Quantization is the process of reducing the precision of weights and activations in neural networks from 32-bit floating point to lower bit-width representations (16-bit, 8-bit, or even 4-bit).
Core Benefits:
• 4x smaller models - FP32 → INT8
• Faster inference - Integer ops are faster
• Lower memory usage - Less RAM required
• Energy efficient - Crucial for mobile/edge