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This chapter explores collaborative frameworks that integrate cloud and edge systems to support distributed LLM service provisioning, with emphasis on model partitioning, inference scaling, and resource management. It introduces application scenarios such as mobile health, humanoid robots, virtual assistants, and autonomous driving and analyzes their latency, bandwidth, and privacy requirements. The chapter details AI native network architectures and mechanisms for parameter-sharing LLM caching, distributed training, and multi-style edge inference, showing how wireless networks can actively support and accelerate language model services.

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Net4LMs: Wireless Network-Aided Language Models

  • 章节

  • 发表日期：

    2026年

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  • Hongyang Du,
  • Xianhao Chen,
  • Yuanwei Liu,
  • Kaibin Huang

摘要

This chapter explores collaborative frameworks that integrate cloud and edge systems to support distributed LLM service provisioning, with emphasis on model partitioning, inference scaling, and resource management. It introduces application scenarios such as mobile health, humanoid robots, virtual assistants, and autonomous driving and analyzes their latency, bandwidth, and privacy requirements. The chapter details AI native network architectures and mechanisms for parameter-sharing LLM caching, distributed training, and multi-style edge inference, showing how wireless networks can actively support and accelerate language model services.