Model Quantization for Inference

This post provides a practical guide to model quantization for AI inference. It explains what quantization is, how it speeds up inference by reducing data movement and computation, and when it is most effective. The post details different quantization formats (FP32, FP16, INT8, INT4), their impact on accuracy and performance, and discusses scenarios where quantization might not be beneficial. It also contrasts quantization with other inference optimization techniques like batching and caching, highlighting the trade-offs between speed, memory usage, and accuracy. The post also touches on the role of hardware support and software overhead in determining the effectiveness of quantization, and how it complements other optimization techniques like caching.