Empirical Guidelines for Deploying LLMs onto Resource-constrained Edge
Devices

Empirical Guidelines for Deploying LLMs onto Resource-constrained Edge Devices

6 June 2024

Meng-Long Jiang

Ahmed Abbasi

Yiyu Shi

Papers citing "Empirical Guidelines for Deploying LLMs onto Resource-constrained Edge Devices"

6 / 6 papers shown

Title
Harnessing the Potential of Large Language Models in Modern Marketing Management: Applications, Future Directions, and Strategic Recommendations Raha Aghaei Ali A. Kiaei Mahnaz Boush Javad Vahidi Mohammad Zavvar Zeynab Barzegar Mahan Rofoosheh OffRL 31 0 0 18 Jan 2025
Investigating the performance of Retrieval-Augmented Generation and fine-tuning for the development of AI-driven knowledge-based systems Róbert Lakatos P. Pollner András Hajdu Tamas Joo 21 8 0 12 Mar 2024
Selecting Large Language Model to Fine-tune via Rectified Scaling Law Haowei Lin Baizhou Huang Haotian Ye Qinyu Chen Zihao Wang Sujian Li Jianzhu Ma Xiaojun Wan James Y. Zou Yitao Liang 82 20 0 04 Feb 2024
Agile-Quant: Activation-Guided Quantization for Faster Inference of LLMs on the Edge Xuan Shen Peiyan Dong Lei Lu Zhenglun Kong Zhengang Li Ming Lin Chao Wu Yanzhi Wang MQ 31 22 0 09 Dec 2023
Distilling Step-by-Step! Outperforming Larger Language Models with Less Training Data and Smaller Model Sizes Lokesh Nagalapatti Chun-Liang Li Chih-Kuan Yeh Hootan Nakhost Yasuhisa Fujii Alexander Ratner Ranjay Krishna Chen-Yu Lee Tomas Pfister ALM 204 498 0 03 May 2023
Scaling Laws for Neural Language Models Jared Kaplan Sam McCandlish T. Henighan Tom B. Brown B. Chess R. Child Scott Gray Alec Radford Jeff Wu Dario Amodei 220 4,424 0 23 Jan 2020