Evaluating the carbon footprint of code implementation

Abstract

With the growing popularity of Artificial Intelligence (AI) and its integration into our daily lives, the environmental impact of code implementation is on the rise. Large Language Models (LLMs) in particular, consume massive amounts of resources throughout their training and deployment phases. This project focuses on the fine-tuning process of LLMs, namely these three models— Meta’s LLaMA-2 (7B), Mistral (7B), and Google’s Gemma (2B, 7B) across different computational configurations, presenting a comparative emissions analysis to discover methods of achieving more environmentally friendly LLMs. A global collaborative initiative was created to encourage transparency in emissions data, which is an important gap that needed to be addressed. The results of this study hope to present ways to achieve more energy-efficient methods of developing LLMs, leading to more sustainable AI development for the future.