Big brain, tiny suitcase.
Quantization reduces the precision of numbers in a neural network to make it faster and smaller, like rounding 3.14159 to 3.14.
Imagine you're packing for a trip and need to fit everything into a tiny suitcase. Instead of bringing your entire wardrobe with every shirt in every color, you pick just a few essential outfits. Quantization does the same for AI models—it trims down the numbers so the model can run on your phone or a small device without losing too much smarts.
It's why your phone can run AI features like real-time language translation or photo editing without needing a supercomputer. Without quantization, most AI models would be too big and slow to use on everyday gadgets.
People often think quantization makes the AI dumber or less accurate. In reality, it trades a tiny bit of precision for huge gains in speed and size, and the model usually performs almost as well.
Quantization is the process of mapping a large set of continuous or high-precision values (e.g., 32-bit floating point) to a smaller set of discrete values (e.g., 8-bit integers). In deep learning, it reduces the memory footprint and computational cost of neural network weights and activations, enabling deployment on resource-constrained hardware like mobile devices and edge processors.