Data types and precision support#

2025-07-31

9 min read time

Applies to Linux and Windows

This topic lists the data types support on AMD GPUs, ROCm libraries along with corresponding HIP data types.

Integral types#

The signed and unsigned integral types supported by ROCm are listed in the following table.

Type name	HIP type	Description
int8	`int8_t`, `uint8_t`	A signed or unsigned 8-bit integer
int16	`int16_t`, `uint16_t`	A signed or unsigned 16-bit integer
int32	`int32_t`, `uint32_t`	A signed or unsigned 32-bit integer
int64	`int64_t`, `uint64_t`	A signed or unsigned 64-bit integer

Floating-point types#

The floating-point types supported by ROCm are listed in the following table.

Type name	HIP type	Description
float8 (E4M3)	`__hip_fp8_e4m3_fnuz`, `__hip_fp8_e4m3`	An 8-bit floating-point number with S1E4M3 bit layout, as described in low precision floating point types page. The FNUZ variant has expanded range with no infinity or signed zero (NaN represented as negative zero), while the OCP variant follows the Open Compute Project specification.
float8 (E5M2)	`__hip_fp8_e5m2_fnuz`, `__hip_fp8_e5m2`	An 8-bit floating-point number with S1E5M2 bit layout, as described in low precision floating point types page. The FNUZ variant has expanded range with no infinity or signed zero (NaN represented as negative zero), while the OCP variant follows the Open Compute Project specification.
float16	`half`	A 16-bit floating-point number that conforms to the IEEE 754-2008 half-precision storage format.
bfloat16	`bfloat16`	A shortened 16-bit version of the IEEE 754 single-precision storage format.
tensorfloat32	Not available	A floating-point number that occupies 32 bits or less of storage, providing improved range compared to half (16-bit) format, at (potentially) greater throughput than single-precision (32-bit) formats.
float32	`float`	A 32-bit floating-point number that conforms to the IEEE 754 single-precision storage format.
float64	`double`	A 64-bit floating-point number that conforms to the IEEE 754 double-precision storage format.

Note

The float8 and tensorfloat32 types are internal types used in calculations in Matrix Cores and can be stored in any type of the same size.
CNDA3 natively supports FP8 FNUZ (E4M3 and E5M2), which differs from the customised FP8 format used in NVIDIA’s H100 (FP8 Formats for Deep Learning).
In some AMD documents and articles, float8 (E5M2) is referred to as bfloat8.
The low precision floating point types page describes how to use these types in HIP with examples.

Level of support definitions#

In the following sections, icons represent the level of support. These icons, described in the following table, are also used in the library data type support pages.

Icon	Definition
NA	Not applicable
❌	Not supported
⚠️	Partial support
✅	Full support

Note

Full support means that the type is supported natively or with hardware emulation.
Native support means that the operations for that type are implemented in hardware. Types that are not natively supported are emulated with the available hardware. The performance of non-natively supported types can differ from the full instruction throughput rate. For example, 16-bit integer operations can be performed on the 32-bit integer ALUs at full rate; however, 64-bit integer operations might need several instructions on the 32-bit integer ALUs.
Any type can be emulated by software, but this page does not cover such cases.

Data type support by hardware architecture#

AMD’s GPU lineup spans multiple architecture generations:

CDNA1 architecture: includes models such as MI100
CDNA2 architecture: includes models such as MI210, MI250, and MI250X
CDNA3 architecture: includes models such as MI300A, MI300X, and MI325X
RDNA3 architecture: includes models such as RX 7900XT and RX 7900XTX
RDNA4 architecture: includes models such as RX 9070 and RX 9070XT

HIP C++ type implementation support#

The HIP C++ types available on different hardware platforms are listed in the following table.

HIP C++ Type	CDNA1	CDNA2	CDNA3	RDNA3	RDNA4
`int8_t`, `uint8_t`	✅	✅	✅	✅	✅
`int16_t`, `uint16_t`	✅	✅	✅	✅	✅
`int32_t`, `uint32_t`	✅	✅	✅	✅	✅
`int64_t`, `uint64_t`	✅	✅	✅	✅	✅
`__hip_fp8_e4m3_fnuz`	❌	❌	✅	❌	❌
`__hip_fp8_e5m2_fnuz`	❌	❌	✅	❌	❌
`__hip_fp8_e4m3`	❌	❌	❌	❌	✅
`__hip_fp8_e5m2`	❌	❌	❌	❌	✅
`half`	✅	✅	✅	✅	✅
`bfloat16`	✅	✅	✅	✅	✅
`float`	✅	✅	✅	✅	✅
`double`	✅	✅	✅	✅	✅

Note

Library support for specific data types is contingent upon hardware support. Even if a ROCm library indicates support for a particular data type, that type will only be fully functional if the underlying hardware architecture (as shown in the table above) also supports it. For example, fp8 types are only available on architectures shown with a checkmark in the relevant rows.

Compute units support#

The following table lists data type support for compute units.

Integral types

Type name	int8	int16	int32	int64
CDNA1	✅	✅	✅	✅
CDNA2	✅	✅	✅	✅
CDNA3	✅	✅	✅	✅
RDNA3	✅	✅	✅	✅
RDNA4	✅	✅	✅	✅

Floating-point types

Type name	float8 (E4M3)	float8 (E5M2)	float16	bfloat16	tensorfloat32	float32	float64
CDNA1	❌	❌	✅	✅	❌	✅	✅
CDNA2	❌	❌	✅	✅	❌	✅	✅
CDNA3	❌	❌	✅	✅	❌	✅	✅
RDNA3	❌	❌	✅	✅	❌	✅	✅
RDNA4	❌	❌	✅	✅	❌	✅	✅

Matrix core support#

The following table lists data type support for AMD GPU matrix cores.

Integral types

Type name	int8	int16	int32	int64
CDNA1	✅	❌	❌	❌
CDNA2	✅	❌	❌	❌
CDNA3	✅	❌	❌	❌
RDNA3	✅	❌	❌	❌
RDNA4	✅	❌	❌	❌

Floating-point types

Type name	float8 (E4M3)	float8 (E5M2)	float16	bfloat16	tensorfloat32	float32	float64
CDNA1	❌	❌	✅	✅	❌	✅	❌
CDNA2	❌	❌	✅	✅	❌	✅	✅
CDNA3	✅	✅	✅	✅	✅	✅	✅
RDNA3	❌	❌	✅	✅	❌	❌	❌
RDNA4	✅	✅	✅	✅	❌	❌	❌

Atomic operations support#

The following table lists which data types are supported for atomic operations on AMD GPUs. The atomics operation type behavior is affected by the memory locations, memory granularity, or scope of operations. For detailed various support of atomic read-modify-write (atomicRMW) operations collected on the Hardware atomics operation support page.

Integral types

Type name	int8	int16	int32	int64
CDNA1	❌	❌	✅	✅
CDNA2	❌	❌	✅	✅
CDNA3	❌	❌	✅	✅
RDNA3	❌	❌	✅	✅
RDNA4	❌	❌	✅	✅

Floating-point types

Type name	float8 (E4M3)	float8 (E5M2)	2 x float16	2 x bfloat16	tensorfloat32	float32	float64
CDNA1	❌	❌	✅	✅	❌	✅	❌
CDNA2	❌	❌	✅	✅	❌	✅	✅
CDNA3	❌	❌	✅	✅	❌	✅	✅
RDNA3	❌	❌	❌	❌	❌	✅	❌
RDNA4	❌	❌	✅	✅	❌	✅	❌

Note

You can emulate atomic operations using software for cases that are not natively supported. Software-emulated atomic operations have a high negative performance impact when they frequently access the same memory address.

Data type support in ROCm libraries#

ROCm library support for int8, float8 (E4M3), float8 (E5M2), int16, float16, bfloat16, int32, tensorfloat32, float32, int64, and float64 is listed in the following tables.

Libraries input/output type support#

The following tables list ROCm library support for specific input and output data types. Refer to the corresponding library data type support page for a detailed description.

Integral types

Library input/output data type name	int8	int16	int32	int64
Composable Kernel	✅/✅	❌/❌	✅/✅	❌/❌
hipCUB	✅/✅	✅/✅	✅/✅	✅/✅
hipRAND	NA/✅	NA/✅	NA/✅	NA/✅
hipSOLVER	❌/❌	❌/❌	❌/❌	❌/❌
hipSPARSELt	✅/✅	❌/❌	❌/❌	❌/❌
hipTensor	❌/❌	❌/❌	❌/❌	❌/❌
MIGraphX	✅/✅	✅/✅	✅/✅	✅/✅
MIOpen	⚠️/⚠️	❌/❌	⚠️/⚠️	❌/❌
RCCL	✅/✅	❌/❌	✅/✅	✅/✅
rocFFT	❌/❌	❌/❌	❌/❌	❌/❌
rocPRIM	✅/✅	✅/✅	✅/✅	✅/✅
rocRAND	NA/✅	NA/✅	NA/✅	NA/✅
rocSOLVER	❌/❌	❌/❌	❌/❌	❌/❌
rocThrust	✅/✅	✅/✅	✅/✅	✅/✅
rocWMMA	✅/✅	❌/❌	❌/✅	❌/❌

Floating-point types

Library input/output data type name	float8 (E4M3)	float8 (E5M2)	float16	bfloat16	tensorfloat32	float32	float64
Composable Kernel	✅/✅	✅/✅	✅/✅	✅/✅	❌/❌	✅/✅	✅/✅
hipCUB	❌/❌	❌/❌	✅/✅	✅/✅	❌/❌	✅/✅	✅/✅
hipRAND	NA/❌	NA/❌	NA/✅	NA/❌	NA/❌	NA/✅	NA/✅
hipSOLVER	❌/❌	❌/❌	❌/❌	❌/❌	❌/❌	✅/✅	✅/✅
hipSPARSELt	✅/✅	✅/✅	✅/✅	✅/✅	❌/❌	❌/❌	❌/❌
hipTensor	❌/❌	❌/❌	✅/✅	✅/✅	❌/❌	✅/✅	✅/✅
MIGraphX	✅/✅	✅/✅	✅/✅	✅/✅	✅/✅	✅/✅	✅/✅
MIOpen	⚠️/⚠️	⚠️/⚠️	✅/✅	⚠️/⚠️	❌/❌	✅/✅	⚠️/⚠️
RCCL	✅/✅	✅/✅	✅/✅	✅/✅	❌/❌	✅/✅	✅/✅
rocFFT	❌/❌	❌/❌	✅/✅	❌/❌	❌/❌	✅/✅	✅/✅
rocPRIM	❌/❌	❌/❌	✅/✅	✅/✅	❌/❌	✅/✅	✅/✅
rocRAND	NA/❌	NA/❌	NA/✅	NA/❌	NA/❌	NA/✅	NA/✅
rocSOLVER	❌/❌	❌/❌	❌/❌	❌/❌	❌/❌	✅/✅	✅/✅
rocThrust	❌/❌	❌/❌	⚠️/⚠️	⚠️/⚠️	❌/❌	✅/✅	✅/✅
rocWMMA	✅/❌	✅/❌	✅/✅	✅/✅	✅/✅	✅/✅	✅/✅

Note

As random number generation libraries, rocRAND and hipRAND only specify output data types for the random values they generate, with no need for input data types.

hipDataType enumeration#

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 HIP type equivalents of the hipDataType enumeration are listed in the following table with descriptions and values.

hipDataType	HIP type	Value	Description
`HIP_R_8I`	`int8_t`	3	8-bit real signed integer.
`HIP_R_8U`	`uint8_t`	8	8-bit real unsigned integer.
`HIP_R_16I`	`int16_t`	20	16-bit real signed integer.
`HIP_R_16U`	`uint16_t`	22	16-bit real unsigned integer.
`HIP_R_32I`	`int32_t`	10	32-bit real signed integer.
`HIP_R_32U`	`uint32_t`	12	32-bit real unsigned integer.
`HIP_R_32F`	`float`	0	32-bit real single precision floating-point.
`HIP_R_64F`	`double`	1	64-bit real double precision floating-point.
`HIP_R_16F`	`half`	2	16-bit real half precision floating-point.
`HIP_R_16BF`	`bfloat16`	14	16-bit real bfloat16 precision floating-point.
`HIP_R_8F_E4M3`	`__hip_fp8_e4m3`	28	8-bit real float8 precision floating-point (OCP version).
`HIP_R_8F_E5M2`	`__hip_fp8_e5m2`	29	8-bit real bfloat8 precision floating-point (OCP version).
`HIP_R_8F_E4M3_FNUZ`	`__hip_fp8_e4m3_fnuz`	1000	8-bit real float8 precision floating-point (FNUZ version).
`HIP_R_8F_E5M2_FNUZ`	`__hip_fp8_e5m2_fnuz`	1001	8-bit real bfloat8 precision floating-point (FNUZ version).

The full list of the hipDataType enumeration listed in library_types.h .

Data types and precision support

Contents

Data types and precision support#

Integral types#

Floating-point types#

Level of support definitions#

Data type support by hardware architecture#

HIP C++ type implementation support#

Compute units support#

Matrix core support#

Atomic operations support#

Data type support in ROCm libraries#

Libraries input/output type support#

hipDataType enumeration#