Green My LLM: Studying the key factors affecting the energy consumption of code assistants
Tristan Coignion, Cl\'ement Quinton, Romain Rouvoy

TL;DR
This study analyzes the energy consumption of LLM-based code assistants like GitHub Copilot, identifying key factors affecting energy use and proposing optimization strategies to reduce environmental impact.
Contribution
It provides the first comprehensive analysis of energy factors in LLM code assistants and offers practical guidelines for energy-efficient configurations.
Findings
Energy consumption is affected by model size, concurrency, and streaming.
A significant portion of code generation requests are canceled or rejected.
Optimizing configurations can lead to substantial energy savings.
Abstract
In recent years,Large Language Models (LLMs) have significantly improved in generating high-quality code, enabling their integration into developers' Integrated Development Environments (IDEs) as code assistants. These assistants, such as GitHub Copilot, deliver real-time code suggestions and can greatly enhance developers' productivity. However, the environmental impact of these tools, in particular their energy consumption, remains a key concern. This paper investigates the energy consumption of LLM-based code assistants by simulating developer interactions with GitHub Copilot and analyzing various configuration factors. We collected a dataset of development traces from 20 developers and conducted extensive software project development simulations to measure energy usage under different scenarios. Our findings reveal that the energy consumption and performance of code assistants are…
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Taxonomy
TopicsOpen Source Software Innovations · Green IT and Sustainability · Peer-to-Peer Network Technologies
