4-Bit Quantization for Inference Optimization

This post details the optimization of the FLUX.2 [dev] image generation model using 4-bit quantization for inference. It explains the model's architecture, identifies matrix multiplication (GEMM) as the primary bottleneck (67% of inference time), and describes the process of quantizing BFloat16 weights to FP4. The explanation includes the mathematical basis for linear mapping between unquantized and quantized values, highlighting how reduced precision increases effective bandwidth and FLOPS, leading to a 1.6x speed improvement with negligible quality loss.