LLM-Slice: Dedicated Wireless Network Slicing for Large Language Models

TL;DR

LLM-Slice introduces dedicated wireless network slices for large language models, significantly improving response speed and resource efficiency in wireless environments by tailored resource allocation and control.

Contribution

This work is the first to propose dedicated network slicing for LLMs in wireless networks, optimizing communication resources for faster and more reliable LLM services.

Findings

Significant improvement in response speed

Enhanced resource utilization

Reduced disconnections

Abstract

The rapid adoption of large language models (LLMs) presents new challenges for existing network architectures due to significant peak traffic and high communication uncertainty. Traditional wireless networks struggle to support efficiently, leading to intolerable response delays, disconnections, and resource wastage. To address these issues, we propose LLM-Slice, the first system to provide dedicated communication slices for LLMs within a wireless network environment. By creating LLM-specific network slices, LLM-Slice efficiently binds services with communication resources. Based on user equipment (UE) requests and a permissions database, the system registers specific slices to offer controllable LLM services, integrating a downlink resource control module to optimize response speed, enhance resource utilization, and reduce disconnections. By deploying and validating in a real UE-gNB-CN environment, numerical results demonstrate that LLM-Slice significantly improves response speed and resource efficiency, providing a novel solution for fast and controllable LLM access in wireless networks.