A Survey of Sustainability in Large Language Models: Applications, Economics, and Challenges
It addresses sustainability issues in AI for researchers, practitioners, and policymakers, but is incremental as it synthesizes existing literature.
This survey tackles the environmental, economic, and computational challenges of large language models, such as energy consumption and carbon emissions, by exploring strategies like resource-efficient training and sustainable deployment to guide sustainable AI development.
Large Language Models (LLMs) have transformed numerous domains by providing advanced capabilities in natural language understanding, generation, and reasoning. Despite their groundbreaking applications across industries such as research, healthcare, and creative media, their rapid adoption raises critical concerns regarding sustainability. This survey paper comprehensively examines the environmental, economic, and computational challenges associated with LLMs, focusing on energy consumption, carbon emissions, and resource utilization in data centers. By synthesizing insights from existing literature, this work explores strategies such as resource-efficient training, sustainable deployment practices, and lifecycle assessments to mitigate the environmental impacts of LLMs. Key areas of emphasis include energy optimization, renewable energy integration, and balancing performance with sustainability. The findings aim to guide researchers, practitioners, and policymakers in developing actionable strategies for sustainable AI systems, fostering a responsible and environmentally conscious future for artificial intelligence.