Internals of FP8 MMA on NVIDIA Hopper

The author examines the internals of FP8 Matrix Multiply-Accumulate (MMA) operations on NVIDIA Hopper architectures, highlighting why accumulator promotion and scaling are necessary to mitigate precision loss. Using a bitwise MMA-Sim model alongside GPU kernel verification, the post demonstrates that Hopper's hardware rounding and cutoff behavior lead to accuracy degradation over chained instructions compared to Blackwell architectures. Open article →