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

2024-12-06

arXiv.org

AI Summary

Large language models (LLMs) face significant sustainability challenges—including high energy consumption, substantial carbon emissions, and intensive resource use—hindering their continuous deployment. To address this, we systematically identify bottlenecks across environmental, economic, and computational dimensions and propose the first multidimensional sustainability assessment framework integrating life cycle assessment (LCA), energy efficiency modeling, and carbon footprint accounting. Methodologically, we introduce the “energy-efficiency–performance trade-off paradigm” and a “renewable-electricity-driven deployment pathway.” Our contributions include three actionable principles: resource-efficient training, green compute orchestration, and a standardized sustainability metrics suite. These results provide AI researchers, engineers, and policymakers with an interdisciplinary, full-lifecycle governance guide for green AI, shifting LLM development from capability-centric optimization toward synergistic capability–sustainability co-optimization.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: (Large) Language Models
• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness

Application Category

• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

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.

Problem

• Environmental Impact
• Resource Efficiency
• Sustainable Computing

Innovation

• Energy-efficient Training
• Environmental Impact Assessment
• Sustainable AI Deployment