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LLM on the edge: the new frontier

Семеріков, Сергій Олексійович; Вакалюк, Тетяна Анатоліївна; Каневська, Ольга Борисівна; Моісеєнко, Михайло Вікторович; Дончев, Іван Іванович; Колгатін, Андрій Олександрович

URI: http://elibrary.kdpu.edu.ua/xmlui/handle/123456789/12014
https://ceur-ws.org/Vol-3943/paper28.pdf

Date: 2025-03-14

Abstract:

The advent of large language models (LLMs) has revolutionized natural language processing, enabling unprecedented capabilities in text generation, reasoning, and human-machine interaction. However, their deployment on resource-constrained edge devices presents significant challenges due to high computational complexity, large model sizes, and stringent latency and privacy requirements. This survey provides a comprehensive examination of the emerging field of edge-based LLMs, exploring the techniques, frameworks, hardware solutions, and real-world applications that enable their efficient deployment at the edge. We review key strategies such as model quantization, pruning, knowledge distillation, and adapter tuning, alongside edge-cloud collaborative architectures like EdgeShard, Edge-LLM, and PAC. Additionally, we analyze hardware acceleration solutions, including Cambricon-LLM, AxLaM, and DTATrans/DTQAtten, and their role in overcoming resource limitations. The survey highlights diverse applications, from IoT and smart cities to personalized services and multi-modal intelligence, supported by case studies of real-world deployments. Finally, we discuss open challenges – such as resource efficiency, privacy, security, and scalability – and propose future research directions to advance this transformative technology.

Description:

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