Green My LLM: Studying the key factors affecting the energy consumption of code assistants
It addresses the environmental impact of AI tools for developers, focusing on reducing energy waste in code assistants, though it is incremental as it builds on existing concerns without introducing new paradigms.
This paper investigates the energy consumption of LLM-based code assistants by simulating developer interactions with GitHub Copilot, finding that factors like model size and request cancellations significantly affect energy usage and offering optimization insights for substantial energy savings.
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 influenced by various factors, such as the number of concurrent developers, model size, quantization methods, and the use of streaming. Notably, a substantial portion of generation requests made by GitHub Copilot is either canceled or rejected by developers, indicating a potential area for reducing wasted computations. Based on these findings, we share actionable insights into optimizing configurations for different use cases, demonstrating that careful adjustments can lead to significant energy savings.