The rapid development of generative AI technologies, including large language models (LLMs), has brought transformative changes to various fields. However, deploying such advanced models on mobile and edge devices remains challenging due to their high computational, memory, communication, and energy requirements. To address these challenges, we propose a model-centric framework for democratizing generative AI deployment on mobile and edge networks. First, we comprehensively review key compact model strategies, such as quantization, model pruning, and knowledge distillation, and present key performance metrics to optimize generative AI for mobile deployment. Next, we provide a focused review of mobile and edge networks, emphasizing the specific challenges and requirements of these environments. We further conduct a case study demonstrating the effectiveness of these strategies by deploying LLMs on real mobile edge devices. Experimental results highlight the practicality of democratized LLMs, with significant improvements in generalization accuracy, hallucination rate, accessibility, and resource consumption. Finally, we discuss potential research directions to further advance the deployment of generative AI in resource-constrained environments.
@article{arxiv.2411.09148,
title = {Toward Democratized Generative AI in Next-Generation Mobile Edge Networks},
author = {Ruichen Zhang and Jiayi He and Xiaofeng Luo and Dusit Niyato and Jiawen Kang and Zehui Xiong and Yonghui Li and Biplab Sikdar},
journal= {arXiv preprint arXiv:2411.09148},
year = {2024}
}