The Krea 2 PR (#14523) said no fused SDPA kernel takes a mask together with
mismatched head counts and that cuDNN raises on `enable_gqa`. Neither is
right: torch's cuDNN kernel accepts both, and the `enable_gqa` raises in
`attention_dispatch.py` are in the context-parallel ops, not the backends.
What actually happens is that torch tries its kernels in a priority order
that changes across versions and GPUs, and whenever math comes before cuDNN
an `enable_gqa` + mask call lands on math and materializes the full score
matrix.

Rewrite the GQA section of models.md around that (kernel compatibility
table, why to repeat when there is a mask) and fix the Krea 2 processor
comment to match.

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>