Is Your NPU Ready for LLMs? Dissecting the Hidden Efficiency Bottlenecks in Mobile LLM Inference

Researchers have conducted the first comprehensive analysis of mobile hardware efficiency, identifying specific bottlenecks that currently hinder large language model performance on smartphones. By examining the interaction between software and hardware layers, the study clarifies why current neural processing units often struggle to maintain latency during local inference. This data provides a technical framework for developers to optimize mobile AI deployments, moving beyond theoretical capabilities toward practical, on-device efficiency.