Green AI: Designing Energy-Efficient Machine Learning Models

Abstract

Artificial Intelligence (AI) and Machine Learning (ML) are increasingly being deployed in diverse sectors, but their computational intensity poses significant energy and environmental challenges. Large-scale models consume substantial electricity, leading to rising carbon footprints that hinder global sustainability goals. This paper explores the concept of Green AI, which emphasizes the development of energy-efficient ML models without compromising accuracy. By reviewing existing methods, frameworks, and innovations in sustainable AI, this research proposes design strategies for computational efficiency, including model compression, transfer learning, and efficient hardware utilization. Data analysis on energy costs of AI models highlights the urgency for energy-aware algorithm design. Findings suggest that Green AI is not only a technological necessity but also a strategic imperative for industries and policymakers in achieving sustainable digital transformation.