Precision Where It Matters: A Novel Spike Aware Mixed-Precision Quantization Strategy for LLaMA-based Language Models (2504.21553v1)

Abstract: LLMs have demonstrated remarkable capabilities in various natural language processing tasks. However, their size presents significant challenges for deployment and inference. This paper investigates the quantization of LLMs, focusing on the LLaMA architecture and its derivatives. We challenge existing assumptions about activation outliers in LLMs and propose a novel mixed-precision quantization approach tailored for LLaMA-like models. Our method leverages the observation that activation spikes in LLaMA architectures are predominantly concentrated in specific projection layers. By applying higher precision (FP16 or FP8) to these layers while quantizing the rest of the model to lower bit-widths, we achieve superior performance compared to existing quantization techniques. Experimental results on LLaMA2, LLaMA3, and Mistral models demonstrate significant improvements in perplexity and zero-shot accuracy, particularly for 8-bit per-tensor quantization. Our approach outperforms general-purpose methods designed to handle outliers across all architecture types, highlighting the benefits of architecture-specific quantization strategies. This research contributes to the ongoing efforts to make LLMs more efficient and deployable, potentially enabling their use in resource-constrained environments. Our findings emphasize the importance of considering model-specific characteristics in developing effective quantization pipelines for state-of-the-art LLMs by identifying and targeting a small number of projections that concentrate activation spikes.

Precision Where It Matters: A Novel Spike Aware Mixed-Precision Quantization Strategy for LLaMA-based LLMs

The paper "Precision Where It Matters: A Novel Spike Aware Mixed-Precision Quantization Strategy for LLaMA-based LLMs" addresses a critical aspect of deploying LLMs—the challenge posed by their enormous size. As LLMs like LLaMA gain prominence for their remarkable capabilities in NLP, the computational demands for storage and inference become significant obstacles. This research contributes to ongoing efforts to make LLMs more efficient by proposing a novel quantization strategy that could potentially enable their deployment in resource-constrained environments.

Overview

The authors introduce a mixed-precision quantization approach specifically designed for LLaMA-like architectures. The strategy departs from general-purpose methods by targeting activation spikes—outliers typically concentrated in specific projection layers. By applying higher precision formats such as FP16 or FP8 to these layers while quantizing the rest of the model to lower bit-widths, the paper reports superior performance compared to existing techniques. This approach is particularly advantageous for 8-bit per-tensor quantization, highlighting the benefits of architecture-specific strategies.

Numerical Results and Claims

Experimental results on LLaMA2, LLaMA3, and Mistral models indicate substantial improvements in perplexity and zero-shot accuracy. The advantage of the proposed method is pronounced in scenarios requiring aggressive quantization, such as 8-bit settings, while maintaining competitive performance in 6-bit configurations despite some instability. The success of this strategy supports the notion that targeted precision adjustments can mitigate the adverse effects of activation spikes without necessitating comprehensive model-wide considerations.

Implications and Future Directions

The implications of this research are twofold. Practically, the mixed-precision approach offers a pathway to reducing the environmental impact of training and deploying large-scale models by lessening their computational and energy footprints. Theoretically, the findings underscore the importance of considering model-specific characteristics when developing quantization pipelines. This tailored approach could serve as a foundation for future investigations into quantization strategies tailored to different architectural families or training paradigms.

Moving forward, the scope of this research could be expanded by exploring similar techniques in other model families and addressing the remaining instability observed in more aggressive quantization scenarios. Additionally, combining the mixed-precision method with other established techniques might yield further enhancements in model efficiency. Such developments would be instrumental in meeting the growing computational demands associated with deploying state-of-the-art LLMs.