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

• URL: http://arxiv.org/abs/2607.09084v1
• Date: Fri, 10 Jul 2026 04:02:22 GMT
• Title: A Survey on the Green Development of Large Models: From Resource-Efficient Architectures to Hardware-Software Co-Design
• Abstract summary: The rapid expansion of large-scale AI models has led to significant performance breakthroughs across diverse domains.<n>It has also raised critical concerns regarding computational costs, energy consumption, and environmental sustainability.<n>This survey aims to offer a holistic roadmap toward sustainable, scalable, and socially responsible development of large models.
• Score: 71.32556809242666
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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

Related papers

• Perspective: Towards sustainable exploration of chemical spaces with machine learning [49.29528647472741]
  We argue that sustainable progress will rely on open data and models, reusable datasets, and domain-specific AI systems.<n>We highlight emerging strategies to enhance efficiency, including general-purpose machine learning (ML) models, multi-fidelity approaches, model distillation, and active learning.
  arXiv  Detail & Related papers  (2026-03-31T13:29:20Z)
• Throughput Optimization as a Strategic Lever in Large-Scale AI Systems: Evidence from Dataloader and Memory Profiling Innovations [0.0]
  This paper synthesizes evidence from recent academic and industry innovations to analyze key advancements in training efficiency.<n>We examine architectural solutions to dataloader bottlenecks, such as the OVERLORD framework, which has demonstrated a 4.5% improvement in end-to-end training throughput.<n>We explore the growing importance of compiler-centric optimizations, exemplified by Triton-distributed, which enables the joint optimization of computation, memory, and communication for substantial performance gains.
  arXiv  Detail & Related papers  (2026-03-27T00:04:23Z)
• Advances and Frontiers of LLM-based Issue Resolution in Software Engineering: A Comprehensive Survey [59.3507264893654]
  Issue resolution is a complex Software Engineering task integral to real-world development.<n> benchmarks like SWE-bench revealed this task as profoundly difficult for large language models.<n>This paper presents a systematic survey of this emerging domain.
  arXiv  Detail & Related papers  (2026-01-15T18:55:03Z)
• The Role of Advanced Computer Architectures in Accelerating Artificial Intelligence Workloads [0.0]
  The remarkable progress in Artificial Intelligence (AI) is foundation-ally linked to a concurrent revolution in computer architecture.<n>As AI models, particularly Deep Neural Networks (DNNs), have grown in complexity, their massive computational demands have pushed traditional architectures to their limits.<n>This paper provides a structured review of this co-evolution, analyzing the architectural landscape designed to accelerate modern AI workloads.
  arXiv  Detail & Related papers  (2025-11-13T06:26:39Z)
• Edge-Cloud Collaborative Computing on Distributed Intelligence and Model Optimization: A Survey [58.50944604905037]
  Edge-cloud collaborative computing (ECCC) has emerged as a pivotal paradigm for addressing the computational demands of modern intelligent applications.<n>Recent advancements in AI, particularly deep learning and large language models (LLMs), have dramatically enhanced the capabilities of these distributed systems.<n>This survey provides a structured tutorial on fundamental architectures, enabling technologies, and emerging applications.
  arXiv  Detail & Related papers  (2025-05-03T13:55:38Z)
• Empowering Edge Intelligence: A Comprehensive Survey on On-Device AI Models [16.16798813072285]
  The rapid advancement of artificial intelligence (AI) technologies has led to an increasing deployment of AI models on edge and terminal devices.<n>This survey comprehensively explores the current state, technical challenges, and future trends of on-device AI models.
  arXiv  Detail & Related papers  (2025-03-08T02:59:51Z)
• On Accelerating Edge AI: Optimizing Resource-Constrained Environments [1.7355861031903428]
  Resource-constrained edge deployments demand AI solutions that balance high performance with stringent compute, memory, and energy limitations.<n>We present a comprehensive overview of the primary strategies for accelerating deep learning models under such constraints.
  arXiv  Detail & Related papers  (2025-01-25T01:37:03Z)
• A Survey on Inference Optimization Techniques for Mixture of Experts Models [50.40325411764262]
  Large-scale Mixture of Experts (MoE) models offer enhanced model capacity and computational efficiency through conditional computation.<n> deploying and running inference on these models presents significant challenges in computational resources, latency, and energy efficiency.<n>This survey analyzes optimization techniques for MoE models across the entire system stack.
  arXiv  Detail & Related papers  (2024-12-18T14:11:15Z)
• Inference Optimization of Foundation Models on AI Accelerators [68.24450520773688]
  Powerful foundation models, including large language models (LLMs), with Transformer architectures have ushered in a new era of Generative AI. As the number of model parameters reaches to hundreds of billions, their deployment incurs prohibitive inference costs and high latency in real-world scenarios. This tutorial offers a comprehensive discussion on complementary inference optimization techniques using AI accelerators.
  arXiv  Detail & Related papers  (2024-07-12T09:24:34Z)
• A Survey of Resource-efficient LLM and Multimodal Foundation Models [22.23967603206849]
  Large foundation models, including large language models (LLMs), vision transformers (ViTs), diffusion, and multimodal models, are revolutionizing the entire machine learning lifecycle. However, the substantial advancements in versatility and performance these models offer come at a significant cost in terms of hardware resources. This survey delves into the critical importance of such research, examining both algorithmic and systemic aspects.
  arXiv  Detail & Related papers  (2024-01-16T03:35:26Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.