This paper provides a comprehensive overview of the principles, challenges, and methodologies associated with quantizing large-scale neural network models. As neural networks have evolved towards larger and more complex architectures to address increasingly sophisticated tasks, the computational and energy costs have escalated significantly. We explore the necessity and impact of model size growth, highlighting the performance benefits as well as the computational challenges and environmental considerations. The core focus is on model quantization as a fundamental approach to mitigate these challenges by reducing model size and improving efficiency without substantially compromising accuracy. We delve into various quantization techniques, including both post-training quantization (PTQ) and quantization-aware training (QAT), and analyze several state-of-the-art algorithms such as LLM-QAT, PEQA(L4Q), ZeroQuant, SmoothQuant, and others. Through comparative analysis, we examine how these methods address issues like outliers, importance weighting, and activation quantization, ultimately contributing to more sustainable and accessible deployment of large-scale models.

本论文系统性地概述了大规模神经网络模型量化的原则、挑战和方法，指出了模型规模增长所带来的计算成本和能源开销问题。通过深入探讨量化技术，包括后训练量化（PTQ）和量化感知训练（QAT），论文展示了如何在不显著影响精度的情况下，降低模型规模并提高效率，为可持续和可获取的大规模模型部署提供了重要影响。

大规模模型量化的艺术与科学：全面概述