After applying quantization techniques to reduce model size and computational cost, the next step is to rigorously assess the outcome and prepare the model for practical use. Quantization often involves a trade-off between efficiency and predictive performance, making careful evaluation essential.

This chapter focuses on these critical final stages. You will learn how to measure the impact of quantization on model quality using metrics such as perplexity and task-specific accuracy benchmarks. We will cover practical methods for benchmarking inference speed and memory consumption on relevant hardware. Furthermore, we will discuss hardware considerations that influence performance, strategies for deploying quantized models in various environments (cloud, edge), and techniques for troubleshooting common quantization-related issues. A key outcome is understanding how to analyze the trade-off between model performance gains like reduced latency $L$ or smaller memory footprint $M$, and potential accuracy reduction, enabling informed decisions for real-world applications.