1. Our approach
At Appliance Labs, our job is to design and deploy private, local artificial intelligence solutions. We are not climate or life-cycle assessment specialists.
But we think it matters to try to understand the environmental impact of the technologies we deploy. So we started digging into the subject.
We studied the work published by Artificial Analysis, Hugging Face, ML.ENERGY, ADEME and other recognized organizations. We tried to relate the capabilities of AI models to their energy use, where measurements exist.
From that data, we built our own estimation method, with its assumptions, its extrapolations and, inevitably, its limits. We don’t claim to have all the answers.
The figures shown are neither a certified life-cycle assessment nor a real measurement of the cloud services you use. They let you explore different scenarios and better understand what drives the footprint of AI.
2. What we compare
Two ways of giving a team of up to 50 people the same amount of AI work for three years: AI services in the cloud, or a single Appliance installed on your premises.
A request is one complete piece of work handed to the AI. It may contain several model calls, reasoning and retries: all of it is already counted in its size.
| Use | Default share | Size of a request | Open reference model | Benchmark conditions |
|---|---|---|---|---|
| AI assistantA question, a summary, a text to write or translate. | 70% | 3,000 tokens read, 1,000 tokens generated | Qwen3-32B ↗ | lm-arena-chat 1 × NVIDIA B200, BF16 |
| AI workflowA long document to analyze, a synthesis or a complete deliverable. | 20% | 15,000 tokens read, 5,000 tokens generated | Qwen3-235B-A22B-Instruct-2507 ↗ | lm-arena-chat 4 × NVIDIA B200, BF16 |
| AI agentsA long multi-step task, with search and tools. | 10% | 150,000 tokens read, 30,000 tokens generated | DeepSeek-R1-0528 ↗ | GPQA 8 × NVIDIA B200, FP8 |
The split is a split of requests, not of people. Pairing a kind of use with a model is illustrative: GPQA is not a real agent run, and lm-arena-chat is not your daily work. No equivalence with a proprietary model is validated. Request sizes are assumptions, not observed usage.
What is counted, and what is not
Cloud AI
Counted
- Electricity of the servers and of the data center
Unknown, absent from the totals
- Manufacturing of the servers
- Tools called by the AI
- Training of the models
One Appliance
Counted
- An initial footprint: manufacturing, transportation, end of life
- The electricity used every year
Unknown, absent from the totals
- Accessories and installation
An unknown item is not zero. The result therefore compares subtotals: it is not a certified emissions reduction.
3. How we calculate
1. The volume of requests
Number of people × requests per day × 220 working days a year. Yearly growth applies from the second year. Requests are then split across the three kinds of use.
2. Energy on the cloud side
We multiply the generated text by the energy ML.ENERGY measured for the open reference model. That energy already covers every GPU and request batching: we do not multiply it a second time. We add 15% for the other server components, then a factor of 1.20 for the data center. Applying a benchmark to our kinds of use is an extrapolation, not a measurement.
3. Emissions on the cloud side
The resulting electricity is multiplied by the carbon intensity chosen for the cloud’s power grid. It is an assumption: we do not know your provider’s.
4. The Appliance
310 kg CO₂e counted from the start, not spread over three years: 244.6 for manufacturing, 58.1 for transportation and 7.3 for end of life, counted upfront and so never added a second time. Then electricity: 27 W idle and 140 W for 8 hours a day, or 566.48 kWh a year, multiplied by 0.029 kg CO₂e/kWh. This budget is fixed: it does not depend on the number of requests.
5. The comparison
We add up both sides year after year. The crossover point is when the cloud total reaches the Appliance’s. It may not exist: with low-carbon cloud electricity, the cloud stays below.
The sources used in the calculation
| Source | What it provides | What it is not |
|---|---|---|
| ML.ENERGY Leaderboard ↗ | The only source of energy figures: measurements published on three open models. | It is neither a measurement of a proprietary model nor a replication by us. |
| RTE, Bilan électrique 2025 ↗ | Carbon intensity of the Appliance’s electricity: French 2025 production, life cycle. | It is not the exact factor of delivered electricity, imports and losses included. |
| Official manufacturer data | Initial footprint of the Appliance: 310 kg CO₂e, from the manufacturer’s official carbon footprint, which covers the whole life cycle (manufacturing, transportation, use, end of life). Idle power: 27 W, measured in the laboratory under the European ecodesign regulation. Documents consulted on October 13, 2026. | These are not measurements made by Appliance Labs. Installation and accessories are not included, and active power (140 W) remains our assumption. |
ML.ENERGY and RTE consulted on October 11, 2026, manufacturer data on October 13, 2026. ML.ENERGY’s measurements are cited and linked to their source. We do not republish their data tables.
The reference scenario
50 people, 20 requests per day, a 70 / 20 / 10 split, flat usage, one Appliance, over three years. The calculator opens on carbon-intensive electricity; all three assumptions are below.
| Cloud electricity | Accounted cloud | Accounted Appliance | Difference (cloud − Appliance) | Crossover point |
|---|---|---|---|---|
| Low-carbon (0.029 kg CO₂e/kWh) | 142 kg CO₂e | 359 kg CO₂e | -217 kg CO₂e | None |
| Intermediate (0.200 kg CO₂e/kWh) | 980 kg CO₂e | 359 kg CO₂e | 620 kg CO₂e | about 12 months |
| Carbon-intensive (0.400 kg CO₂e/kWh) | 1,959 kg CO₂e | 359 kg CO₂e | 1,600 kg CO₂e | about 6 months |
These figures do not demonstrate an emissions reduction and describe no commercial service. The variants do not rank providers, and their range is not a confidence interval.
4. Our assumptions and limits
Every value carries its status. Two of them, cloud electricity and the initial footprint, can be changed in the calculator’s advanced settings.
- Manufacturer dataAppliance initial footprint: 310 kg CO₂e
Manufacturing, transportation and end of life. Value set by Appliance Labs from the official carbon footprint published by the manufacturer, which covers the device’s whole life cycle. Editable in the advanced settings.
- AssumptionActive power: 140 W
8 hours a day, 365 days a year. Our working assumption, to be confirmed by measurements on deployed Appliances.
- Manufacturer dataIdle power: 27 W
Measured in the laboratory by the manufacturer, under the European ecodesign regulation. Rounded value.
- Published by RTEAppliance electricity: 0.029 kg CO₂e/kWh
French 2025 production, life cycle, published by RTE. A proxy, not the exact factor of the electricity delivered.
- AssumptionCloud electricity: 0.029, 0.200 or 0.400 kg CO₂e/kWh
Three assumptions to choose from in the advanced settings; the calculator opens on the most carbon-intensive one. None is the value of a provider.
- AssumptionData center overhead (PUE): 1.20
Calculation assumption, not the value of a provider.
- AssumptionOther server components: 15% of the GPU energy
Calculation assumption.
- AssumptionVolumes: 220 days
Working days per year, over 3 years. The size of a request in each kind of use is an assumption, not observed usage.
Measured, declared, assumed: four levels of evidence
- Published third-party measurement
- The energy of the three open models, measured by ML.ENERGY. The emission factor of French electricity, published by RTE.
- Official manufacturer data
- Idle power (27 W), measured in the laboratory under European regulation. The product’s carbon footprint, calculated by the manufacturer over the whole life cycle and published with its range.
- Assumption
- Volumes, the size of requests, PUE, the share of other components, cloud electricity, the 140 W active power, and the choice of 310 kg CO₂e within the manufacturer’s range.
- Measured by Appliance Labs
- Not yet. Measurements on deployed Appliances are planned.
What the method cannot do
- The Appliance’s capacity is not verified. The reference scenario asks it to generate about 163 tokens per second on average over eight working hours, with no margin for peaks. Several devices might be needed, which would increase the local footprint accordingly.
- Pairing our three kinds of use with the three reference models is illustrative. The effect of long inputs is not modeled.
- The initial footprint comes from a manufacturer estimate, published with its range: our scenarios run from 198 to 705 kg CO₂e, and we use 310 kg CO₂e. The exact configuration assessed is not specified.
- The 140 W active power is our assumption, to be confirmed by measurements under real load. The Appliance’s fixed electricity budget is not a load law.
- Installation and the Appliance’s accessories are not covered yet.
- The emission factors of the cloud and of the Appliance do not yet rest on harmonized boundaries.
- The variants are scenarios, not probabilities. Some put the cloud below the Appliance, others above.
- A renewable electricity contract does not automatically bring physical consumption down to zero emissions.
- The method has not yet been through an independent review.
5. A method that keeps improving
As we deploy our appliances, we will try to check our estimates against field data, notably with CodeCarbon. We are also studying EcoLogits to enrich our method.
To date, these tools have produced no field measurement: no figure on this page comes from a measurement made by Appliance Labs.
We will share our results as they come, including when they are not favorable to Appliance Labs.
And if you work on these topics, we would be glad to talk. A better source, an error you spot or constructive criticism will help us improve this approach.
The work we are studying for the next versions
| Work | What we look for in it | Where we stand |
|---|---|---|
| Artificial Analysis ↗ | Compare the capabilities of models, to relate comparable services. | Research in progress. No score enters the calculation, no equivalence is validated. |
| Hugging Face AI Energy Score ↗ | Study the energy use of open models, under known measurement conditions. | Research in progress. No coefficient used; stars are not watt-hours. |
| CodeCarbon ↗ | Check our estimates against field data, on deployed Appliances. | Not deployed yet. No field measurement to date. |
| EcoLogits ↗ | Enrich the method with another estimate of cloud services. | Under study. Tool not run; its results are not added to ours. |
| ADEME ↗ | Better estimate manufacturing, from the life-cycle assessment of AI GPUs. | Under study. Its value is not transferred to the Appliance in the current calculation. |
What changed
Version 0.2.0 · October 13, 2026Current version
- The Appliance’s initial footprint now rests on official manufacturer data: 310 kg CO₂e, instead of an unsourced provision of 255 kg CO₂e.
- Idle power goes from 38 W to 27 W, measured in the laboratory by the manufacturer.
- The device’s end of life is now counted, inside the initial footprint. It is no longer among the unknown items.
- As a result, in the reference scenario the Appliance goes from 310 to 359 kg CO₂e over three years, and the carbon crossover point comes later: about 6 months instead of 5 with carbon-intensive electricity.
Version 0.1.0 · October 11, 2026
- First published version: three kinds of use, ML.ENERGY energy landmarks, and unsourced provisions for the Appliance.
Estimation method v0.2.0 · updated on October 13, 2026