Why is quantization of models necessary before deployment?

Model quantization is the process of converting neural network weights and activations from a floating-point format (e.g., float32) to a more compact format with lower precision (e.g., int8).

Why is this needed before deployment:

• Reducing the model size — saving memory and disk space.
• Speeding up inference — integer operations are faster on many devices, especially on mobile and embedded systems.
• Lowering power consumption — important for resource-constrained devices.

At the same time, quantization may slightly reduce the model's accuracy, but modern methods minimize this effect while maintaining acceptable quality.