A curated overview of resources for reducing the environmental footprint of AI development and usage.

Contributions and pull requests are welcome!

• AIPowerMeter [Website] [Source code]
• CarbonAI [Source code]
• carbontracker [Source code] [Paper]
• CodeCarbon [Website] [Source code] [Paper]
• Eco2AI [Source code] [Paper]
• EcoLogits [Source code] [Documentation]
• EnergyMeter [Source code] [Paper]
• experiment-impact-tracker [Source code] [Paper]
• powermeter [Source code]
• pyJoules [Source code]
• tracarbon [Source code]
• zeus [Website] [Source code] [Paper]

The following tools are designed to calculate the footprint based on information about the choice of algorithms, configuration and hardware.

• Green Algorithms [Website] [Paper]
• ML CO2 Impact [Website] [Paper]

• d2m [Website] [Source code] – a machine learning pipeline for ML model development with automatic monitoring and tracking of the carbon footprint

Particularly important papers are highlighted.

• Energy and Policy Considerations for Deep Learning in NLP (Strubell et al. 2019) [Paper]
• Quantifying the Carbon Emissions of Machine Learning (Lacoste et al. 2019) [Paper]
• Green AI (Schwartz et al. 2020) [Paper] [Notes]
• Carbontracker: Tracking and Predicting the Carbon Footprint of Training Deep Learning Models (Anthony et al. 2020) [Paper]
• Carbon Emissions and Large Neural Network Training (Patterson, et al. 2021) [Paper]
• Chasing Carbon: The Elusive Environmental Footprint of Computing (Gupta et al. 2020) [Paper]
• Green Algorithms: Quantifying the Carbon Footprint of Computation (Lannelongue et al. 2021) [Paper]
• A Pratical Guide to Quantifying Carbon Emissions for Machine Learning researchers and practitioners (Ligozat et al. 2021) [Paper]
• A framework for energy and carbon footprint analysis of distributed and federated edge learning (Savazzi et al. 2021) [Paper] [Notes]
• Aligning artificial intelligence with climate change mitigation (Kaack et al. 2021) [Paper]
• New universal sustainability metrics to assess edge intelligence (Lenherr et al. 2021) [Paper]
• Unraveling the Hidden Environmental Impacts of AI Solutions for Environment Life Cycle Assessment of AI Solutions (Ligozat et al. 2022) [Paper]
• Measuring the Carbon Intensity of AI in Cloud Instances (Dodge et al. 2022) [Paper]
• Estimating the Carbon Footprint of BLOOM a 176B Parameter Language Model (Luccioni et al. 2022) [Paper]
• Bridging Fairness and Environmental Sustainability in Natural Language Processing (Hessenthaler et al. 2022) [Paper]
• Eco2AI: carbon emissions tracking of machine learning models as the first step towards sustainable AI (Budennyy et al. 2022) [Paper]
• Environmental assessment of projects involving AI methods (Lefèvre et al. 2022) [Paper]
• Sustainable AI: Environmental Implications, Challenges and Opportunities (Wu et al. 2022) [Paper]
• A first look into the carbon footprint of federated learning (Qiu et al. 2022) [Paper] [Notes]
• The Carbon Footprint of Machine Learning Training Will Plateau, Then Shrink (Patterson et al. 2022) [Paper]
• Towards the Systematic Reporting of the Energy and Carbon Footprints of Machine Learning (Henderson et al. 2022) [Paper]
• Towards Sustainable Artificial Intelligence: An Overview of Environmental Protection Uses and Issues (Pachot et al. 2022) [Paper]
• Measuring the Environmental Impacts of Artificial Intelligence Compute and Applications (OECD 2022) [Paper]
• Method and evaluations of the effective gain of artificial intelligence models for reducing CO2 emissions (Delanoë et al. 2023) [Paper]
• Making AI Less "Thirsty": Uncovering and Addressing the Secret Water Footprint of AI Models (Li et al. 2023) [Paper]
• Zeus: Understanding and Optimizing GPU Energy Consumption of DNN Training (You et al. 2023) [Paper]
• Chasing Low-Carbon Electricity for Practical and Sustainable DNN Training (Yang et al. 2023) [Paper]
• LLMCarbon: Modeling the End-To-End Carbon Footprint of Large Language Models (Faiz et al. 2023) [Paper]
• Power Hungry Processing: Watts Driving the Cost of AI Deployment? (Luccioni et al. 2023) [Paper]
• A Synthesis of Green Architectural Tactics for ML-Enabled Systems (Järvenpää et al. 2023) [Paper]
• Toward Sustainable HPC: Carbon Footprint Estimation and Environmental Implications of HPC Systems (Li et al. 2023) [Paper]
• Exploring the Carbon Footprint of Hugging Face's ML Models: A Repository Mining Study (Castaño et al. 2023) [Paper]
• Estimating the environmental impact of Generative-AI services using an LCA-based methodology (Berthelot et al. 2023) [Paper]
• From Words to Watts: Benchmarking the Energy Costs of Large Language Model Inference (Samsi et al. 2023) [Paper]
• Power Hungry Processing: Watts Driving the Cost of AI Deployment? (Luccioni et al. 2023) [Paper]
• Perseus: Reducing Energy Bloat in Large Model Training (Chung et al. 2024) [Paper]
• Timeshifting strategies for carbon-efficient long-running large language model training (Jagannadharao et al. 2024) [Paper]
• Engineering Carbon Emissions Aware Machine Learning Pipelines (Husom et al. 2024) [Paper]
• Measuring and Improving the Energy Efficiency of Large Language Models Inference (Argerich et al. 2024) [Paper] [GitHub]
• Green AI: Exploring Carbon Footprints, Mitigation Strategies, and Trade Offs in Large Language Model Training (Liu et al. 2024) [Paper]
• A simplified machine learning product carbon footprint evaluation tool (Lang et al.) [Paper]
• Beyond Efficiency: Scaling AI Sustainably (Wu et al. 2024) [Paper]
• Towards Efficient Generative Large Language Model Serving: A Survey from Algorithms to Systems (Miao et al. 2024) [Paper]
• Towards Greener LLMs: Bringing Energy-Efficiency to the Forefront of LLM Inference (Stojkovic et al. 2024) [Paper]
• The Price of Prompting: Profiling Energy Use in Large Language Models Inference (Husom et al. 2024) [Paper]
• Hybrid Heterogeneous Clusters Can Lower the Energy Consumption of LLM Inference Workloads (Wilkins et al. 2024) [Paper]
• Offline Energy-Optimal LLM Serving: Workload-Based Energy Models for LLM Inference on Heterogeneous Systems (Wilkins et al. 2024) [Paper]

• Evaluating the carbon footprint of NLP methods: a survey and analysis of existing tools (Bannour et al. 2021) [Paper]
• A Survey on Green Deep Learning (Xu et al. 2021) [Paper] [Notes]
• A Systematic Review of Green AI (Verdecchia et al. 2023) [Paper]
• Counting Carbon: A Survey of Factors Influencing the Emissions of Machine Learning (Luccioni et al. 2023) [Paper]

• LLM-Perf Leaderboard
• ML.ENERGY Leaderboard

• Green Software Foundation – non-profit foundation promoting software development with sustainability as a core priority [Website]
• 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]

• Awesome Green AI by samuelrince