TensorRT Support Matrix :: Deep Learning SDK Documentation

Abstract

This support matrix is for TensorRT 5.0.2. These matrices provide a look into supported features and software for TensorRT APIs, parsers, and layers.

For previously released TensorRT documentation, see TensorRT Archives.

1. Features For Platforms And Software

Table 1. List of supported features per platform.
	Linux x86-64	Linux AArch64	QNX AArch64	Windows x64
Supported CUDA versions	9.0, 10.0	10.0	10.0	10.0
Supported cuDNN versions	7.3.1	7.3.1	7.3.1	7.3.1
TensorRT Python API	Yes	No	No	No
NvUffParser	Yes	Yes	Yes	Yes
NvOnnxParser	Yes	Yes	Yes	No

Note: Serialized engines are not portable across platforms or TensorRT versions.

2. Layers And Features

Table 2. List of supported features per TensorRT layer.
Layer	Dimensions of input tensor	Dimensions of output tensor	Does the operation apply to only the innermost 3 dimensions?	Supports broadcast¹	Supports broadcast across batch²
Activation	0-7 dimensions	0-7 dimensions	No	No	No
Concatenation	1-7 dimensions	1-7 dimensions	No	No	No
Constant	0-7 dimensions	0-7 dimensions	No	No	Always
Convolution	3 or more dimensions	3 or more dimensions	Yes	No	No
Deconvolution	3 or more dimensions	3 or more dimensions	Yes	No	No
ElementWise	0-7 dimensions	0-7 dimensions	No	Yes	Yes
FullyConnected	3 or more dimensions	3 or more dimensions	Yes	No	No
Gather	Input1: 1-7 dimensions Input2: 0-7 dimensions	0-7 dimensions	No	No	Yes
Identity	0-7 dimensions	0-7 dimensions	No	No	No
IPluginV2	User defined	User defined	User defined	User defined	User defined
LRN	3 or more dimensions	3 or more dimensions	Yes	No	No
MatrixMultiply	2 or more dimensions	2 or more dimensions	No	Yes	Yes
Padding	3 or more dimensions	3 or more dimensions	Yes	No	No
Plugin	User defined	User defined	User defined	User defined	User defined
Pooling	3 or more dimensions	3 or more dimensions	Yes	Yes	Yes
RaggedSoftMax	Input: 2 dimensions Bounds: 2 dimensions	2 or more dimensions	No	No	Yes
Reduce	1-7 dimensions	0-7 dimensions	No	No	No
RNN	3 dimensions	3 dimensions	No	No	No
RNNv2	Data/Hidden/Cell: 2 or more dimensions Seqlen: 0 or more dimensions	Data/Hidden/Cell: 2 or more dimensions	No	No	No
Scale	3 or more dimensions	3 or more dimensions	Yes	No	No
Shuffle	0-7 dimensions	0-7 dimensions	No	No	No
SoftMax	1-7 dimensions	1-7 dimensions	No	No	No
TopK	1-7 dimensions	Output1: 1-7 dimensions Output2: 1-7 dimensions	Yes	No	Yes
Unary	0-7 dimensions	0-7 dimensions	No	No	No

For more information about each of the TensorRT layers, see TensorRT Layers.

The following table lists the TensorRT layers and the precision modes that each layer supports. It also lists the ability of the layer to run on Deep Learning Accelerator (DLA). For more information about additional constraints, see DLA Supported Layers.

For more information about each of the TensorRT layers, see TensorRT Layers. To view a list of the specific attributes that are supported by each layer, refer to the TensorRT API documentation.

Table 3. List of supported precision mode per TensorRT layer.
Layer	FP32	FP16	INT32	DLA³
Activation	Yes	Yes	No	Yes
Concatenation	Yes	Yes	Yes	Yes
Constant	Yes	Yes	Yes	No
Convolution	Yes	Yes	No	Yes
Deconvolution	Yes	Yes	No	Yes
ElementWise	Yes	Yes	No	Yes
FullyConnected	Yes	Yes	No	Yes
Gather	Yes	Yes	Yes	No
Identity	Yes	Yes	Yes	No
IPluginV2	Yes	Yes	No	No
LRN	Yes	Yes	No	Yes
MatrixMultiply	Yes	Yes	No	No
Padding	Yes	Yes	No	No
Plugin	Yes	Yes	No	No
Pooling	Yes	Yes	No	Yes
RaggedSoftMax	Yes	No	No	No
Reduce	Yes	Yes	No	No
RNN	Yes	Yes	No	No
RNNv2	Yes	Yes	No	No
Scale	Yes	Yes	No	Yes
Shuffle	Yes	Yes	Yes	No
SoftMax	Yes	Yes	No	No
TopK	Yes	Yes	No	No
Unary	Yes	Yes	No	No

4. Hardware And Precision

The following table lists NVIDIA hardware and which precision modes each hardware supports. It also lists availability of Deep Learning Accelerator (DLA) on these hardware.

Table 4. List of supported precision mode per hardware.
SM Version	Example Device	FP32	FP16	INT8	FP16 Tensor Cores	INT8 Tensor Cores	DLA
7.5	Tesla T4	Yes	Yes	Yes	Yes	Yes	No
7.2	Jetson AGX Xavier	Yes	Yes	Yes	Yes	Yes	Yes
7.0	Tesla V100	Yes	Yes	Yes	Yes	No	No
6.1	Tesla P4	Yes	No	Yes	No	No	No
6.0	Tesla P100	Yes	Yes	No	No	No	No
5.2	Tesla M4	Yes	No	No	No	No	No

5. Software Versions Per Platform

Table 5. List of supported platforms per software version.
	Ubuntu 14.04	Ubuntu 16.04	Ubuntu 18.04	CentOS 7.5	Linux AArch64	QNX	Windows 10
Compiler version	gcc 4.8.4	gcc 5.4.0	gcc 7.3.0	gcc 4.8.5	gcc 5.3.1	gcc 5.4.0	MSVC 2017u5
Python versions	2.7, 3.4	2.7, 3.5	2.7, 3.6	2.7

Note: Serialized engines are not portable across platforms or TensorRT versions.

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