Previous chapters established how to quantize Large Language Models (LLMs) and deploy them using standard toolkits and frameworks. However, achieving optimal results often requires tackling specific, non-trivial issues that arise during implementation. This chapter concentrates on these advanced difficulties.

You will learn practical strategies for addressing accuracy degradation, especially when employing aggressive low-bit quantization schemes (e.g., sub-$INT4$). We will cover methods for identifying and managing problematic outlier values in weights and activations, which can significantly affect quantization fidelity. Furthermore, this chapter examines the impact of hardware capabilities and kernel availability on performance, compares static versus dynamic quantization approaches for different deployment needs, and provides techniques for systematically debugging issues encountered during the quantization process. Finally, we'll discuss integrating these optimized models effectively into production environments.