剖析大模型可持续性难题，给出节能降碳的实用方案。
A Survey of Sustainability in Large Language Models: Applications, Economics, and Challenges
- 系统梳理大模型在训练与部署中的能耗与碳排放问题
- 提出资源优化、可再生能源整合等多维度减碳策略
- 适合关注AI环保与政策制定的研究者和从业者
大型语言模型（LLMs）在自然语言理解、生成和推理方面展现出强大能力，已广泛应用于科研、医疗和创意媒体等领域。然而其快速普及也带来了环境、经济与计算层面的可持续性挑战，尤其体现在数据中心的能源消耗、碳排放与资源利用上。本文综述现有研究，探讨资源高效训练、绿色部署及全生命周期评估等缓解路径，重点涵盖能效优化、可再生能源集成，以及性能与可持续性的平衡。研究旨在为研究人员、实践者和政策制定者提供可操作的可持续AI发展策略，推动人工智能向更负责任、环境友好的方向演进。
原文摘要 · Abstract (English)
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.
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