Measuring the Carbon Intensity of AI in Cloud Instances
Open accessAbstract
The advent of cloud computing has provided people around the world with unprecedented access to computational power and enabled rapid growth in technologies such as machine learning, the computational demands of which incur a high energy cost and a commensurate carbon footprint. As a result, recent scholarship has called for better estimates of the greenhouse gas impact of AI: data scientists today do not have easy or reliable access to measurements of this information, which precludes development of actionable tactics. We argue that cloud providers presenting information about software carbon intensity to users is a fundamental stepping stone towards minimizing emissions.
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Examples of distributed computation include fully decentralized training and redirecting training processes depending on which cloud region has the lowest carbon intensity at a given time of day.
“Examples include fully decentralized training [59] or redirecting training processes depending on which cloud region has the lowest carbon intensity at a given time of day [30].”
From From Efficiency Gains to Rebound Effects: The Problem of Jevons' Paradox in AI's Polarized Environmental Debate (2025) · cites this paper as [30]
Training BERT (a 6 billion parameter LLM) would produce between 21 to 78 metric tons of CO2 to train to completion.
“A recent study estimated that the training of BERT (a 6 billion parameter LLM) would produce between 21 to 78 metric tons of CO2 to train to completion [14].”
From ChatGPT and large language models in academia: opportunities and challenges (2023) · cites this paper as [14]
Training a 6B transformer consumes ~103.5 MWh.
“The total energy consumption for training a transformer model with 6B parameters to completion is estimated to be around 103.5 MWh [33].”
From Harnessing the power of llms in practice: a survey on chatGPT and beyond (2024) · cites this paper as [33]
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LACS: Learning-Augmented Algorithms for Carbon-Aware Resource Scaling with Uncertain Demand
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EC-NAS: Energy Consumption Aware Tabular Benchmarks for Neural Architecture Search
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Green AI Quotient: Assessing Greenness of AI-based software and the way forward
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COBRA Frames: Contextual Reasoning about Effects and Harms of Offensive Statements
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Sustainability of Machine Learning Models: An Energy Consumption Centric Evaluation
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A view of the sustainable computing landscape
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Multi-Objective Optimization for Sparse Deep Multi-Task Learning
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Vessim: A Testbed for Carbon-Aware Applications and Systems
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The Environmental Cost of Engineering Machine Learning-Enabled Systems
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- Authors
- Dodge, J., Prewitt, T., Tachet des Combes, R., Odmark, E., Schwartz, R., Strubell, E., Luccioni, A. S., Smith, N. A., DeCario, N., & Buchanan, W.
- Affiliations
- Allen Institute for Artificial Intelligence (US)
- Microsoft (United States) (US)
- Microsoft Research, Canada
- Hebrew University of Jerusalem (IL)
- Carnegie Mellon University (US)
- Hugging Face (US)
- Venue
- 2022 ACM Conference on Fairness Accountability and Transparency
- Publisher
- ACM
- Published
- 2022-06-20
- Pages
- 1877-1894
- OpenAlex
- W4283157527
- Cited by
- 257
- References
- 17
- Citations by year
- 2026: 73 · 2025: 74 · 2024: 60 · 2023: 43 · 2022: 7
- FWCI
- 74.38
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- ACM Conference on Fairness, Accountability, and Transparency (FAccT) (publishedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University) (cc-by)
- Funders
- Allen Institute for Artificial Intelligence
- Microsoft
- Topics
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Green IT and Sustainability
- Physical Sciences › Computer Science › Hardware and Architecture › Energy Efficiency in Computing
- Physical Sciences › Computer Science › Information Systems › Cloud Computing and Resource Management
- Keywords
- carbon intensity, artificial intelligence, cloud computing, greenhouse gas emissions, energy consumption, machine learning, carbon footprint, cloud service providers, emission measurement
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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