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# Data type support

This topic lists the supported data types for the hipBLASLt GEMM operation, which is performed by hipblasLtMatmul().

The `hipDataType` enumeration defines data precision types and is primarily used when the data reference itself does not include type information, such as in `void*` pointers. This enumeration is mainly utilized in BLAS libraries.

The hipBLASLt input and output types are listed in the following table.

hipDataType | hipBLASLt type | Description
--- | --- | ---
`HIP_R_8I` | `hipblasLtInt8` | 8-bit real signed integer.
`HIP_R_32I` | `hipblasLtInt32` | 32-bit real signed integer.
`HIP_R_4F_E2M1` | N/A | 4-bit real float4 precision floating-point
`HIP_R_6F_E2M3` | N/A | 6-bit real float6 precision floating-point
`HIP_R_6F_E3M2` | N/A | 6-bit real bfloat6 precision floating-point
`HIP_R_8F_E4M3_FNUZ` | `hipblaslt_f8_fnuz` | 8-bit real float8 precision floating-point
`HIP_R_8F_E5M2_FNUZ` | `hipblaslt_bf8_fnuz` | 8-bit real bfloat8 precision floating-point
`HIP_R_8F_E4M3` | `hipblaslt_f8` | 8-bit real float8 precision floating-point
`HIP_R_8F_E5M2` | `hipblaslt_bf8` | 8-bit real bfloat8 precision floating-point
`HIP_R_16F` | `hipblasLtHalf` | 16-bit real half precision floating-point
`HIP_R_16BF` | `hipblasLtBfloat16` | 16-bit real bfloat16 precision floating-point
`HIP_R_32F` | `hipblasLtFloat` | 32-bit real single precision floating-point

Note

The `hipblaslt_f8_fnuz` and `hipblaslt_bf8_fnuz` data types are only supported on the gfx942 platform. The `hipblaslt_f8` and `hipblaslt_bf8` data types are only supported on the gfx950 and gfx12 platforms.

The hipBLASLt compute modes are listed in the following table.

hipDataType | Description
--- | ---
`HIPBLAS_COMPUTE_32I` | 32-bit integer compute mode.
`HIPBLAS_COMPUTE_16F` | 16-bit half precision floating-point compute mode.
`HIPBLAS_COMPUTE_32F` | 32-bit singple precision floating-point compute mode.
`HIPBLAS_COMPUTE_64F` | 64-bit double precision floating-point compute mode.
`HIPBLAS_COMPUTE_32F_FAST_16F` | Enables the library to utilize Tensor Cores with 32-bit float computation for matrices with 16-bit half precision input and output.
`HIPBLAS_COMPUTE_32F_FAST_16BF` | Enables the library to utilize Tensor Cores with 32-bit float computation for matrices with 16-bit bfloat16 precision input and output.
`HIPBLAS_COMPUTE_32F_FAST_TF32` | Enables the library to utilize Tensor Cores with TF32 computation for matrices with 32-bit input and output.

## Data type combinations

hipBLASLt supports various combinations of input (A, B), accumulation (C), output (D), and compute data types for GEMM operations. The library enables mixed-precision operations, allowing you to use lower precision inputs with higher precision compute for optimal performance while maintaining accuracy where needed.

The GEMM operation follows this equation:

\[D = Activation(alpha \cdot op(A) \cdot op(B) + beta \cdot op(C) + bias)\]

Where \(op( )\) refers to in-place operations, such as transpose and non-transpose, and \(alpha\) and \(beta\) are scalars.

For complete details on supported data type combinations, including specific compute types, scale types, and bias configurations, see the hipBLASLt API reference page.

For more information about data type support for the other ROCm libraries, see Data types and precision support page.