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MambaBlock::forward already had a working, tested CUDA dispatch (forward_cuda: cuBLAS matmuls for projections, CPU for the scan). backward() had none — it silently ran entirely CPU-serial on GPU tensors via to_vec()/from_vec() D2H/H2D round-trips. This adds the missing acceleration, mirroring forward_cuda's hybrid split: the four large projection-parameter gradients (in_proj, x_proj, dt_proj, out_proj) now go through batched GPU matmuls; the inherently sequential scan reverse-pass and small per-channel grads stay CPU. Extracted CpuWeights::pull and recompute_forward_cpu out of the old inline per-batch forward-recompute block inside backward() (pure refactor, gradient-checked unchanged by real_selective_scan.rs's existing 6 tests) so CPU backward and the new CUDA backward share identical forward math and can never numerically diverge on it. New CUDA-vs-CPU gradient-check test (mamba_cuda_backward_matches_cpu, #[ignore]-gated, GPU-only) caught a real bug during development: Tensor::contiguous() is a no-op stub in this rtx-tensor version, and cuda_matmul reads raw GPU storage by shape.dims() ignoring strides/offset, so .transpose(..).matmul(..) on a GPU tensor silently computed garbage (80-200x relative error on 3 of 4 accelerated gradients). Fixed by building already-transposed [dim, b*l] buffers on CPU before upload instead of transposing GPU-side. All 9 gradients now match CPU backward within ~2.2e-5 max relative error (tolerance 1e-4). Co-Authored-By: Claude Sonnet 5 <[email protected]>