NVIDIA’s Mamba-2 Kernels Run Up to 1.82 Times Faster in Tests

NVIDIA outlined a cuTile implementation of Mamba-2 that ran 1.14 to 1.82 times faster than a Triton reference in forward-pass tests, offering a measure of kernel performance on one Blackwell Ultra GPU rather than a full training run. Jane Street engineers worked with NVIDIA to build and optimize the code using cuTile, NVIDIA’s Python interface for GPU programming. The comparison used a single NVIDIA B300 SXM6 GPU. It tested the cuTile kernels against an open-source Mamba-2 implementation written in Triton. Mamba-2 processes sequences in chunks and carries a fixed-size hidden state from one chunk to the next. The architecture is designed to use matrix operations efficiently during training while keeping memory requirements fixed during inference. Jane Street uses deep learning in quantitative research.

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