{"id":61794,"url":"https://github.com/ejhusom/green-ai","name":"green-ai","description":"A curated list of Green AI resources.","projects_count":75,"last_synced_at":"2026-10-25T08:00:23.226Z","repository":{"id":209848922,"uuid":"725077732","full_name":"ejhusom/green-ai","owner":"ejhusom","description":"A curated list of Green AI 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projects and foundations","Papers","Leaderboards","Tools","Other resources"],"sub_categories":["Survey papers","Tools for AI/ML development with integrated carbon footprint reporting","Tools for measuring and quantifying footprint","Tools for calculation/estimation of footprint"],"readme":"# Green AI 🌱\n\nA curated overview of resources for reducing the environmental footprint of AI development and usage.\n\nContributions and pull requests are welcome!\n\n\n## Tools\n\n### Tools for measuring and quantifying footprint\n\n- AIPowerMeter [[Website]](https://greenai-uppa.github.io/AIPowerMeter/) [[Source code]](https://github.com/GreenAI-Uppa/AIPowerMeter)\n- CarbonAI [[Source code]](https://github.com/Capgemini-Invent-France/CarbonAI)\n- carbontracker [[Source code]](https://github.com/lfwa/carbontracker) [[Paper]](https://arxiv.org/pdf/2007.03051.pdf)\n- CodeCarbon [[Website]](https://codecarbon.io/) [[Source code]](https://github.com/mlco2/codecarbon) [[Paper]](https://arxiv.org/pdf/1911.08354.pdf)\n- Eco2AI [[Source code]](https://github.com/sb-ai-lab/Eco2AI) [[Paper]](https://arxiv.org/pdf/2208.00406.pdf)\n- EcoLogits [[Source code]](https://github.com/genai-impact/ecologits) [[Documentation]](https://ecologits.ai/latest/)\n- EnergyMeter [[Source code]](https://github.com/maufadel/EnergyMeter) [[Paper]](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10549890)\n- experiment-impact-tracker [[Source code]](https://github.com/Breakend/experiment-impact-tracker) [[Paper]](https://arxiv.org/pdf/2002.05651.pdf)\n- powermeter [[Source code]](https://github.com/autoai-incubator/powermeter)\n- pyJoules [[Source code]](https://github.com/powerapi-ng/pyJoules)\n- tracarbon [[Source code]](https://github.com/fvaleye/tracarbon)\n- zeus [[Website]](https://ml.energy/zeus) [[Source code]](https://github.com/ml-energy/zeus) [[Paper]](https://www.usenix.org/system/files/nsdi23-you.pdf)\n\n### Tools for calculation/estimation of footprint\n\nThe following tools are designed to calculate the footprint based on information about the choice of algorithms, configuration and hardware.\n\n- Green Algorithms [[Website]](http://calculator.green-algorithms.org/) [[Paper]](https://onlinelibrary.wiley.com/doi/epdf/10.1002/advs.202100707)\n- ML CO2 Impact [[Website]](https://mlco2.github.io/impact/) [[Paper]](https://arxiv.org/pdf/1910.09700.pdf)\n\n### Tools for AI/ML development with integrated carbon footprint reporting\n\n- d2m [[Website]](https://sintef-9012.github.io/d2m/) [[Source code]](https://github.com/SINTEF-9012/d2m) – a machine learning pipeline for ML model development with automatic monitoring and tracking of the carbon footprint \n\n## Papers\n\nParticularly important papers are highlighted.\n\n- **Energy and Policy Considerations for Deep Learning in NLP** (Strubell et al. 2019) [[Paper]](https://arxiv.org/pdf/1906.02243.pdf)\n- Quantifying the Carbon Emissions of Machine Learning (Lacoste et al. 2019) [[Paper]](https://arxiv.org/pdf/1910.09700.pdf)\n- **Green AI** (Schwartz et al. 2020) [[Paper]](https://cacm.acm.org/magazines/2020/12/248800-green-ai/fulltext) [[Notes]](notes/schwartz2020.md)\n- Carbontracker: Tracking and Predicting the Carbon Footprint of Training Deep Learning Models (Anthony et al. 2020) [[Paper]](https://arxiv.org/pdf/2007.03051.pdf)\n- Carbon Emissions and Large Neural Network Training (Patterson, et al. 2021) [[Paper]](https://arxiv.org/ftp/arxiv/papers/2104/2104.10350.pdf)\n- Chasing Carbon: The Elusive Environmental Footprint of Computing (Gupta et al. 2020) [[Paper]](https://arxiv.org/pdf/2011.02839.pdf)\n- Green Algorithms: Quantifying the Carbon Footprint of Computation (Lannelongue et al. 2021) [[Paper]](https://onlinelibrary.wiley.com/doi/10.1002/advs.202100707)\n- A Pratical Guide to Quantifying Carbon Emissions for Machine Learning researchers and practitioners (Ligozat et al. 2021) [[Paper]](https://hal.archives-ouvertes.fr/hal-03376391/document)\n- A framework for energy and carbon footprint analysis of distributed and federated edge learning (Savazzi et al. 2021) [[Paper]](https://arxiv.org/pdf/2103.10346.pdf) [[Notes]](notes/savazzi2021.md)\n- **Aligning artificial intelligence with climate change mitigation** (Kaack et al. 2021) [[Paper]](https://hal.archives-ouvertes.fr/hal-03368037/document)\n- New universal sustainability metrics to assess edge intelligence (Lenherr et al. 2021) [[Paper]](https://www.sciencedirect.com/science/article/pii/S2210537921000718?via%3Dihub)\n- Unraveling the Hidden Environmental Impacts of AI Solutions for Environment Life Cycle Assessment of AI Solutions (Ligozat et al. 2022) [[Paper]](https://arxiv.org/pdf/2110.11822.pdf)\n- Measuring the Carbon Intensity of AI in Cloud Instances (Dodge et al. 2022) [[Paper]](https://arxiv.org/pdf/2206.05229.pdf)\n- Estimating the Carbon Footprint of BLOOM a 176B Parameter Language Model (Luccioni et al. 2022) [[Paper]](https://arxiv.org/pdf/2211.02001.pdf)\n- Bridging Fairness and Environmental Sustainability in Natural Language Processing (Hessenthaler et al. 2022) [[Paper]](https://arxiv.org/pdf/2211.04256.pdf)\n- Eco2AI: carbon emissions tracking of machine learning models as the first step towards sustainable AI (Budennyy et al. 2022) [[Paper]](https://arxiv.org/pdf/2208.00406.pdf)\n- Environmental assessment of projects involving AI methods (Lefèvre et al. 2022) [[Paper]](https://hal.science/hal-03922093v1/document)\n- Sustainable AI: Environmental Implications, Challenges and Opportunities (Wu et al. 2022) [[Paper]](https://arxiv.org/pdf/2111.00364.pdf)\n- A first look into the carbon footprint of federated learning (Qiu et al. 2022) [[Paper]](https://arxiv.org/pdf/2102.07627.pdf) [[Notes]](notes/qiu2022.md)\n- The Carbon Footprint of Machine Learning Training Will Plateau, Then Shrink (Patterson et al. 2022) [[Paper]](https://arxiv.org/ftp/arxiv/papers/2204/2204.05149.pdf)\n- Towards the Systematic Reporting of the Energy and Carbon Footprints of Machine Learning (Henderson et al. 2022) [[Paper]](https://arxiv.org/pdf/2002.05651.pdf)\n- Towards Sustainable Artificial Intelligence: An Overview of Environmental Protection Uses and Issues (Pachot et al. 2022) [[Paper]](https://arxiv.org/ftp/arxiv/papers/2212/2212.11738.pdf)\n- **Measuring the Environmental Impacts of Artificial Intelligence Compute and Applications** (OECD 2022) [[Paper]](https://www.oecd-ilibrary.org/docserver/7babf571-en.pdf?expires=1701262318\u0026id=id\u0026accname=guest\u0026checksum=FAB39144A63BB5953FF7D56D7C18B147)\n- Method and evaluations of the effective gain of artificial intelligence models for reducing CO2 emissions (Delanoë et al. 2023) [[Paper]](https://www.sciencedirect.com/science/article/pii/S030147972300049X)\n- Making AI Less \"Thirsty\": Uncovering and Addressing the Secret Water Footprint of AI Models (Li et al. 2023) [[Paper]](https://arxiv.org/pdf/2304.03271.pdf)\n- Zeus: Understanding and Optimizing GPU Energy Consumption of DNN Training (You et al. 2023) [[Paper]](https://www.usenix.org/conference/nsdi23/presentation/you)\n- Chasing Low-Carbon Electricity for Practical and Sustainable DNN Training (Yang et al. 2023) [[Paper]](https://www.climatechange.ai/papers/iclr2023/29)\n- LLMCarbon: Modeling the End-To-End Carbon Footprint of Large Language Models (Faiz et al. 2023) [[Paper]](https://arxiv.org/pdf/2309.14393.pdf)\n- Power Hungry Processing: Watts Driving the Cost of AI Deployment? (Luccioni et al. 2023) [[Paper]](https://arxiv.org/pdf/2311.16863.pdf)\n- A Synthesis of Green Architectural Tactics for ML-Enabled Systems (Järvenpää et al. 2023) [[Paper]](https://arxiv.org/pdf/2312.09610.pdf)\n- Toward Sustainable HPC: Carbon Footprint Estimation and Environmental Implications of HPC Systems (Li et al. 2023) [[Paper]](https://arxiv.org/abs/2306.13177)\n- Exploring the Carbon Footprint of Hugging Face's ML Models: A Repository Mining Study (Castaño et al. 2023) [[Paper]](https://ieeexplore.ieee.org/document/10304801)\n- Estimating the environmental impact of Generative-AI services using an LCA-based methodology (Berthelot et al. 2023) [[Paper]](https://hal.univ-lorraine.fr/INRIA/hal-04346102v2)\n- From Words to Watts: Benchmarking the Energy Costs of Large Language Model Inference (Samsi et al. 2023) [[Paper]](https://arxiv.org/abs/2310.03003)\n- **Power Hungry Processing: Watts Driving the Cost of AI Deployment?** (Luccioni et al. 2023) [[Paper](https://arxiv.org/pdf/2311.16863.pdf)]\n- Perseus: Reducing Energy Bloat in Large Model Training (Chung et al. 2024) [[Paper]](https://arxiv.org/abs/2312.06902)\n- Timeshifting strategies for carbon-efficient long-running large language model training (Jagannadharao et al. 2024) [[Paper]](https://link.springer.com/article/10.1007/s11334-023-00546-x)\n- Engineering Carbon Emissions Aware Machine Learning Pipelines (Husom et al. 2024) [[Paper](https://dl.acm.org/doi/pdf/10.1145/3644815.3644943)]\n- Measuring and Improving the Energy Efficiency of Large Language Models Inference (Argerich et al. 2024) [[Paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10549890)] [[GitHub](https://github.com/maufadel/EnergyMeter)]\n- Green AI: Exploring Carbon Footprints, Mitigation Strategies, and Trade Offs in Large Language Model Training (Liu et al. 2024) [[Paper]](https://arxiv.org/abs/2404.01157)\n- A simplified machine learning product carbon footprint evaluation tool (Lang et al.) [[Paper]](https://www.sciencedirect.com/science/article/pii/S2666789424000254)\n- Beyond Efficiency: Scaling AI Sustainably (Wu et al. 2024) [[Paper]](https://arxiv.org/abs/2406.05303v1)\n- Towards Efficient Generative Large Language Model Serving: A Survey from Algorithms to Systems (Miao et al. 2024) [[Paper]](https://arxiv.org/abs/2312.15234)\n- Towards Greener LLMs: Bringing Energy-Efficiency to the Forefront of LLM Inference (Stojkovic et al. 2024) [[Paper]](https://arxiv.org/abs/2403.20306)\n- The Price of Prompting: Profiling Energy Use in Large Language Models Inference (Husom et al. 2024) [[Paper]](https://arxiv.org/abs/2407.16893)]\n- Hybrid Heterogeneous Clusters Can Lower the Energy Consumption of LLM Inference Workloads (Wilkins et al. 2024) [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3632775.3662830)]\n- Offline Energy-Optimal LLM Serving: Workload-Based Energy Models for LLM Inference on Heterogeneous Systems (Wilkins et al. 2024) [[Paper]](https://arxiv.org/pdf/2407.04014)]\n- AI, Climate, and Regulation: From Data Centers to the AI Act (Erbert et al. 2024 [[Paper]](https://arxiv.org/pdf/2410.06681)\n- LLMCO2: Advancing Accurate Carbon Footprint Prediction for LLM Inferences (Fu et al. 2024) [[Paper]](https://arxiv.org/abs/2410.02950)\n- Addition is all you need for energy-efficient language models (Luo et al. 2024) [[Paper]](https://arxiv.org/pdf/2410.00907)\n- Artificial Intelligence in Climate Change Mitigation: A Review of Predictive Modeling and Data-Driven Solutions for Reducing Greenhouse Gas Emissions (Adegbite et al. 2024) [[Paper]](https://wjarr.com/sites/default/files/WJARR-2024-3043.pdf)\n\n\u003c!-- - How to shrink AI's ballooning carbon footprint (Gibney et al. 2022) [[Paper]]() --\u003e\n\n### Survey papers\n\n- Evaluating the carbon footprint of NLP methods: a survey and analysis of existing tools (Bannour et al. 2021) [[Paper]](https://aclanthology.org/2021.sustainlp-1.2.pdf)\n- **A Survey on Green Deep Learning** (Xu et al. 2021) [[Paper]](https://arxiv.org/pdf/2111.05193.pdf) [[Notes]](notes/xu2021.md)\n- **A Systematic Review of Green AI** (Verdecchia et al. 2023) [[Paper]](https://arxiv.org/pdf/2301.11047.pdf)\n- Counting Carbon: A Survey of Factors Influencing the Emissions of Machine Learning (Luccioni et al. 2023) [[Paper]](https://arxiv.org/pdf/2302.08476v1.pdf)\n\n\n\u003c!-- ### Green AI and Edge Computing --\u003e\n\n\u003c!-- - Energy efficient AI on[Shah 2020: Energy efficient AI on Edge]() --\u003e\n\u003c!-- - [Shah 2020: Energy efficient AI on Edge]() --\u003e\n\u003c!-- \t- Training AI models results in huge carbon emissions --\u003e\n\u003c!-- \t- Our approach for carbon efficient AI --\u003e\n\u003c!-- \t\t- Pruning --\u003e\n\u003c!-- \t\t- Quantization --\u003e\n\n## Leaderboards\n\n- [LLM-Perf Leaderboard](https://huggingface.co/spaces/optimum/llm-perf-leaderboard)\n- [ML.ENERGY Leaderboard](https://ml.energy/leaderboard/)\n\n## Organizations, projects and foundations\n\n- Green Software Foundation – non-profit foundation promoting software development with sustainability as a core priority [[Website]](https://greensoftware.foundation/)\n- ENFIELD: European Lighthouse to Manifest Trustworthy and Green AI – project for creating a European Centre of Excellence with Green AI as one of the pillars [[Website]](https://www.enfield-project.eu/)\n\n## Other resources\n\n- [Awesome Green AI](https://github.com/samuelrince/awesome-green-ai/tree/main) by [samuelrince](https://github.com/samuelrince)\n","projects_url":"https://awesome.ecosyste.ms/api/v1/lists/ejhusom%2Fgreen-ai/projects"}