awesome-green-ai

CodeCarbon

– Track emissions from Compute and recommend ways to reduce their impact on the environment

carbontracker

– Track and predict the energy consumption and carbon footprint of training deep learning models

Eco2AI

– A python library which accumulates statistics about power consumption and CO2 emission during running code

Zeus

– A framework for deep learning energy measurement and optimization

EcoLogits

– Estimates the energy consumption and environmental footprint of LLM inference through APIs

Tracarbon

– Tracks your device's energy consumption and calculates your carbon emissions using your location

AIPowerMeter

– Easily monitor energy usage of machine learning programs

carbonai

– Python package to monitor the power consumption of any algorithm

experiment-impact-tracker

– A simple drop-in method to track energy usage, carbon emissions, and compute utilization of your system

GATorch

– An Energy-Aware PyTorch Extension

GPU Meter

– Power Consumption Meter for NVIDIA GPUs

PyJoules

– A Python library to capture the energy consumption of code snippets

Scaphandre

– A metrology agent dedicated to electrical power consumption metrics

CodeCarbon

– Track emissions from Compute and recommend ways to reduce their impact on the environment

PowerJoular

– Monitor power consumption of multiple platforms and processes

ALUMET

– A modular and efficient software measurement tool

cardamon

– A tool for measuring the power consumption and carbon footprint of your software

Boagent

– Local API and monitoring agent focussed on environmental impacts of the host

Powerletrics

– PowerLetrics is a framework designed to monitor and analyze power consumption metrics at the process level on Linux

vJoule

– A tool to estimate the energy consumption of your processes

jupyter-power-usage

– Jupyter extension to display CPU and GPU power usage and carbon emissions

Zeus

– A framework for deep learning energy measurement and optimization

GEOPM

– A framework to enable efficient power management and performance optimizations

A tool to easily estimate the carbon footprint of a project

Compute model emissions and add the results to your paper with our generated latex template

Estimate energy consumption and environmental impacts of LLM inference

Estimate your AI model's carbon footprint

MLCarbon

End-to-end carbon footprint modeling tool

GenAI Carbon Footprint

A tool to estimate energy use (kWh) and carbon emissions (gCO2eq) from LLM usage

A data model and a viewer for carbon footprint scenarios

Boaviztapi

Multi-criteria impacts of compute resources taking into account manufacturing and usage

Compute resources data explorer not limited to AI

Compute carbon footprint of IT resources taking into account manufactuing and usage (🇫🇷 only)

Benchmarking LLMs on performance and energy

Energy consumption of GenAI models at inference

Energy efficiency ratings for AI models

Energy and Policy Considerations for Deep Learning in NLP -

Quantifying the Carbon Emissions of Machine Learning -

Carbontracker: Tracking and Predicting the Carbon Footprint of Training Deep Learning Models -

Green AI -

The Energy and Carbon Footprint of Training End-to-End Speech Recognizers -

Carbon Emissions and Large Neural Network Training -

Green Algorithms: Quantifying the Carbon Footprint of Computation -

Aligning artificial intelligence with climate change mitigation -

A Practical Guide to Quantifying Carbon Emissions for Machine Learning researchers and practitioners -

Unraveling the Hidden Environmental Impacts of AI Solutions for Environment Life Cycle Assessment of AI Solutions -

Measuring the Carbon Intensity of AI in Cloud Instances -

Estimating the Carbon Footprint of BLOOM a 176B Parameter Language Model -

Bridging Fairness and Environmental Sustainability in Natural Language Processing -

Eco2AI: carbon emissions tracking of machine learning models as the first step towards sustainable AI -

Environmental assessment of projects involving AI methods -

Sustainable AI: Environmental Implications, Challenges and Opportunities -

The Carbon Footprint of Machine Learning Training Will Plateau, Then Shrink -

Towards the Systematic Reporting of the Energy and Carbon Footprints of Machine Learning -

Towards Sustainable Artificial Intelligence: An Overview of Environmental Protection Uses and Issues -

Method and evaluations of the effective gain of artificial intelligence models for reducing CO2 emissions -

Making AI Less "Thirsty": Uncovering and Addressing the Secret Water Footprint of AI Models -

Zeus: Understanding and Optimizing GPU Energy Consumption of DNN Training -

Trends in AI inference energy consumption: Beyond the performance-vs-parameter laws of deep learning

Chasing Low-Carbon Electricity for Practical and Sustainable DNN Training -

Toward Sustainable HPC: Carbon Footprint Estimation and Environmental Implications of HPC Systems -

Reducing the Carbon Impact of Generative AI Inference (today and in 2035) -

LLMCarbon: Modeling the End-To-End Carbon Footprint of Large Language Models -

The growing energy footprint of artificial intelligence -

Exploring the Carbon Footprint of Hugging Face's ML Models: A Repository Mining Study -

Exploding AI Power Use: an Opportunity to Rethink Grid Planning and Management -

Power Hungry Processing: Watts Driving the Cost of AI Deployment? -

Perseus: Removing Energy Bloat from Large Model Training -

Timeshifting strategies for carbon-efficient long-running large language model training -

Estimating the environmental impact of Generative-AI services using an LCA-based methodology -

Towards Greener LLMs: Bringing Energy-Efficiency to the Forefront of LLM Inference -

Green AI: Exploring Carbon Footprints, Mitigation Strategies, and Trade Offs in Large Language Model Training -

Engineering Carbon Emission-aware Machine Learning Pipelines -

A simplified machine learning product carbon footprint evaluation tool -

Beyond Efficiency: Scaling AI Sustainably -

The Price of Prompting: Profiling Energy Use in Large Language Models Inference -

MLCA: a tool for Machine Learning Life Cycle Assessment -

Hype, Sustainability, and the Price of the Bigger-is-Better Paradigm in AI -

Addition is All You Need for Energy-efficient Language Models -

E-waste challenges of generative artificial intelligence -

Evaluating the carbon footprint of NLP methods: a survey and analysis of existing tools -

A Survey on Green Deep Learning -

A Systematic Review of Green AI -

Counting Carbon: A Survey of Factors Influencing the Emissions of Machine Learning -

Towards Efficient Generative Large Language Model Serving: A Survey from Algorithms to Systems -

The great challenges of generative AI (🇫🇷 only) -

General framework for frugal AI -

Powering Up Europe: AI Datacenters and Electrification to Drive +c.40%-50% Growth in Electricity Consumption -

Generational Growth — AI/data centers’ global power surge and the sustainability impact -

AI and the Environment - International Standards for AI and the Environment -

Powering artificial intelligence: a study of AI’s footprint—today and tomorrow -

Artificial Intelligence and Electricity: A System Dynamics Approach -