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Estimate an AI workload’s footprint by measuring its total electricity use, adding data-centre overhead with PUE, and multiplying the result by a time- and location-specific grid emissions factor. For training, start with accelerator power, runtime and utilization, then add CPUs, memory, networking and storage. For inference, use requests or tokens as the functional unit and account for prefill, decoding, batching and sequence length. Report the model, hardware, region, dates, factors, boundary and uncertainty with every result; a GPU TDP-only number is not a defensible total footprint.
What exactly should you measure?
Start by defining the service you are assessing and the boundary around it. The International Telecommunication Union’s ITU-T L.1801 guidance says a comparison should specify the function, quantity, duration and quality of the service. Without those details, “grams of CO2e per query” can compare unlike workloads.
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- Training: kWh per complete training run, or gCO2e per million training examples at a stated quality target.
- Inference: gCO2e per request, per conversation turn, or per 1,000 input and output tokens.
- Batch processing: kWh per batch and, separately, emissions per item processed.
- Service operations: emissions per month, with request volume and uptime recorded so the denominator is auditable.
Keep quality in the definition. A model that produces longer answers, uses a larger context window or meets a stricter accuracy target is delivering a different service from a smaller, shorter-output model.
Set the accounting boundary
An operational-only result covers electricity consumed while the workload runs and the facility overhead associated with that electricity. A fuller footprint can also include embodied emissions from manufacturing hardware, transport, storage, networking, water and end-of-life treatment. State the boundary in the headline of your spreadsheet or dashboard; otherwise readers may mistake an operational estimate for a life-cycle assessment.
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Which activity data do you need?
Use direct telemetry whenever possible. GPU and CPU power readings, scheduler logs, cloud usage exports and power-distribution measurements are stronger evidence than a machine’s nameplate rating. Record the source and time window for every value.
Training data checklist
- Accelerator model and number of devices.
- Start and stop timestamps, including restarts, evaluation passes and checkpointing.
- Average or time-series power and utilization for each node.
- CPU, memory, networking and storage energy, or a documented method for allocating shared infrastructure.
- Datacentre or cloud region and its PUE value and effective date.
- Electricity emissions factor, including whether it is location-based, market-based or marginal.
Inference data checklist
- Number of requests and successful versus failed requests.
- Input and output token counts, context length and any retrieved documents.
- Model version, hardware type, batch size and throughput.
- Time spent in prefill and decode phases, if the serving stack exposes them.
- Idle capacity and replicas kept warm for availability.
- Region, PUE and grid factor for the serving time period.
Cloud billing alone rarely supplies all of this information. A bill can identify instance-hours, but it does not by itself reveal utilization, non-GPU components or the carbon factor that applied when the electricity was consumed.
How do you calculate training energy?
The practical starting equation quoted by the International Telecommunication Union in Measuring what matters: How to assess AI’s environmental impact (2025) is:
Energy (kWh) = GPU power draw (kW) × training time (h) × utilization rate
Use measured average draw if you have it. If you only have a rated value, label the result as an estimate and show the assumption. GPU TDP is not the whole system: add CPUs, memory, networking, storage and the energy of shared nodes.
Illustrative training calculation
Suppose eight H100 accelerators are assigned to a 36-hour run. The H100 rating in the cited Lawrence Berkeley National Laboratory Energy Technologies Area study is 10.2 kW at the system level, and intensive workloads averaged 76% of that rating. Treating that 76% as an assumed average draw for this illustration gives:
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10.2 kW × 0.76 = 7.752 kW. - GPU energy:
7.752 kW × 8 × 36 h = 2,232.6 kWh. - Add a separately measured or explicitly assumed amount for CPUs, memory, networking and storage. If you use a hypothetical 20% system addition, IT energy becomes
2,232.6 × 1.20 = 2,679.1 kWh.
The 20% addition above is only a scenario assumption, not a measured industry constant. Replace it with node-level telemetry or a documented allocation. If your power reading already covers the complete node, do not add the components a second time.
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Why a TDP-only estimate can be misleading
The 2025 Lawrence Berkeley National Laboratory Energy Technologies Area study reported 11.4% mean absolute percentage error for its architecture-specific H100 model, compared with 27–37% error for approaches based on TDP. The result supports using empirical measurements or an architecture-specific model, not treating a rating as actual draw. It does not make the 76% observation universal: workload mix, power caps, communication and utilization can all change the average.
How does PUE change the result?
Power usage effectiveness (PUE) is facility energy divided by IT-equipment energy. Multiply your IT electricity by PUE to include cooling, power conversion, lighting and other datacentre overhead:
Total facility energy (kWh) = IT energy (kWh) × PUE
Use the provider’s value for the site and period when available, and preserve its date. Cloud Carbon Footprint documents example assumptions of AWS 1.135, GCP 1.1 and Azure 1.185; these are modelling assumptions, not universal current values for every region or workload.
| Illustrative IT energy | PUE assumption | Facility energy | What the cell means |
|---|---|---|---|
| 2,679.1 kWh | 1.10 | 2,947.0 kWh | IT estimate multiplied by a stated facility factor |
| 2,679.1 kWh | 1.135 | 3,041.8 kWh | Example assumption documented by Cloud Carbon Footprint |
| 2,679.1 kWh | 1.185 | 3,175.7 kWh | Example assumption documented by Cloud Carbon Footprint |
Do not apply PUE when your electricity meter already measures the entire facility. Conversely, omitting PUE from server-only telemetry understates the energy attributable to the workload.
How do you convert electricity into carbon emissions?
Multiply facility electricity by a grid emissions factor expressed in kilograms of CO2e per kilowatt-hour:
CO2e (kg) = total facility energy (kWh) × grid factor (kg CO2e/kWh)
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The factor must match the workload’s geography and time period. Disclose whether it is location-based (average electricity mix), market-based (contractual instruments and supplier claims) or marginal (the emissions associated with additional demand). Prefer a market-based factor when it is available and relevant to your accounting policy, but do not present it as interchangeable with a location-based result.
Continuing the training example
Using the illustrative 2,679.1 kWh of IT energy, a PUE of 1.10 and a hypothetical grid factor of 0.40 kg CO2e/kWh produces:
2,679.1 × 1.10 × 0.40 = 1,178.8 kg CO2e
The 0.40 factor is a placeholder for a clearly identified regional factor; replace it with the factor published for your location and timestamp. The calculation is not transferable to another region without changing that input.
How should you estimate inference per request or token?
Inference is better represented by requests or tokens than by elapsed server time alone. Prefill and decode have different computational patterns, and batching, sequence length, model architecture and utilization can materially change energy per response.
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Per-request method
- Measure or obtain an energy estimate for the serving hardware during the observation window.
- Record successful requests, input and output tokens, batching and throughput for the same window.
- Divide energy by requests (or by tokens) to obtain the operational intensity.
- Apply PUE and the grid factor for the serving region and time.
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. This is a study estimate, not a universal constant. A model, prompt length, hardware mix or utilization pattern outside that study can produce a different value.
Per-token method
When your service exposes token counts, keep input and output tokens separate. Apply a measured or published Wh-per-token factor to each category if available, then sum, apply PUE and multiply by grid intensity:
CO2e = [(input tokens × Wh/input token) + (output tokens × Wh/output token)] ÷ 1,000 × PUE × grid factor
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If only one factor is available, say whether it represents a blended input/output average. Keep model version, hardware, region, timestamp and all factors in the record so another analyst can reproduce the result.
Illustrative request calculation
For 100,000 requests, using the Microsoft Research median solely as an example, IT energy would be 100,000 × 0.34 Wh = 34 kWh. At PUE 1.10 and a hypothetical 0.40 kg CO2e/kWh factor, the result is 34 × 1.10 × 0.40 = 14.96 kg CO2e. Report the study estimate and your assumptions beside the number rather than implying every request consumes 0.34 Wh.
When should embodied emissions and other life-cycle stages be included?
Include manufacturing, transport, hardware replacement, storage, networking, water and end-of-life when your claim is about a full footprint, a product life cycle or a procurement comparison. If those data are unavailable, publish an operational-only result and identify the omissions.
The Green Software Foundation’s SCI for AI reference expresses the broader structure as:
SCI = (E × I + M) / R
- E: operational energy.
- I: carbon intensity.
- M: embodied emissions allocated to the service.
- R: the functional result, such as requests or tokens meeting a defined quality level.
Allocation is crucial. If a server supports several customers, assign embodied and operational impacts using a documented rule such as accelerator-hours, reserved capacity or measured utilization. Do not add an unallocated manufacturing total to a single model run.
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How do you show uncertainty instead of false precision?
Publish a low, central and high case or perform a sensitivity analysis. Vary the inputs that are least certain: power draw, utilization, non-GPU overhead, PUE, token volume and grid factor. Mark each input as measured, vendor-reported, modelled or assumed.
| Input | Low case | Central case | High case |
|---|---|---|---|
| Average accelerator draw | Measured lower interval | Measured mean | Measured upper interval or rated-value scenario |
| Non-GPU energy | Telemetry allocation | Best-supported allocation | Conservative allocation for missing components |
| PUE | Site value, efficient season | Annual site value | Documented provider assumption |
| Grid factor | Lower time-specific factor | Published period average | Higher marginal or regional factor |
ITU identifies indirect training estimates and omitted life-cycle stages as persistent limitations. A range does not repair missing data, but it shows readers how much the conclusion depends on assumptions.
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What is different about training and inference accounting?
| Dimension | Training | Inference |
|---|---|---|
| Best functional unit | Complete run, fine-tuning job or kWh per training example | Request, conversation turn or 1,000 tokens |
| Main activity data | Cluster energy across the run, accelerator count and runtime | Requests, input/output tokens, prefill, decode and batching |
| Allocation issue | Shared nodes, evaluation and checkpointing | Warm replicas, idle capacity and multi-tenant serving |
| Key variability | Utilization, communication and checkpoint frequency | Sequence length, model architecture and throughput |
| Typical reporting risk | Using TDP as if it were measured total energy | Treating one published Wh-per-query value as universal |
Comparisons are valid only when model, hardware, token volume, quality, boundary and denominator are aligned. A smaller model with longer outputs may not have lower emissions per completed task than a larger model with concise outputs.
How can you turn the method into a repeatable worksheet?
- Name the service: record model version, task, quality target and functional unit.
- Capture activity: export telemetry or scheduler data for the exact run or serving window.
- Calculate IT energy: integrate measured power over time; use the GPU-power formula only when its assumptions are documented.
- Add non-GPU loads: include CPUs, memory, networking and storage, or state the allocation rule.
- Apply PUE: enter the site value and effective date; use a documented assumption only when a site value is unavailable.
- Apply carbon intensity: select the geography, period and accounting method, then record the source and units.
- Add life-cycle items: allocate embodied hardware and other stages when the claim requires them.
- Run sensitivity cases: vary uncertain inputs and preserve low, central and high outputs.
- Publish the audit trail: retain raw logs, formulas, factors, timestamps and exclusions with the result.
For financial planning, you can also estimate electricity cost by multiplying facility kWh by the applicable tariff, demand charge allocation and currency period. Keep that monetary calculation separate from CO2e: a cheap kilowatt-hour is not necessarily a low-carbon kilowatt-hour.
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- Counting TDP as energy: TDP is a rating, not a measurement of average draw or total node consumption.
- Leaving out PUE: server telemetry excludes cooling and electrical overhead unless the meter is facility-wide.
- Using a national annual average without qualification: electricity mix changes by region and hour.
- Mixing operational and embodied figures: label the boundary so a life-cycle number is not compared with an electricity-only number.
- Dividing by the wrong denominator: include failed requests, retries or generated tokens according to the service definition.
- Ignoring idle replicas: reserved inference capacity consumes energy even when traffic is light; allocate it with a stated rule.
- Comparing unlike quality levels: token counts and accuracy targets belong in the functional-unit definition.
- Reporting a single precise number: show sensitivity when inputs are estimated or vendor-reported.
How should you compare two workloads or cloud regions?
Build a side-by-side record with identical fields before looking at the totals. Align the functional unit, training-versus-inference category, model size and architecture, input and output token lengths, hardware and utilization, PUE, grid-factor geography and accounting method, and inclusion of embodied hardware, storage, networking, water and end-of-life. If one side lacks a value, write “not stated” rather than silently substituting an average.
A lower operational result can reflect a cleaner grid, a more efficient site or a shorter output rather than a more efficient model. Present the drivers separately so a decision-maker can tell which change produced the reduction.
Frequently Asked Questions
Can I estimate an AI footprint from my cloud bill alone?
A bill can provide instance-hours or accelerator-hours, but it usually lacks utilization, non-GPU energy, PUE and the time-specific grid factor. Use the bill as an activity-data starting point and obtain telemetry or documented assumptions for the missing fields.
What should I do if my provider does not publish a site PUE?
Use a clearly labelled documented assumption, calculate a sensitivity range with more than one plausible PUE, and contact the provider for a region- and date-specific value. Do not present an industry example as the provider’s measured performance.
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No. Caching can reduce compute, but cache storage, lookup, networking and the remaining serving path still consume energy. Measure the cache-hit and cache-miss paths separately if they are material to your service.
How often should an AI footprint estimate be updated?
Recalculate when the model, hardware, serving region, batching policy or token mix changes, and refresh grid factors and PUE at the cadence of the underlying data. A monthly or quarterly review is useful for stable services, while fast-changing deployments need event-based updates.
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