Large Language Models on Mobile Devices: Measurements, Analysis, and Insights

Xiang Li, Zhenyan Lu, Dongqi Cai, Xiao Ma, Mengwei Xu

Abstract: Deploying large language models (LLMs) inference into mobile devices is cost-efficient for companies, and well addresses the privacy concern of users. However, the limited computation capacity and memory constraints of mobile devices hinder their practical deployment. Prior work strives to expand model size for better accuracy performance, while there is a lack of systematic understanding of "small" sub-10 billion LLMs that are already feasible for current commodity devices. To better reveal the current landscape of LLMs on mobile devices, we conducted a comprehensive measurement study, deploying 22 models across 4 mobile devices. Our measurements focus on accuracy, inference latency, and memory footprint across various input lengths, devices, and execution engines. The observations from the measurements point us toward promising directions for efficient LLM deployment on mobile devices.