TensorRT 10.14.1 Support Matrix#

Platform and Software Support#

The following table shows TensorRT component support across different platforms, including supported CUDA versions, cuDNN, Python API, ONNX parser, and control flow features.

Important

Engine Portability

Platform: Serialized engines are not portable across platforms (Linux, Windows, etc.).
Version Compatibility: Engines built with the version-compatible flag can run with newer TensorRT versions within the same major version.
Hardware Compatibility: Engines built with hardware compatibility mode can run on multiple GPU architectures, depending on the hardware compatibility level used. Without this mode, engines are not portable across different GPU architectures.

Driver Requirements: Refer to the NVIDIA CUDA Release Notes for minimum compatible NVIDIA Driver versions.

Component	Linux x86-64 (10.14.x)	Windows x64 (10.14.x)	Linux SBSA (10.14.x)	NVIDIA JetPack (10.7.x)
NVIDIA CUDA	13.0 update 2 12.9 update 1 12.8 update 1 [1] 12.6 update 3 [1] 12.5 update 1 [1] 12.4 update 1 [1] 12.3 update 2 [1] 12.2 update 2 [1] 12.1 update 1 [1] 12.0 update 1 [1]	13.0 update 2 12.9 update 1 12.8 update 1 [1] 12.6 update 3 [1] 12.5 update 1 [1] 12.4 update 1 [1] 12.3 update 2 [1] 12.2 update 2 [1] 12.1 update 1 [1] 12.0 update 1 [1]	13.0 update 2	12.6 update 3
NVIDIA cuDNN (Optional)	8.9.7	8.9.7	N/A	8.9.6
TensorRT Python API	Supported	Supported	Supported	Supported
ONNX Parser	Supported	Supported	Supported	Supported
Control Flow (Loops)	Supported	Supported	Supported	Supported

GPU Architecture and Precision Support#

The following table shows which NVIDIA GPUs and their compute capabilities are supported by TensorRT, along with the precision modes (TF32, FP32, FP16, FP8, FP4, BF16, INT8) and Deep Learning Accelerator (DLA) availability for each architecture.

CUDA Compute Capability	Example Devices	TF32	FP32	FP16	FP8	FP4	BF16	INT8	DLA
12.1	NVIDIA DGX Spark	Supported	Supported	Supported	Supported	Supported	Supported	Supported	N/A
12.0	NVIDIA RTX PRO 6000 Blackwell	Supported	Supported	Supported	Supported	Supported	Supported	Supported	N/A
11.0	NVIDIA Jetson AGX Thor	Supported	Supported	Supported	Supported	Supported	Supported	Supported	N/A
10.3	NVIDIA B300	Supported	Supported	Supported	Supported	Supported	Supported	Supported	N/A
10.0	NVIDIA B200	Supported	Supported	Supported	Supported	Supported	Supported	Supported	N/A
9.0	NVIDIA H100 NVIDIA GH200 480 GB	Supported	Supported	Supported	Supported	Supported [2]	Supported	Supported	N/A
8.9	NVIDIA L40S	Supported	Supported	Supported	Supported	Supported [2]	Supported	Supported	N/A
8.7	NVIDIA DRIVE AGX Orin	Supported	Supported	Supported	N/A	N/A	N/A	Supported	Supported
8.6	NVIDIA A10	Supported	Supported	Supported	N/A	N/A	Supported	Supported	N/A
8.0	NVIDIA A100	Supported	Supported	Supported	N/A	N/A	Supported	Supported	N/A
7.5	NVIDIA T4	N/A	Supported	Supported	N/A	N/A	N/A	Supported	N/A

Supported Compute Capabilities#

The following table shows which GPU compute capabilities are supported on each platform.

Platform	Compute Capability
Linux x86-64	7.5 8.0 8.6 8.9 9.0 10.0 [3] 10.3 [4] 12.0 [3]
Windows 10 x64	7.5 8.0 8.6 8.9 9.0 10.0 [3] 10.3 [4] 12.0 [3]
Ubuntu 24.04 SBSA	7.5 8.0 8.6 9.0 10.0 10.3 11.0 12.0 12.1
NVIDIA JetPack AArch64	8.7

Compiler and Python Requirements#

The following table shows the required compiler and Python versions for building and using TensorRT on each supported platform.

Platform	Compiler Version	Python Version
Ubuntu 22.04 x86-64	gcc 11.2.1	3.10
Ubuntu 24.04 x86-64	gcc 11.2.1	3.12
Rocky Linux 8.9 x86-64	gcc 11.2.1	3.8
Rocky Linux 9.3 x86-64	gcc 11.2.1	3.9
Debian 12 x86-64	gcc 11.2.1	3.11
SLES 15 x86-64	gcc 11.2.1	N/A
Windows 10 x64 Windows 11 x64 Windows Server 2022 x64	MSVC 2019 v16.9.2	N/A
Ubuntu 24.04 SBSA	gcc 11.4	3.12
NVIDIA JetPack AArch64	gcc 11.4	3.12

Note

Python Version Support

Debian/RPM packages: Support the Python version listed in the table for each platform.
Wheel packages (tar/zip): Support Python 3.8, 3.9, 3.10, 3.11, 3.12, and 3.13 across all platforms.

ONNX Operator Support#

TensorRT provides extensive ONNX operator support through the ONNX parser. The complete and up-to-date ONNX operator support list for TensorRT is available in the ONNX-TensorRT Operator Support documentation.

Footnotes