arXiv cs.CL· Rahul Maliakkal, Yashasvi Makin, Plawan Rath, Rohit Jain, Ankur Sadhoo·· 11 小时前AI 评分22
大语言模型在资源受限边缘设备上的部署：从业者综述
Large Language Model Deployment on Resource-Constrained Edge Devices: A Practitioners Survey
AI 导读
这篇综述梳理了让 LLM 能在资源受限的移动与边缘设备上部署的最新软硬件优化技术。边缘部署可带来本地数据处理、成本节省、更低延迟和个性化响应等收益，但 LLM 庞大的资源需求仍是主要障碍。
正文 · 原文
Abstract:Large Language Models (LLMs) have transformed many areas of artificial intelligence (AI), but their substantial resource requirements limit their deployment on mobile and edge devices. Although LLMs have demonstrated impressive capabilities in various applications, the next challenge is deploying these models on resource-limited edge devices. Recent innovations in software optimizations and specialized hardware have made it feasible to deploy LLMs on edge devices. Edge deployment offers benefits including local data processing, cost savings, reduced latency, and personalized responses. This paper reviews the latest techniques that make the deployment of LLMs on edge devices feasible.
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computation and Language (cs.CL) |
| Cite as: | arXiv:2611.09075 [cs.LG] |
| (or arXiv:2611.09075v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2611.09075 arXiv-issued DOI via DataCite (pending registration) |
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| Related DOI: | https://doi.org/10.1109/CCWC67433.2026.11393769
DOI(s) linking to related resources |
Submission history
From: Rahul Maliakkal [view email]
[v1]
Fri, 6 Nov 2026 07:01:48 UTC (118 KB)
来源：arXiv cs.CL · arxiv.org