# Network Edge Inference for Large Language Models: Principles, Techniques, and Opportunities

arXiv: https://arxiv.org/abs/2604.22906 · HTML: https://mlsanity.com/paper/2604.22906/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Apr 2026.

## Built on by (most cited first, 4 of 4)

- [PyroDash: Cost-Efficient Token-Level Small-Large Language Model Collaborative Inference](https://mlsanity.com/paper/2607.20327/) (2026)
- [BALANCE: Hybrid Autoregressive-Speculative LLM Inference at the Network Edge](https://mlsanity.com/paper/2608.05926/) (2026)
- [Large Models for Small Devices: Recent Advances and Empirical Analysis of Edge AI Deployment](https://mlsanity.com/paper/2608.15693/) (2026)
- [Queue-Aware Token Communications: Bridging AI Representation and Wireless Service](https://mlsanity.com/paper/2610.16730/) (2026)

## Abstract (arXiv)

Large language models (LLMs) have advanced rapidly, emerging as versatile tools across fields thanks to their exceptional language understanding, generation, and reasoning capabilities. However, performing LLM inference at the network edge remains challenging due to their large memory and compute demands. This survey outlines the challenges specific to LLM edge inference and provides a comprehensive overview of recent progress, covering system architectures, model optimization and deployment, and resource management and scheduling. By synthesizing state-of-the-art techniques and mapping future directions, this survey aims to unlock the potential of LLMs in resource-constrained edge environments.