A new study from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) demonstrates a significant advancement in making AI systems more energy-efficient. The research focuses on reducing the computational power required for large language models (LLMs) during the inference phase, where the trained model generates responses. The team’s method, called ‘LLM inference with adaptive computation,’ selectively activates only the necessary parts of the neural network for a given query, rather than running the entire model. This approach can reduce energy consumption by up to 50% on certain tasks with minimal impact on accuracy. The technique is particularly promising for deploying powerful AI on edge devices and in data centers, where energy costs are a major concern. The full details of the research are available in the published paper: https://example.com/full-article-link.