# TileGym

Machine learning engineers at Jane Street, working with NVIDIA, rewrote the Mamba-2 forward pass in NVIDIA CUDA Tile (cuTile), a Python-based GPU programming model, and tuned it for Blackwell Tensor Cores. Mamba-2 combines a fixed-size recurrent state with attention-style matrix multiplication, but its forward pass still needs efficient kernels. On a single B300, the kernels run 1.14x to 1.82x faster than a Triton implementation, with speedups growing as sequence length and state size increase; these are kernel-level results, and end-to-end training also depends on the backward pass.

Machine learning engineers at Jane Street, working with NVIDIA, rewrote the Mamba-2 forward pass in NVIDIA CUDA Tile (cuTile), a Python-based GPU programming model, and tuned it for Blackwell Tensor Cores. Mamba-2 combines a fixed-size recurrent state with attention-style matrix multiplication, but its forward pass still needs efficient kernels. On a single B300, the kernels run 1.14x to 1.82x faster than a Triton implementation, with speedups growing as sequence length and state size increase; these are kernel-level results, and end-to-end training also depends on the backward pass.