LSAQ introduces a novel Layer-Specific Adaptive Quantization system for LLMs, using Jaccard similarity to assess layer importance and dynamically adjusting quantization precision based on edge device resources, achieving superior accuracy on zero-shot tasks and lower perplexity compared to baseline methods while enabling efficient deployment.
Large Language Model, Efficiency, Pre-training, Multimodal Systems
Binrui Zeng, Bin Ji, Xiaodong Liu, Jie Yu, Shasha Li, Jun Ma, Xiaopeng Li, Shangwen Wang, Xinran Hong, Yongtao Tang
National University of Defense Technology, Changsha, China
Generated by grok-3
Background Problem
The rapid advancement of Large Language Models (LLMs) has led to exceptional performance across various domains, but their massive parameter sizes pose significant challenges for deployment on resource-limited edge devices, such as personal computers or consumer-grade GPUs. Existing quantization techniques, particularly Post-Training Quantization (PTQ), reduce memory requirements by converting high-precision weights to lower-precision formats, yet most methods apply uniform quantization across all layers, ignoring varying layer importance and failing to dynamically adapt to diverse computational resources on edge devices. This limitation restricts the flexibility and efficiency of LLM deployment. The paper introduces Layer-Specific Adaptive Quantization (LSAQ) to address these issues by proposing a novel method to evaluate layer importance and adaptively adjust quantization strategies based on available resources, aiming to optimize storage and inference efficiency while maintaining model performance.
Method
LSAQ is a system for adaptive quantization and dynamic deployment of LLMs on edge devices, focusing on layer-specific strategies based on importance. Its core idea is to assess layer importance using Jaccard similarity between top-k token sets derived from the input and output hidden states of each layer, where a lower similarity indicates higher importance due to greater semantic transformation. The method operates in offline and online phases: offline, it evaluates layer importance by projecting hidden states to vocabulary space using an embedding matrix, constructing top-k token sets, and computing Jaccard similarity as per ; it also detects available GPU resources. Based on this, a quantization strategy is formulated, allocating higher precision (e.g., INT8) to more important layers and lower precision (e.g., INT4) to less important ones, maximizing high-precision layers within resource constraints. Online, the model is quantized per-channel using scaling factors to minimize errors, as in , and deployed. This adaptive approach ensures efficient resource utilization while preserving critical model capabilities.
Experiment
The experiments were conducted on Llama-2-7B, Llama-2-13B, and Llama-3-8B models, evaluating performance on six zero-shot tasks (PIQA, ARC-e, ARC-c, BoolQ, HellaSwag, WinoGrande) for reasoning and generalization, and perplexity on the WikiText2 dataset for predictive accuracy. The setup tested quantization at average bit-widths of 7, 6, and 5 bits by applying INT4 to 25%, 50%, and 75% of layers respectively, with INT8 for the rest, comparing LSAQ against Layer-Wise Quantization (LWQ) using cosine similarity. Results showed LSAQ outperforming LWQ in average accuracy across zero-shot tasks in 87.5% of cases (except for Llama-2-7B at 7 bits due to consistent layer importance ranking) and achieving lower perplexity in most scenarios, indicating better preservation of model capabilities. Deployment tests demonstrated significant memory reduction (e.g., Llama-2-7B from 12.82GB at FP16 to 3.56GB at lower bits), enabling deployment on mainstream GPUs. The experimental design is comprehensive in model and task selection, and results align with expectations of maintaining performance via adaptive quantization. However, the lack of latency or energy consumption metrics and limited exploration of quantization split ratios suggest gaps in real-world applicability assessment.
Further Thoughts
The LSAQ approach opens intriguing avenues for further exploration, particularly in the context of real-time adaptive systems beyond static quantization. Could the Jaccard similarity metric be extended to dynamically adjust during inference based on input data characteristics, potentially integrating with online learning paradigms to refine layer importance over time? This could address potential misjudgments in static assessments seen in edge cases like Llama-2-7B at 7-bit quantization. Additionally, connecting LSAQ with federated learning could enable collaborative importance assessment across distributed edge devices, balancing privacy and performance—how would layer importance vary across diverse user data distributions? Another insight relates to energy efficiency, an underexplored aspect in the paper; integrating LSAQ with AI for Science initiatives could optimize not just memory but also power consumption on edge hardware, critical for sustainable AI deployment. Finally, comparing LSAQ’s semantic focus with methods in vision foundation models might reveal cross-modal quantization strategies, where semantic transformation metrics could guide compression in multimodal systems, potentially unifying deployment challenges across domains.