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.
- 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]]
- AI, Climate, and Regulation: From Data Centers to the AI Act (Erbert et al. 2024 [Paper]
- LLMCO2: Advancing Accurate Carbon Footprint Prediction for LLM Inferences (Fu et al. 2024) [Paper]
- Addition is all you need for energy-efficient language models (Luo et al. 2024) [Paper]
- 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]
- 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]