# Green My LLM: Studying the key factors affecting the energy consumption of code assistants

arXiv: https://arxiv.org/abs/2411.11892 · HTML: https://mlsanity.com/paper/2411.11892/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 37 references that are in the map.
- First posted on arXiv 7 Nov 2024.

## Builds on (most cited first, 8 of 8)

- [Evaluating Large Language Models Trained on Code](https://mlsanity.com/paper/2107.03374/) (2021)
- [Program Synthesis with Large Language Models](https://mlsanity.com/paper/2108.07732/) (2021)
- [StarCoder: may the source be with you!](https://mlsanity.com/paper/2305.06161/) (2023)
- [StarCoder 2 and The Stack v2: The Next Generation](https://mlsanity.com/paper/2402.19173/) (2024)
- [Counting Carbon: A Survey of Factors Influencing the Emissions of Machine Learning](https://mlsanity.com/paper/2302.08476/) (2023)
- [Learn to Code Sustainably: An Empirical Study on LLM-based Green Code Generation](https://mlsanity.com/paper/2403.03344/) (2024)
- [PePR: Performance Per Resource Unit as a Metric to Promote Small-Scale Deep Learning in Medical Image Analysis](https://mlsanity.com/paper/2403.12562/) (2024)
- [Deep Learning Models in Speech Recognition: Measuring GPU Energy Consumption, Impact of Noise and Model Quantization for Edge Deployment](https://mlsanity.com/paper/2405.01004/) (2024)

## Built on by (most cited first, 6 of 6)

- [COPA: Comparing the incomparable in multi-objective model evaluation](https://mlsanity.com/paper/2503.14321/) (2025)
- [Energy-Aware Code Generation with LLMs: Benchmarking Small vs. Large Language Models for Sustainable AI Programming](https://mlsanity.com/paper/2508.08332/) (2025)
- [Aggregating empirical evidence from data strategy studies: a case on model quantization](https://mlsanity.com/paper/2505.00816/) (2025)
- [Pimp My LLM: Leveraging Variability Modeling to Tune Inference Hyperparameters](https://mlsanity.com/paper/2602.17697/) (2026)
- [Attention to Detail: Evaluating Energy, Performance, and Accuracy Trade-offs Across vLLM Configurations](https://mlsanity.com/paper/2607.09172/) (2026)
- [From Perception to Action: Can UI Interventions Foster Sustainable LLM Chatbot](https://mlsanity.com/paper/2606.10861/) (2026)

## Abstract (arXiv)

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.