The Hidden Cost of AI: Unraveling the Power-Hungry Nature of Large Language Models

Large Language Models (LLMs) have revolutionized artificial intelligence, driving advancements in natural language processing, automated content generation, and numerous other applications. However, these models' increasing scale and computational requirements pose significant energy consumption challenges. This paper comprehensively reviews power consumption in LLMs, highlighting key factors such as model size, hardware dependencies, and optimization techniques. We analyze the power demands of various state-of-the-art models, compare their efficiency across different hardware architectures, and explore strategies for reducing energy consumption without compromising performance. Additionally, we discuss the environmental impact of large-scale AI computations and propose future research directions for sustainable AI development. Our findings aim to inform researchers, engineers, and policymakers about the growing energy demand.}