An Empirical Analysis and Resource Footprint Study of Deploying Large Language Models on Edge Devices-Reference-Cited by-同舟云学术

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An Empirical Analysis and Resource Footprint Study of Deploying Large Language Models on Edge Devices

Published:2024-04-18 Issue: Volume: Page:69-76
ISSN:
Container-title:Proceedings of the 2024 ACM Southeast Conference on ZZZ
language:
Short-container-title:

Author:

Dhar Nobel¹^ORCID,Deng Bobin¹^ORCID,Lo Dan¹^ORCID,Wu Xiaofeng²^ORCID,Zhao Liang¹^ORCID,Suo Kun¹^ORCID

Affiliation:

1. Kennesaw State University, Marietta, Georgia, USA

2. City University of Macau, Macao, Macau

Funder

National Science Foundation

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3603287.3651205

Reference32 articles.

1. [n. d.]. Jetson AGX Orin. https://www.nvidia.com/en-us/autonomous-machines/embedded-systems/jetson-orin/.

2. [n.d.]. Port of Facebook's LLaMA Model in C/C++. https://github.com/ggerganov/llama.cpp.

3. 2023. A New Foundation for AI on Android. https://android-developers.googleblog.com/2023/12/a-new-foundation-for-ai-on-android.html.

4. 2023. Qualcomm Works with Meta to Enable On-device AI Applications Using Llama 2. https://www.qualcomm.com/news/releases/2023/07/qualcomm-works-with-meta-to-enable-on-device-ai-applications-usi.

5. 2023. Samsung Looks Towards AI For The Galaxy S24. https://www.forbes.com/sites/ewanspence/2023/11/13/samsung-galaxys24-ultra-generative-ai-qualcomm-snapdragon-exynos-2400/?sh=6a019d2b3fba.

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