What is Model Quantization in Deep Learning and how does it improve inference performance?

Model Quantization reduces the numerical precision of neural network parameters and computations to improve inference speed, memory efficiency, and deployment scalability.

Deep learning models are often computationally expensive because they use high-precision floating-point operations, typically FP32. Large models with billions of parameters consume massive memory and require expensive hardware acceleration.

Quantization addresses this by converting weights and activations into lower-precision formats such as:

Core principle: Instead of storing weights like: 32-bit floating point

The model stores compressed lower-bit representations.

Benefits:

3.…