Assessing the ecological impact of LLMs: the innovative Ecologits initiative

The rise of Generative Artificial Intelligence (GenIA) and Large Language Models (LLM) technologies has revolutionized many sectors, from content creation to the provision of personalized services. Today, Generative Artificial Intelligence is increasingly interwoven into our lives through tools (ChatGPT, Midjourney, DALL-E, etc.) capable of generating content (text, visual and video) of near-human quality and at unrivalled speed. However, this technological advance is accompanied by considerable energy consumption and a significant environmental impact. It is therefore crucial toassess these impacts to promote a reasoned and responsible use of generative AI and LLMs in particular. In this article, we explore why it is necessary to measure the ecological impact of generative AI and how the Ecologits.ai project contributes to this process with an innovative and impactful approach.

Performance and cost: an incomplete assessment of LLMs

L’évaluation des LLM repose sur plusieurs critères clés qui incluent des aspects de performance technique, de pertinence contextuelle, d’éthique et de sécurité, ainsi que des considérations d’efficacité et de coût. Aujourd’hui, les performances et le coût des modèles de langage sont souvent au centre des préoccupations des entreprises qui les utilisent. Cependant, cette évaluation reste incomplète si l’on omet de prendre en compte l’impact écologique de ces technologies.

Indeed, LLMs require huge amounts of energy for training and inference, and their increasing use contributes significantly to greenhouse gas emissions. According to some studies, training large models could generate as much CO2 as a car over its lifetime. In view of this, measuring only the performance and cost of LLMs is tantamount to ignoring a crucial part of their overall impact. However, while the performance and cost of generative AI are easy to assess, the environmental impact of its use remains a considerable grey area, despite - or perhaps because of - the incredible speed of its adoption by the general public.

Dans ce contexte, l’association Data for good, qui travaille chaque année sur des projets à impacts positifs sur des thématiques sociales, sociétales et environnementales, a conduit une recherche approfondie sur les enjeux de l’IA générative, ses implications directes et indirectes sur notre société et notre environnement. Une centaine de bénévoles ont créé le collectif “GenAI Impact” pour mettre en lumière l’impact des technologies de GenAI sur l’environnement, mieux les comprendre, les quantifier et sensibiliser les utilisateurs. L’objectif ? Mieux évaluer l’impact environnemental de l’IA générative et permettre une utilisation raisonnée et responsable.

C’est ainsi que le collectif du projet « GenAI Impact» a créé 𝐄𝐜𝐨𝐋𝐨𝐠𝐢𝐭𝐬, une bibliothèque Python conçue pour permettre aux utilisateurs de mesurer les impacts environnementaux des requêtes faites aux différentes applications d’ IA générative.

Ecologits.ai: a pioneering initiative

The first version of Ecologits.ai is an innovative Python library that evaluates the environmental impact and energy consumption of LLMs during inference. This module is in line with a logic of transparency and environmental responsibility, providing developers and companies with the tools they need to quantify and reduce the ecological footprint of their models.

Ecologits.ai features

1. Measuring energy consumption: Ecologits.ai provides tools for measuring the amount of energy consumed during model inference. This includes granularity to see energy consumed per query, per session, or over longer periods.
2. Carbon footprint calculation: Based on energy consumption data and using specific conversion factors, the library calculates the carbon footprint generated by LLM use.

A transparent, collaborative approach

One of Ecologits.ai's strengths is its transparent, collaborative approach. The project is open-source, allowing the community to contribute, improve and verify the algorithms used. This transparency guarantees that the measurements taken are reliable and accepted by the community.

The aim of this tool is to foster a sustainable approach within the technology industry, where developers can make informed choices to minimize environmental impact without sacrificing performance.

In concrete terms, EcoLogits tracks the energy consumption and environmental footprint of AI models used for inference. Models belonging to five Artificial Intelligence services (OpenAI, MistralAI, Hugging Face, Cohere, Anthropic, Google) can already be analyzed.

Wikit's contribution to Ecologits

En tant que spécialiste de la création de chatbots basés sur l’IA générative et engagés dans une démarche numérique responsable, il nous a paru nécessaire de contribuer à un projet tel qu’Ecologits. Notre équipe de Recherche & Développement a identifié un sujet clé pour optimiser la librairie : favoriser l’utilisation d’Ecologits en production en proposant une intégration avec la passerelle LiteLLM. Ce projet aussi open source facilite l’appel de grands modèles de langages (une sorte de proxy) en standardisant les appels aux différents fournisseurs sous le format d’OpenAI.

Why is this necessary? Having to individualize call parameters for each supplier greatly complicates the architecture of our applications. Unifying them in a single format makes the application more flexible and facilitates the integration of new models and/or suppliers, while keeping the code simple.

We have therefore contributed to Ecologits by proposing integration with LiteLLM. Our objective is clear: to facilitate the integration of environmental impact measurement in production. We are still actively proposing improvements to the project so that it becomes a mandatory best practice in the creation of architectures based on large language models.

Conclusion

Thanks to solutions like Ecologits.ai, companies now have an invaluable tool for accurately assessing the ecological impact of LLMs. This enables them to choose models that combine performance and sustainability. By adopting a holistic and innovative approach, it is possible to minimize the trade-off between ecology and performance, by making technological choices that support both business and environmental objectives. Performance and cost must no longer be the sole criteria for evaluating LLMs; it's time to integrate ecological impact into this equation for a more sustainable future!