Integer Quantization: Deep Dive

Quantization reduces neural network model size and energy consumption by representing weights and activations using fewer bits (e.g., INT8 instead of FP16), enabling models like a 70B parameter transformer to fit on a single GPU while consuming significantly less memory and energy than floating-point operations. The process involves mapping high-precision real values to a lower-precision integer grid using a scale factor and zero-point offset, with hardware accelerators using specialized Multiply-Accumulate units that benefit from integer arithmetic's reduced computational cost and silicon footprint compared to floating-point operations.