Green AI: Minimizing Environmental Cost of AI Model Training and Deployment

Abstract

The rapid development of artificial intelligence (AI), particularly deep learning models, has contributed to transformative innovations across various industries. The environmental influence of AI model training and deployment, especially energy consumption and carbon emissions through large-scale computational tasks, has gained increasing attention. This paper explores the concept of “Green AI,” a framework that emphasises minimizing the environmental costs of AI without sacrificing performance. By examining current practices in model development, energy consumption during training, and the role of sustainable deployment strategies, this research highlights practical solutions to mitigate AI’s environmental footprint while encouraging more efficient and eco-friendly models.