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Fix error in Blackwell document of referring to Mxf4 format as NVF4 (#2977)

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* Update blackwell_functionality.md

Fixed the error of referring to Mxf4 format as NVF4.

* Correct NVF4 output matrix reference in documentation
This commit is contained in:
Ziang Li
2026-02-27 00:25:16 -08:00
committed by GitHub
parent de67bb7a42
commit 518327d631
1 changed files with 2 additions and 2 deletions
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media/docs/cpp/blackwell_functionality.md
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@@ -443,11 +443,11 @@ which are listed above.
Several examples of block scaled dense GEMM kernels can be found in [examples/72_blackwell_narrow_precision_gemm](https://github.com/NVIDIA/cutlass/tree/main/examples/72_blackwell_narrow_precision_gemm/) directory:
1. [NVF4 Gemm with block scaling](https://github.com/NVIDIA/cutlass/tree/main/examples/72_blackwell_narrow_precision_gemm/72a_blackwell_nvfp4_bf16_gemm.cu)
2. [NVF4 Gemm with block scaling and NVF4 output matrix](https://github.com/NVIDIA/cutlass/tree/main/examples/72_blackwell_narrow_precision_gemm/72b_blackwell_nvfp4_nvfp4_gemm.cu)
3. [Mixed precision Nvf4 x Mxf8 GEMM with block scaling](https://github.com/NVIDIA/cutlass/tree/main/examples/72_blackwell_narrow_precision_gemm/72c_blackwell_mixed_mxfp8_bf16_gemm.cu)
3. [Mixed precision Mxf4 x Mxf8 GEMM with block scaling](https://github.com/NVIDIA/cutlass/tree/main/examples/72_blackwell_narrow_precision_gemm/72c_blackwell_mixed_mxfp8_bf16_gemm.cu)
Several examples of block scaled sparse GEMM kernels can be found in [examples/84_blackwell_narrow_precision_sparse_gemm](https://github.com/NVIDIA/cutlass/tree/main/examples/84_blackwell_narrow_precision_sparse_gemm) directory:
1. [NVF4 Gemm with block scaling](https://github.com/NVIDIA/cutlass/tree/main/examples/84_blackwell_narrow_precision_sparse_gemm/84a_blackwell_nvfp4_bf16_sparse_gemm.cu)
2. [Mixed precision Nvf4 x Mxf8 GEMM with block scaling](https://github.com/NVIDIA/cutlass/tree/main/examples/84_blackwell_narrow_precision_sparse_gemm/84b_blackwell_mixed_mxfp8_bf16_sparse_gemm.cu)
2. [Mixed precision Mxf4 x Mxf8 GEMM with block scaling](https://github.com/NVIDIA/cutlass/tree/main/examples/84_blackwell_narrow_precision_sparse_gemm/84b_blackwell_mixed_mxfp8_bf16_sparse_gemm.cu)
Collective builder interface expects the same arguments as any other CUTLASS 3.x kernels as described
[here](gemm_api_3x.md#collective-builder-for-collectivemmas) with a small difference for Collective MMA builder interface.