awesome-green-ai
AI sustainability toolkit
A curated collection of resources and tools to help developers reduce the environmental impact of AI
A curated list of awesome Green AI resources and tools to assess and reduce the environmental impacts of using and deploying AI.
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Awesome Green AI 🤖🌱 / 🛠 Tools / Code-Based Tools | |||
| CodeCarbon | 1,204 | 11 months ago | – Track emissions from Compute and recommend ways to reduce their impact on the environment |
| carbontracker | 405 | 12 months ago | – Track and predict the energy consumption and carbon footprint of training deep learning models |
| Eco2AI | 237 | 12 months ago | – A python library which accumulates statistics about power consumption and CO2 emission during running code |
| Zeus | 224 | 11 months ago | – A framework for deep learning energy measurement and optimization |
| EcoLogits | 103 | 11 months ago | – Estimates the energy consumption and environmental footprint of LLM inference through APIs |
| Tracarbon | 98 | 11 months ago | – Tracks your device's energy consumption and calculates your carbon emissions using your location |
| AIPowerMeter | 22 | 12 months ago | – Easily monitor energy usage of machine learning programs |
| carbonai | 46 | about 3 years ago | – Python package to monitor the power consumption of any algorithm |
| experiment-impact-tracker | 277 | almost 2 years ago | – A simple drop-in method to track energy usage, carbon emissions, and compute utilization of your system |
| GATorch | 9 | over 2 years ago | – An Energy-Aware PyTorch Extension |
| GPU Meter | 1 | about 4 years ago | – Power Consumption Meter for NVIDIA GPUs |
| PyJoules | 75 | over 2 years ago | – A Python library to capture the energy consumption of code snippets |
Awesome Green AI 🤖🌱 / 🛠 Tools / Monitoring Tools | |||
| Scaphandre | 1,664 | about 1 year ago | – A metrology agent dedicated to electrical power consumption metrics |
| CodeCarbon | 1,204 | 11 months ago | – Track emissions from Compute and recommend ways to reduce their impact on the environment |
| PowerJoular | 71 | 11 months ago | – Monitor power consumption of multiple platforms and processes |
| ALUMET | 32 | 11 months ago | – A modular and efficient software measurement tool |
| cardamon | 23 | 12 months ago | – A tool for measuring the power consumption and carbon footprint of your software |
| Boagent | 23 | about 1 year ago | – Local API and monitoring agent focussed on environmental impacts of the host |
| Powerletrics | 13 | 11 months ago | – PowerLetrics is a framework designed to monitor and analyze power consumption metrics at the process level on Linux |
| vJoule | 10 | over 1 year ago | – A tool to estimate the energy consumption of your processes |
| jupyter-power-usage | 8 | over 1 year ago | – Jupyter extension to display CPU and GPU power usage and carbon emissions |
Awesome Green AI 🤖🌱 / 🛠 Tools / Optimization Tools | |||
| Zeus | 224 | 11 months ago | – A framework for deep learning energy measurement and optimization |
| GEOPM | 96 | 11 months ago | – A framework to enable efficient power management and performance optimizations |
Awesome Green AI 🤖🌱 / 🛠 Tools / Calculation Tools | |||
| Green Algorithms | A tool to easily estimate the carbon footprint of a project | ||
| ML CO2 Impact | Compute model emissions and add the results to your paper with our generated latex template | ||
| EcoLogits Calculator | Estimate energy consumption and environmental impacts of LLM inference | ||
| AI Carbon | Estimate your AI model's carbon footprint | ||
| MLCarbon | 32 | over 1 year ago | End-to-end carbon footprint modeling tool |
| GenAI Carbon Footprint | 1 | over 1 year ago | A tool to estimate energy use (kWh) and carbon emissions (gCO2eq) from LLM usage |
| Carbon footprint modeling tool | A data model and a viewer for carbon footprint scenarios | ||
| Boaviztapi | 75 | 11 months ago | Multi-criteria impacts of compute resources taking into account manufacturing and usage |
| Datavizta | Compute resources data explorer not limited to AI | ||
| EcoDiag | Compute carbon footprint of IT resources taking into account manufactuing and usage (🇫🇷 only) | ||
Awesome Green AI 🤖🌱 / 🛠 Tools / Leaderboards | |||
| LLM Perf Leaderboad | Benchmarking LLMs on performance and energy | ||
| ML.Energy Leaderboard | Energy consumption of GenAI models at inference | ||
| AI Energy Score Leaderboard | Energy efficiency ratings for AI models | ||
Awesome Green AI 🤖🌱 / 📚 Papers | |||
| Strubell et al. (2019) | Energy and Policy Considerations for Deep Learning in NLP - | ||
| Lacoste et al. (2019) | Quantifying the Carbon Emissions of Machine Learning - | ||
| Anthony et al. (2020) | Carbontracker: Tracking and Predicting the Carbon Footprint of Training Deep Learning Models - | ||
| Schwartz et al. (2020) | Green AI - | ||
| Parcollet et al. (2021) | The Energy and Carbon Footprint of Training End-to-End Speech Recognizers - | ||
| Patterson, et al. (2021) | Carbon Emissions and Large Neural Network Training - | ||
| Lannelongue et al. (2021) | Green Algorithms: Quantifying the Carbon Footprint of Computation - | ||
| Kaack et al. (2021) | Aligning artificial intelligence with climate change mitigation - | ||
| Ligozat et al. (2021) | A Practical Guide to Quantifying Carbon Emissions for Machine Learning researchers and practitioners - | ||
| Ligozat et al. (2022) | Unraveling the Hidden Environmental Impacts of AI Solutions for Environment Life Cycle Assessment of AI Solutions - | ||
| Dodge et al. (2022) | Measuring the Carbon Intensity of AI in Cloud Instances - | ||
| Luccioni et al. (2022) | Estimating the Carbon Footprint of BLOOM a 176B Parameter Language Model - | ||
| Hessenthaler et al. (2022) | Bridging Fairness and Environmental Sustainability in Natural Language Processing - | ||
| Budennyy et al. (2022) | Eco2AI: carbon emissions tracking of machine learning models as the first step towards sustainable AI - | ||
| Lefèvre et al. (2022) | Environmental assessment of projects involving AI methods - | ||
| Wu et al. (2022) | Sustainable AI: Environmental Implications, Challenges and Opportunities - | ||
| Patterson et al. (2022) | The Carbon Footprint of Machine Learning Training Will Plateau, Then Shrink - | ||
| Henderson et al. (2022) | Towards the Systematic Reporting of the Energy and Carbon Footprints of Machine Learning - | ||
| Pachot et al. (2022) | Towards Sustainable Artificial Intelligence: An Overview of Environmental Protection Uses and Issues - | ||
| Delanoë et al. (2023) | Method and evaluations of the effective gain of artificial intelligence models for reducing CO2 emissions - | ||
| Li et al. (2023) | Making AI Less "Thirsty": Uncovering and Addressing the Secret Water Footprint of AI Models - | ||
| You et al. (2023) | Zeus: Understanding and Optimizing GPU Energy Consumption of DNN Training - | ||
| Desislavov et al. (2023) | Trends in AI inference energy consumption: Beyond the performance-vs-parameter laws of deep learning | ||
| Yang et al. (2023) | Chasing Low-Carbon Electricity for Practical and Sustainable DNN Training - | ||
| Li et al. (2023) | Toward Sustainable HPC: Carbon Footprint Estimation and Environmental Implications of HPC Systems - | ||
| Chien et al. (2023) | Reducing the Carbon Impact of Generative AI Inference (today and in 2035) - | ||
| Faiz et al. (2023) | LLMCarbon: Modeling the End-To-End Carbon Footprint of Large Language Models - | ||
| De Vries (2023) | The growing energy footprint of artificial intelligence - | ||
| Castano et al. (2023) | Exploring the Carbon Footprint of Hugging Face's ML Models: A Repository Mining Study - | ||
| Lin et al. (2023) | Exploding AI Power Use: an Opportunity to Rethink Grid Planning and Management - | ||
| Luccioni et al. (2023) | Power Hungry Processing: Watts Driving the Cost of AI Deployment? - | ||
| Chung et al. (2023) | Perseus: Removing Energy Bloat from Large Model Training - | ||
| Jagannadharao et al. (2023) | Timeshifting strategies for carbon-efficient long-running large language model training - | ||
| Berthelot et al. (2024) | Estimating the environmental impact of Generative-AI services using an LCA-based methodology - | ||
| Stojkovic et al. (2024) | Towards Greener LLMs: Bringing Energy-Efficiency to the Forefront of LLM Inference - | ||
| Liu et al. (2024) | Green AI: Exploring Carbon Footprints, Mitigation Strategies, and Trade Offs in Large Language Model Training - | ||
| Humsom et al. (2024) | Engineering Carbon Emission-aware Machine Learning Pipelines - | ||
| Lang et al. (2024) | A simplified machine learning product carbon footprint evaluation tool - | ||
| Wu et al. (2024) | Beyond Efficiency: Scaling AI Sustainably - | ||
| Huson et al. (2024) | The Price of Prompting: Profiling Energy Use in Large Language Models Inference - | ||
| Morand et al. (2024) | MLCA: a tool for Machine Learning Life Cycle Assessment - | ||
| Varoquaux et al. (2024) | Hype, Sustainability, and the Price of the Bigger-is-Better Paradigm in AI - | ||
| Luo et al. (2024) | Addition is All You Need for Energy-efficient Language Models - | ||
| Wang et al. (2024) | E-waste challenges of generative artificial intelligence - | ||
Awesome Green AI 🤖🌱 / 📚 Papers / Survey Papers | |||
| Bannour et al.(2021) | Evaluating the carbon footprint of NLP methods: a survey and analysis of existing tools - | ||
| Xu et al. (2021) | A Survey on Green Deep Learning - | ||
| Verdecchia et al. (2023) | A Systematic Review of Green AI - | ||
| Luccioni et al. (2023) | Counting Carbon: A Survey of Factors Influencing the Emissions of Machine Learning - | ||
| Miao et al. (2023) | Towards Efficient Generative Large Language Model Serving: A Survey from Algorithms to Systems - | ||
Awesome Green AI 🤖🌱 / 🏢 Reports | |||
| Data For Good 2023 | The great challenges of generative AI (🇫🇷 only) - | ||
| AFNOR 2024 | General framework for frugal AI - | ||
| Goldman Sachs 2024 | Powering Up Europe: AI Datacenters and Electrification to Drive +c.40%-50% Growth in Electricity Consumption - | ||
| Goldman Sachs 2024 | Generational Growth — AI/data centers’ global power surge and the sustainability impact - | ||
| ITU 2024 | AI and the Environment - International Standards for AI and the Environment - | ||
| Deloitte 2024 | Powering artificial intelligence: a study of AI’s footprint—today and tomorrow - | ||
| Schneider Electric 2024 | Artificial Intelligence and Electricity: A System Dynamics Approach - | ||