A Survey of Sustainability in Large Language Models: Applications, Economics, and Challenges

TL;DR

This survey reviews the environmental, economic, and computational challenges of large language models, emphasizing sustainability strategies like energy efficiency, renewable energy, and lifecycle assessments to reduce their environmental impact.

Contribution

It provides a comprehensive synthesis of existing research on LLM sustainability, highlighting strategies for energy optimization and responsible deployment to guide future development.

Findings

Energy consumption and carbon emissions are significant in LLM deployment.

Resource-efficient training and renewable energy can mitigate environmental impacts.

Lifecycle assessments help in understanding and reducing LLM environmental footprints.

Abstract

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.