想了解怎么让大模型更省资源、更环保？这篇综述从算法优化到硬件设计都讲透了，还提到了DeepSeek等应用案例。
该综述系统梳理了大型模型绿色发展的研究进展，涵盖注意力算子优化、线性复杂度架构、模型稀疏化与合并等效率型模型设计方法，以及数据高效学习、参数高效微调、计算压缩等训练与部署策略。在硬件层面，总结了主流AI芯片、内存优化、跨平台部署与可持续基础设施。还探讨了大型模型在DeepSeek、遥感解译、国家级基础设施等可持续关键领域的应用，并指出持续学习范式、以内存为中心的硬件和标准化评估协议等未来挑战。
A Survey on the Green Development of Large Models: From Resource-Efficient Architectures to Hardware-Software Co-Design
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