A new Nvidia patent application describes a compiler that identifies redundant attention computations. Transformer models use masks to exclude certain relationships between tokens. GPUs, however, process the corresponding matrix in blocks. That can leave unnecessary work even when many values are masked out.
US application US 2026/0259730 A1 was published on September 3.
Bounds instead of cell-by-cell checks
The compiler analyzes a specified mask rule at compile time. It generates left and right bounding functions for the valid region of the attention matrix. The GPU kernel can then skip fully masked tiles without checking every cell separately.
- More flexible: Custom masks would also be supported.
- Less work: Unneeded values are never computed.
- Same output: Only fully masked tiles may be omitted.
Assessment: The mask type matters
The approach addresses a significant cost factor in large language and image models. Potential gains depend on mask structure, tile size and compiler overhead. Irregular masks may leave many partially occupied tiles. The application describes a mechanism but provides no public benchmark or evidence of integration into CUDA or a shipping Nvidia product. A patent grant is also still pending.