A Survey on the Green Development of Large Models: From Resource-Efficient Architectures to Hardware-Software Co-Design

2026-07-10

AI Summary

This study addresses the pressing environmental sustainability challenges posed by the high computational costs and energy consumption of large-scale AI models. It presents a systematic review of full-stack technical pathways toward greener foundation models, uniquely integrating co-optimization strategies across algorithmic and hardware layers. On the algorithmic side, it encompasses linear-complexity architectures, sparsification, and parameter-efficient fine-tuning; on the hardware side, it includes energy-efficient chips, memory-centric designs, and cross-platform deployment. The work further extends these advances to sustainability-oriented applications such as remote sensing and national infrastructure. By constructing a comprehensive roadmap spanning model design, training, and deployment, this research provides both theoretical grounding and practical guidance for developing large models that are efficient, scalable, and socially responsible.

Abstract

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

Problem

• large models
• green development
• energy consumption
• computational cost
• environmental sustainability

Innovation

• resource-efficient architectures
• hardware-software co-design
• model sparsification
• energy-efficient AI hardware
• sustainable AI