Abstract
This paper introduces Artemis AI, a novel framework leveraging multiple Large Language Models (LLMs) op-timise code performance. Artemis AI achieves significant performance improvements in highly-optimised code across diverse domains with minimal changes. We focus on three representative open-source projects: QuantLib (quantitative finance), Llama2.c (natural language processing), and OpenAI Whisper (automatic speech recognition) and one proprietary high performance code-base. Our multi-stage process involves extracting target code snippets, independent optimisation by multiple LLMs, and a search-based selection of the optimal solutions, achieving a 30% reduction in execution time for QuantLib, a 52% reduction for Llama2.c, and a 15% reduction for OpenAI Whisper. These results highlight the potential of multi-LLM collaboration for substantial performance gains that lead to greener software while preserving code readability and reliability.