CCCL block_scan_raking.cuh: parallel prefix scan over C elements using
GPU-native raking threads, not sequential host-driven loops.
Our _forward_sub_lower was a Python for-loop over chunk_size=64 rows,
each launching a separate matmul kernel. This is 64 sequential kernel
launches per DeltaNet layer per chunk.
Fix: Use torch.linalg.solve_triangular (cuBLAS trsm) which solves
the entire (I-A)@X=RHS system in ONE kernel launch. Falls back to
the Python loop if cuSOLVER is unavailable on BI-V100.
CCCL source: cub/cub/block/specializations/block_scan_raking.cuh
Maps to: qwen3_6_scripts/qwen3_5.py (_forward_sub_lower)