# Quantized attention

AMD introduced Fused-Softmax Attention, a training-free algorithm for the CDNA 4 architecture that folds the dominant softmax operations into attention's matrix multiplies so low-precision matrix throughput is actually used. On an MI355X GPU the MXFP8, MXFP6 and MXFP4 kernels reach about 3.0, 4.0 and 4.6 PFLOP/s, while the added error stays below the error floor set by quantizing the GEMM operands. On Wan2.2-T2V-A14B a hybrid MXFP6/4 schedule delivers up to end-to-end speedup while preserving visual quality, and the kernels ship in AMD's open-source AITER library.

AMD introduced Fused-Softmax Attention, a training-free algorithm for the CDNA 4 architecture that folds the dominant softmax operations into attention's matrix multiplies so low-precision matrix throughput is actually used. On an MI355X GPU the MXFP8, MXFP6 and MXFP4 kernels reach about 3.0, 4.0 and 4.6 PFLOP/s, while the added error stays below the error floor set by quantizing the GEMM operands. On Wan2.2-T2V-A14B a hybrid MXFP6/4 schedule delivers up to end-to-end speedup while preserving visual quality, and the kernels ship in AMD's open-source AITER library.