d9bbef54d8
Previous commit broadcast Q@K^T (saved 1GB/step). This commit broadcasts scores@V too (saves 2GB/step). Before: V expanded from [kv_h, padded_len, d] to [H, padded_len, d] 4×100K×256×4B → 24×100K×256×4B = 400MB → 2.4GB allocation After: broadcast matmul at kv_h level se: [kv_h, gqa, P, 1, part_sz] @ V: [kv_h, 1, P, part_sz, d] → [kv_h, gqa, P, 1, d] → reshape to [H, P, d] V stays at kv_h size: 400MB (no 2.4GB allocation) Total per-decode-step memory for 100K context: Before all GQA opts: 3.6GB (K expansion + V expansion) After: 600MB (6x total reduction from GQA ratio=6) This is the CCCL insight applied: transform_reduce with a compound type. Instead of expanding to full head count then reducing, keep the reduction at the minimal group size and broadcast the grouping dimension.