-specific empirical model reported 11.4% mean absolute percentage error, compared with 27–37% error for TDP-based approaches. These figures support using workload-specific telemetry or calibrated models rather than treating TDP as consumption. They are study results for the tested H100 environment, not a universal factor for every cluster.
How do I estimate inference energy per request or token?
Measure a representative request stream and divide energy by the same functional output. For token accounting:
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- Record input and output tokens for the measurement interval.
- Measure or estimate Wh consumed by the serving hardware and associated IT equipment.
- Divide Wh by input tokens, output tokens or a declared combined token denominator.
- Apply PUE and grid intensity to convert energy into facility-adjusted CO2e.
- Report model version, hardware, region, timestamp, batching and token distribution.
Separate prefill from decode when possible. Prefill processes the prompt; decode generates output. Their energy profiles can differ, and batching can improve throughput while changing per-request energy. Include rejected requests, retries and warm-up traffic if they are part of the service boundary.
Microsoft Research estimated a median of 0.34 Wh per query, with an interquartile range of 0.18–0.67 Wh, for frontier-scale models above 200 billion parameters on H100 nodes in a 2025 study. Treat this as a study estimate, not a constant for all models, prompts, hardware or regions.
What is PUE, and how do I apply it correctly?
Power usage effectiveness is:
PUE = total data-centre facility energy ÷ IT equipment energy
If your IT workload uses 1,000 kWh and the site PUE is 1.2, facility-adjusted energy is 1,200 kWh. Use the provider or site value for the relevant period rather than silently substituting an industry average. Record whether the value is annual, monthly, measured or an assumption.
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Which grid carbon factor should I use?
Multiply facility-adjusted kWh by a grid emissions factor in kg CO2e/kWh that matches the electricity accounting question. Carbon intensity varies by geography, hour, season and generation mix.
Location-based versus market-based accounting
- Location-based: uses the average emissions intensity of electricity in the physical grid where the workload runs.
- Market-based: uses contractual instruments or supplier-specific factors where those are recognized and documented.
- Marginal: estimates emissions from the generation responding to an additional unit of demand; it answers a different question from an average factor.
State the geography, period, units, source and method next to the factor. A provider’s renewable-energy claim does not automatically make every hour of consumption zero-carbon under a location-based calculation.
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How can I calculate a complete example?
The following is an illustrative calculation, not a measurement of a particular provider or model. Assume a training job’s measured IT energy is 2,400 kWh, the site PUE is 1.20 and the selected grid factor is 0.40 kg CO2e/kWh.
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- Operational emissions: 2,880 × 0.40 = 1,152 kg CO2e.
- If the run produced one final model, report 1,152 kg CO2e per completed run within the stated operational boundary.
If you instead report emissions per 1,000 output tokens, divide by the measured token total and multiply by 1,000. Do not change the denominator without changing the label.
How should I handle uncertainty and sensitivity?
Publish a range when any major input is estimated. At minimum, vary power, utilization, PUE, token count and grid factor. A simple low, central and high case is more honest than a precise-looking single number.
Classify every input
- Measured: from a meter or validated telemetry.
- Vendor-reported: supplied by a cloud or hardware provider.
- Modeled: calculated from an empirical model.
- Assumed: selected because direct data was unavailable.
Show which inputs dominate the result. For example, a large uncertainty in accelerator draw can matter more than a small difference between two nearby PUE values. Preserve the timestamp because grid factors, model versions and serving configurations change.
ITU identifies indirect training estimates and omitted life-cycle stages as persistent limitations. State what was excluded rather than implying that an operational estimate represents manufacturing or disposal impacts.
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Include embodied hardware emissions when the claim concerns a full footprint, product comparison or long-lived infrastructure decision. Allocate manufacturing impacts across the expected service life using a documented rule, such as accelerator-hours or delivered inference volume. Add transport, storage, networking and end-of-life only when the data and allocation method are credible.
Cooling and water can be material, especially at facilities with water-intensive cooling. They should be reported as separate environmental indicators or converted with a stated method; water withdrawal is not interchangeable with CO2e. Avoid adding unsupported lifecycle estimates simply to make an operational number appear comprehensive.
How do I compare two AI workloads fairly?
Align every comparison axis before interpreting the result.
| Axis | What must match or be disclosed |
|---|---|
| Functional unit | Per run, request, task or 1,000 tokens; include quality and duration. |
| Workload type | Training, fine-tuning, batch inference or interactive inference. |
| Model | Parameter scale, architecture, precision and software version. |
| Token workload | Input length, output length, sequence distribution and retries. |
| Hardware | Accelerator model, count, node design and utilization. |
| Facility | PUE value, site, measurement period and allocation boundary. |
| Electricity factor | Geography, timestamp and location-based, market-based or marginal method. |
| Lifecycle scope | Operational-only or includes embodied hardware, water, networking and end-of-life. |
| Uncertainty | Measured, modeled and assumed inputs, plus the range. |
A lower number is not automatically a better system if it delivers lower quality, shorter outputs or a different service. Keep quality requirements in the functional unit.
What implementation workflow works for a production team?
- Write the measurement specification. Define the service, denominator, quality target, boundary and reporting period.
- Instrument the workload. Capture accelerator and node energy, runtime, tokens, requests, batching and retries.
- Join infrastructure data. Attach the correct PUE and grid factor by region and time.
- Calculate and version the result. Store formulas, factors, model version, hardware, region and timestamps with the output.
- Run sensitivity cases. Produce low, central and high results and identify the largest drivers.
- Review changes. Recalculate after model upgrades, hardware migrations, context-window changes, batching changes or region changes.
A ledger or database record should preserve the raw kWh, PUE, grid factor, token counts and allocation method. This prevents a dashboard from hiding assumptions behind a single headline number.
What common mistakes make AI footprint estimates unreliable?
- Using GPU TDP as measured energy: TDP omits utilization and non-GPU equipment.
- Ignoring PUE: IT electricity is not the same as total facility electricity.
- Using a generic grid average: geography and time can materially change emissions.
- Mixing denominators: per request and per 1,000 tokens are not interchangeable.
- Counting only successful requests: retries, failures and warm-up traffic may consume energy too.
- Double-counting utilization: do not multiply already measured energy by utilization again.
- Comparing unlike services: model size, quality, context and output length must be aligned.
- Presenting assumptions as facts: label vendor values, models and proxies.
- Calling operational energy a full footprint: disclose omitted manufacturing and end-of-life stages.
What should a publishable result look like?
Report the result in one sentence that carries its qualifications: “This workload produced X gCO2e per 1,000 output tokens, using measured IT energy, site PUE Y, a location-based grid factor of Z kg CO2e/kWh for region and month, with embodied hardware excluded.” Add a table of inputs and a range when uncertainty is material.
For internal decisions, retain the underlying run-level data. Aggregate only after checking that model version, hardware, region and functional unit remain comparable.
Frequently Asked Questions
Can I estimate emissions from cloud GPU hours alone?
GPU-hours can provide a rough proxy, but they do not reveal actual power, utilization, CPU and memory energy, PUE or the region’s grid factor. Treat the result as an assumption-based range and replace it with telemetry or provider energy data when available.
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Should input and output tokens have separate carbon rates?
Yes, when your measurements show different prefill and decode energy or when prompt and completion lengths vary widely. Report separate rates or disclose a weighted combined rate, and keep the token definition consistent across workloads.
How often should an AI footprint estimate be updated?
Update it when the model, precision, serving hardware, batching policy, region, electricity factor or PUE period changes. A scheduled review is also useful because grid intensity and request distributions can shift even when the software stays constant.
Does renewable electricity make an AI request carbon-free?
Not necessarily. A market-based accounting method may assign a lower factor when qualifying contractual instruments apply, while location-based accounting still reflects the physical grid. Publish both methods when stakeholders need to understand those different claims.
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