Green Research Computing

An interactive tool to help you estimate your energy emissions and water use from interacting with different generative AI models.

Huggingface leaderboard covering a variety of generative AI models, ranked by energy consumption.

Library that lets you easily monitor energy usage of machine learning programs. It uses RAPL for the CPU and nvidia-smi for the GPU.

Python package to measure the carbon footprint of machine learning models.

Eco2AI is a python library for CO2 emission tracking. It monitors energy consumption of CPU & GPU devices and estimates equivalent carbon emissions taking into account the regional emission coefficient.

EcoLogits tracks the energy consumption and environmental impacts of using generative AI models through APIs. It supports major LLM providers such as OpenAI, Anthropic, Mistral AI and more.

A version of the Green Algorithms carbon footprint calculator tailored for measuring AI training jobs.

The goal of the ML.ENERGY Leaderboard is to give people a sense of how much energy LLMs would consume, and the complex tradeoffs between energy, system performance, and user experience.

GPU carbon footprint measurement for machine learning tasks. From the makers of CodeCarbon. Includes a LaTeX template to be used for reporting carbon emissions in ML-related publications.

Comparison of popular NLP and computer vision models by carbon intensity and by region.

Zeus is a library for measuring the energy consumption of Deep Learning workloads and for optimizing their energy consumption. Part of the ML.ENERGY initiative.