LSAQ: Layer-Specific Adaptive Quantization for Large Language Model Deployment

• URL: http://arxiv.org/abs/2412.18135v1
• Date: Tue, 24 Dec 2024 03:43:15 GMT
• Title: LSAQ: Layer-Specific Adaptive Quantization for Large Language Model Deployment
• Authors: Binrui Zeng, Bin Ji, Xiaodong Liu, Jie Yu, Shasha Li, Jun Ma, Xiaopeng Li, Shangwen Wang, Xinran Hong,
• Abstract summary: We propose LSAQ (Layer-Specific Adaptive Quantization), a system for adaptive quantization and dynamic deployment of large language models (LLMs) based on layer importance.<n>The system adaptively adjusts quantization strategies in real time according to the resource availability of edge devices, assigning different precision levels to layers of varying importance.
• Score: 13.235417359529965
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: As large language models (LLMs) demonstrate exceptional performance across various domains, the deployment of these models on edge devices has emerged as a new trend. Quantization techniques, which reduce the size and memory footprint of LLMs, are effective for enabling deployment on resource-constrained edge devices. However, existing one-size-fits-all quantization methods often fail to dynamically adjust the memory consumption of LLMs based on specific hardware characteristics and usage scenarios. To address this limitation, we propose LSAQ (Layer-Specific Adaptive Quantization), a system for adaptive quantization and dynamic deployment of LLMs based on layer importance. LSAQ evaluates layer importance by constructing top-k token sets from the inputs and outputs of each layer and calculating their Jaccard coefficient. Using this evaluation, the system adaptively adjusts quantization strategies in real time according to the resource availability of edge devices, assigning different precision levels to layers of varying importance. This approach significantly reduces the storage requirements of LLMs while maintaining model performance, enabling efficient deployment across diverse hardware platforms and usage scenarios.

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