Edge-LLM: A Collaborative Framework for Large Language Model Serving in Edge Computing

The rapid advancement and extensive implementation of Large Language Models (LLMs) are milestones in the realm of artificial intelligence. Although Parameter-Efficient Transfer Learning (PETL), a.k.a. Adapter, methods have reduced the barrier for fine-tuning and inference on LLMs, it becomes a chall...

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Bibliographic Details

Published in:	Proceedings (IEEE International Conference on Web Services. Online) pp. 799 - 809
Main Authors:	Cai, Fenglong, Yuan, Dong, Yang, Zhe, Cui, Lizhen
Format:	Conference Proceeding
Language:	English
Published:	IEEE 07.07.2024
Subjects:	Adaptation models Collaboration collaborative framework Computational modeling edge computing Frequency modulation Graphics processing units large language model Large language models online scheduling algorithm Quality of service Quantization (signal) Scheduling algorithms service Web services
ISSN:	2836-3868
Online Access:	Get full text
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