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# Supported ROCm and NVIDIA CUDA functions

Here is a list of the ROCm and NVIDIA CUDA functions supported by hipBLASLt:

- ROCm

  - AMD sparse MFMA matrix core support

  - Mixed-precision computation support:

    - `FP16` input/output, `FP32` matrix core accumulate

    - `BFLOAT16` input/output, `FP32` matrix core accumulate

    - `INT8` input/output, `INT32` matrix core accumulate

    - `INT8` input, `FP16` output, `INT32` matrix core accumulate

    - `FP8` input, `FP32` output, `FP32` matrix core accumulate

    - `BF8` input, `FP32` output, `FP32` matrix core accumulate

  - Matrix pruning and compression functionalities

  - Auto-tuning functionality (see `hipsparseLtMatmulSearch()`)

  - Batched sparse GEMM support:

    - Single sparse matrix/multiple dense matrices (broadcast)

    - Multiple sparse and dense matrices

    - Batched bias vector

  - Activation function fuse in SpMM kernel support:

    - ReLU

    - ClippedReLU (ReLU with upper bound and threshold setting)

    - GeLU

    - GeLU scaling (implied enable GeLU)

    - Abs

    - LeakyReLU

    - Sigmoid

    - Tanh

  - Ongoing feature development

    - Add support for mixed-precision computation:

      - `FP8` input/output, `FP32` matrix core accumulate

      - `BF8` input/output, `FP32` matrix core accumulate

      - Add kernel selection and generator, used to provide the appropriate solution for the specific problem

- CUDA

  - Support for CUDA cuSPARSELt v0.6.3