# Generating Energy-efficient code with LLMs

arXiv: https://arxiv.org/abs/2411.10599 · HTML: https://mlsanity.com/paper/2411.10599/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 31 references that are in the map.
- First posted on arXiv 15 Nov 2024.

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

- [Code Llama: Open Foundation Models for Code](https://mlsanity.com/paper/2308.12950/) (2023)
- [DeepSeek-Coder: When the Large Language Model Meets Programming -- The Rise of Code Intelligence](https://mlsanity.com/paper/2401.14196/) (2024)
- [Carbon Emissions and Large Neural Network Training](https://mlsanity.com/paper/2104.10350/) (2021)
- [From Words to Watts: Benchmarking the Energy Costs of Large Language Model Inference](https://mlsanity.com/paper/2310.03003/) (2023)
- [Is Temperature the Creativity Parameter of Large Language Models?](https://mlsanity.com/paper/2405.00492/) (2024)
- [Great Power, Great Responsibility: Recommendations for Reducing Energy for Training Language Models](https://mlsanity.com/paper/2205.09646/) (2022)
- [On Evaluating the Efficiency of Source Code Generated by LLMs](https://mlsanity.com/paper/2404.06041/) (2024)
- [A Controlled Experiment on the Energy Efficiency of the Source Code Generated by Code Llama](https://mlsanity.com/paper/2405.03616/) (2024)

## Built on by (most cited first, 4 of 4)

- [Evaluating the Energy-Efficiency of the Code Generated by LLMs](https://mlsanity.com/paper/2505.20324/) (2025)
- [Energy-Aware Code Generation with LLMs: Benchmarking Small vs. Large Language Models for Sustainable AI Programming](https://mlsanity.com/paper/2508.08332/) (2025)
- [Can We Make Code Green? Understanding Trade-Offs in LLMs vs. Human Code Optimizations](https://mlsanity.com/paper/2503.20126/) (2025)
- [Improving the Energy-Efficiency of the Code Generated by LLMs through Effective Prompting](https://mlsanity.com/paper/2610.02571/) (2026)

## Abstract (arXiv)

The increasing electricity demands of personal computers, communication networks, and data centers contribute to higher atmospheric greenhouse gas emissions, which in turn lead to global warming and climate change. Therefore the energy consumption of code must be minimized. Code can be generated by large language models. We look at the influence of prompt modification on the energy consumption of the code generated. We use three different Python code problems of varying difficulty levels. Prompt modification is done by adding the sentence ``Give me an energy-optimized solution for this problem'' or by using two Python coding best practices. The large language models used are CodeLlama-70b, CodeLlama-70b-Instruct, CodeLlama-70b-Python, DeepSeek-Coder-33b-base, and DeepSeek-Coder-33b-instruct. We find a decrease in energy consumption for a specific combination of prompt optimization, LLM, and Python code problem. However, no single optimization prompt consistently decreases energy consumption for the same LLM across the different Python code problems.