NVIDIA outlined three cuTile kernels for Mamba-2 and said optimized forward-pass code ran 1.14x to 1.82x faster than a Triton reference.
NVIDIA said it has used its cuTile Python DSL to implement and optimize the forward pass of Mamba-2, a sequence model used by Jane Street engineers in an early adoption effort. The Oct. 19, 2026 post describes three custom kernels and reports that the optimized cuTile version ran 1.14x to 1.82x faster than an open source Triton implementation in the benchmark setup NVIDIA disclosed.
According to NVIDIA’s Oct. 19, 2026 developer blog, cuTile is a Python-based DSL and programming model for NVIDIA GPUs that abstracts thread- and warp-level scheduling behind tile-centric primitives. The company said the Mamba-2 work was done with Jane Street, which the post described as a deeply technical global trading firm using deep learning in quantitative research.
The article frames Mamba-2 as an architecture intended to balance the training efficiency of attention models with the fixed-memory inference characteristics of recurrent networks. NVIDIA said the forward pass was split into three kernels: chunk_cumsum to compute intra-chunk cumulative sums, fwd_pass_state to carry hidden state across chunks, and compute_out to fuse output generation while keeping matrix operations Tensor Core-friendly.
mma . NVIDIA also said the implementation uses log-space reformulations, causal masking, and exp2-based scaling to preserve numerical stability while keeping computation in tiled matrix form rather than a memory-heavy scan.
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