系统梳理大模型绿色发展的路径，从高效架构到软硬件协同设计。
A Survey on the Green Development of Large Models: From Resource-Efficient Architectures to Hardware-Software Co-Design
- 聚焦注意力优化、线性复杂度结构与模型稀疏化等高效架构设计。
- 提出算法与硬件协同优化方案，降低训练部署能耗与资源消耗。
- 适合关注可持续AI、模型效率与绿色计算的研究者与从业者。
大规模AI模型的迅猛发展在多个领域取得显著性能突破，但同时也带来了计算成本高、能耗大及环境可持续性挑战。本文系统综述大模型绿色发展的现状，重点涵盖资源高效架构与全栈软硬件协同设计。梳理了高效模型构建的最新进展，包括注意力算子优化、线性复杂度架构、模型稀疏化与合并；以及数据高效学习、参数高效微调和计算压缩等训练与部署策略。此外，探讨了能效优先的AI硬件，如主流AI芯片、内存优化、跨平台部署与可持续基础设施。进一步分析大模型在深求（DeepSeek）、遥感图像解析、国家级基础设施与全球倡议中的可持续应用。最后，讨论关键挑战与未来方向，强调持续学习范式、以内存为中心的硬件设计与标准化评估体系的必要性。本综述旨在为大模型的可持续、可扩展与社会负责任发展提供整体路线图。
原文摘要 · Abstract (English)
The rapid expansion of large-scale AI models has led to significant performance breakthroughs across diverse domains, yet it has also raised critical concerns regarding computational costs, energy consumption, and environmental sustainability. This survey provides a comprehensive overview of the green development of large models, emphasizing resource-efficient architectures and full-stack hardware-software co-design. We systematically review recent advances in efficient model construction, including attention operator optimization, linear-complexity architectures, and model sparsification and merging, as well as training and deployment strategies such as data-efficient learning, parameter-efficient fine-tuning, and computational compression. Beyond algorithmic improvements, we explore energy-efficient AI hardware, including mainstream AI chips, memory optimization, cross-platform deployment, and sustainable infrastructure. Furthermore, we examine how large models are being applied to sustainability-critical domains such as DeepSeek, remote sensing interpretation, national-scale infrastructure, and global initiatives. Finally, we discuss key challenges and future directions, highlighting the need for continual learning paradigms, memory-centric hardware, and standardized evaluation protocols. This survey aims to offer a holistic roadmap toward sustainable, scalable, and socially responsible development of large models. Paper homepage: https://cje.ejournal.org.cn/article/doi/10.23919/cje.2025.00.438
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