LLM Inference Carbon Intensity Calculator

Estimate carbon emissions intensity for LLM inference by combining workload throughput with facility efficiency and grid factors.

Sum of prompt and completion tokens served per request.
Average sustained request volume across the hour.
Combined IT load for GPUs, CPUs, and networking delivering the workload.
Blended marginal emissions factor for the facility location.
Defaults to 1.2 when left blank to reflect modern data center efficiency.
Defaults to 0%. Values above 1 are treated as percentages of delivered energy.

Informational model; validate with audited energy and emissions inventories before public reporting.

Examples

  • 1,500 tokens, 900 requests/hour, 45 kW, 0.38 kg/kWh, default PUE 1.2, 20% renewables ⇒ 0.0122 kg CO2e (12.16 g) per 1,000 tokens
  • 800 tokens, 1,200 requests/hour, 30 kW, 0.50 kg/kWh, PUE 1.3, 0% renewables ⇒ 0.0203 kg CO2e (20.31 g) per 1,000 tokens

FAQ

What if my workload varies throughout the day?

Use the average requests per hour and power draw across the reporting window, or run the calculator per shift and average the intensities weighted by token volume.

How should I source grid carbon intensity values?

Pull marginal emissions factors from your utility, ISO, or government datasets; if only annual averages are available, note the assumption in sustainability disclosures.

Can I model offsets or renewable energy certificates?

Represent contracted renewable supply as the optional percentage so the output reflects the net emissions after those procurements.

Does the power draw include networking and storage gear?

Yes. Use metered IT load at the rack or row level so that all equipment required to serve the tokens is represented.

Additional Information

  • Result expresses kilograms of CO2e per 1,000 generated tokens with a grams conversion in parentheses.
  • PUE defaults to 1.2 to reflect contemporary hyperscale facilities when no value is provided.
  • Renewable share reduces the grid carbon intensity proportionally, capped at 95% to avoid negative emissions.

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