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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Veröffentlicht in:	Proceedings (IEEE International Conference on Web Services. Online) S. 799 - 809
Hauptverfasser:	Cai, Fenglong, Yuan, Dong, Yang, Zhe, Cui, Lizhen
Format:	Tagungsbericht
Sprache:	Englisch
Veröffentlicht:	IEEE 07.07.2024
Schlagworte:	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-Zugang:	Volltext
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