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Large Language Models for Energy-Efficient Code: Emerging Results and Future Directions
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Energy-efficient software helps improve mobile device experiences and reduce the carbon footprint of data centers. However, energy goals are often de-prioritized in order to meet other requirements. We take inspiration from recent work exploring the use of large language models (LLMs) for different software engineering activities. We propose a novel application of LLMs: as code optimizers for energy efficiency. We describe and evaluate a prototype, finding that over 6 small programs our system can improve energy efficiency in 3 of them, up to 2x better than compiler optimizations alone. From our experience, we identify some of the challenges of energy-efficient LLM code optimization and propose a research agenda.
Forward citations
Cited by 2 Pith papers
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Evaluating the Energy-Efficiency of the Code Generated by LLMs
LLM-generated Python solutions typically consume more energy than canonical human-written solutions, with DeepSeek-v3 and GPT-4o the most efficient LLMs and worst-case gaps near 450 times on certain problems.
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Energy-Aware Code Generation with LLMs: Benchmarking Small vs. Large Language Models for Sustainable AI Programming
On 150 LeetCode problems, GPT-4.0 and DeepSeek-Reasoner beat three 3B-parameter models on correctness and speed; the 52% energy-efficiency claim counts any of three SLMs on correct outputs, not a per-model advantage.
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