Harnessing On-Device Large Language Model: Empirical Results and Implications for AI PC

• URL: http://arxiv.org/abs/2505.15030v3
• Date: Sat, 07 Jun 2025 02:45:47 GMT
• Title: Harnessing On-Device Large Language Model: Empirical Results and Implications for AI PC
• Authors: Qingyu Song, Peiyu Liao, Wenqian Zhao, Yiwen Wang, Shoubo Hu, Hui-Ling Zhen, Ning Jiang, Mingxuan Yuan,
• Abstract summary: Large Language Models (LLMs) on edge devices offer significant privacy benefits.<n>These on-device LLMs inherently face performance limitations due to reduced model capacity and necessary compression techniques.<n>We introduce a systematic methodology -- encompassing model capability, development efficiency, and system resources -- for evaluating on-device LLMs.
• Score: 8.837470787975308
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The increasing deployment of Large Language Models (LLMs) on edge devices, driven by model advancements and hardware improvements, offers significant privacy benefits. However, these on-device LLMs inherently face performance limitations due to reduced model capacity and necessary compression techniques. To address this, we introduce a systematic methodology -- encompassing model capability, development efficiency, and system resources -- for evaluating on-device LLMs. Our comprehensive evaluation, encompassing models from 0.5B to 14B parameters and seven post-training quantization (PTQ) methods on commodity laptops, yields several critical insights: 1) System-level metrics exhibit near-linear scaling with effective bits-per-weight (BPW). 2) A practical threshold exists around $\sim$3.5 effective BPW, larger models subjected to low-bit quantization consistently outperform smaller models utilizing higher bit-precision. 3) Quantization with low BPW incurs marginal accuracy loss but significant memory savings. 4) Determined by low-level implementation specifics power consumption on CPU, where computation-intensive operations spend more power than memory-intensive ones. These findings offer crucial insights and practical guidelines for the efficient deployment and optimized configuration of LLMs on resource-constrained edge devices. Our codebase is available at https://github.com/simmonssong/LLMOnDevice.

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