A Performance Evaluation of a Quantized Large Language Model on Various Smartphones

• URL: http://arxiv.org/abs/2312.12472v1
• Date: Tue, 19 Dec 2023 10:19:39 GMT
• Title: A Performance Evaluation of a Quantized Large Language Model on Various Smartphones
• Authors: Tolga \c{C}\"opl\"u, Marc Loedi, Arto Bendiken, Mykhailo Makohin, Joshua J. Bouw, Stephen Cobb (Haltia, Inc.)
• Abstract summary: This paper explores the feasibility and performance of on-device large language model (LLM) inference on various Apple iPhone models. Leveraging existing literature on running multi-billion parameter LLMs on resource-limited devices, our study examines the thermal effects and interaction speeds of a high-performing LLM. We present real-world performance results, providing insights into on-device inference capabilities.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper explores the feasibility and performance of on-device large language model (LLM) inference on various Apple iPhone models. Amidst the rapid evolution of generative AI, on-device LLMs offer solutions to privacy, security, and connectivity challenges inherent in cloud-based models. Leveraging existing literature on running multi-billion parameter LLMs on resource-limited devices, our study examines the thermal effects and interaction speeds of a high-performing LLM across different smartphone generations. We present real-world performance results, providing insights into on-device inference capabilities.

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