Energy-Efficient and Sustainable Artificial Intelligence Models..

Abstract

Computing demand, energy consumption, and associated environmental problems have been accelerated by artificial intelligence (AI) technologies, which have seen rapid progress and widespread adoption. Despite the significant benefits that AI-driven systems provide to sectors such as healthcare, finance, transportation, and government, there are valid concerns about their long-term sustainability and viability caused by their increasing carbon footprint. This paper examines the concept of sustainable and energy-efficient AI models with a focus on methods that reduce computer complexity, optimize resource utilization, and minimize environmental costs without compromising performance. Methods like as green data centers, energy-aware algorithm design, federated and edge learning, model compression, and AI infrastructure integration of renewable energy sources are important and explored. Sustainable AI, the study continues, is an essential part of aligning technological development with global environmental goals, moral duty, and legal mandates. The article argues that artificial. intelligence models should emphasize efficiency, scalability, and ecological balance in order to ensure responsible digital transformation and sustainable development in the era of intelligent systems