Abstract

These support matrices provide a look into the supported platforms, features, and hardware capabilities of the TensorRT 7.2.1 APIs, parsers, and layers.

For previously released TensorRT documentation, see TensorRT Archives.

1. Features For Platforms And Software

This section lists the supported TensorRT features based on which platform and software.
Table 1. List of supported features per platform.
  Linux x86-64 Windows x64 Linux ppc64le Linux AArch64
Supported CUDA versions

11.1

11.0 update 1

10.2

11.1

11.0 update 1

10.2

11.0 update 1

11.1

10.2

Supported cuBLAS versions

11.2.1.74

11.2.0.252

10.2.2.214

11.2.1.74

11.2.0.252

10.2.2.214

11.2.0.252

11.2.1.74

10.2.2.214

Supported cuDNN versions cuDNN 8.0.4 cuDNN 8.0.4 cuDNN 8.0.3 cuDNN 8.0.4
TensorRT Python API Yes No Yes Yes
NvUffParser Yes Yes Yes Yes
NvOnnxParser Yes Yes Yes Yes
Loops Yes Yes Yes Yes
Note: Serialized engines are not portable across platforms or TensorRT versions.

2. Layers And Features

The section lists the supported TensorRT layers and each of the 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 broadcast (see Note 1) Supports broadcast across batch (see Note 2)
IActivationLayer 0-7 dimensions 0-7 dimensions No No No
IConcatenationLayer 1-7 dimensions 1-7 dimensions No No No
IConstantLayer has no inputs 0-7 dimensions No No Always
IConvolutionLayer > 2D Convolution 3 or more dimensions 3 or more dimensions Yes No No
IConvolutionLayer > 3D Convolution 4 or more dimensions 4 or more dimensions No No No
IDeconvolutionLayer > 2D Deconvolution 3 or more dimensions 3 or more dimensions Yes No No
IDeconvolutionLayer > 3D Deconvolution 4 or more dimensions 4 or more dimensions No No No
IElementWiseLayer 0-7 dimensions 0-7 dimensions No Yes Yes
IFillLayer 1 dimension 0-7 dimensions No NA NA
IFullyConnectedLayer 3 or more dimensions 3 or more dimensions Yes No No
IGatherLayer
  • Input1: 1-7 dimensions
  • Input2: 0-7 dimensions
0-7 dimensions No No Yes
IIdentityLayer 0-7 dimensions 0-7 dimensions No No No
IIteratorLayer 1-7 dimensions 0-6 dimensions No No NA
ILoopOutputLayer 0-7 dimensions 0-7 dimensions No No NA
ILRNLayer 3 or more dimensions 3 or more dimensions Yes No No
IMatrixMultiplyLayer 2 or more dimensions 2 or more dimensions No Yes Yes
IPaddingLayer 3 or more dimensions 3 or more dimensions Yes No No
IParametricReluLayer 1-7 dimensions 1-7 dimensions No No No
IPluginLayer User defined User defined User defined User defined User defined
IPluginV2Layer User defined User defined User defined User defined User defined
IPoolingLayer > 2D Pooling 3 or more dimensions 3 or more dimensions Yes Yes Yes
IPoolingLayer > 3D Pooling 4 or more dimensions 4 or more dimensions No Yes Yes
IRaggedSoftMaxLayer
  • Input: 2 dimensions
  • Bounds: 2 dimensions
2 or more dimensions No No Yes
IRecurrenceLayer 0-7 dimensions 0-7 dimensions No No NA
IReduceLayer 1-7 dimensions 0-7 dimensions No No No
IResizeLayer 1-7 dimensions 1-7 dimensions No No No
IRNNLayer 3 dimensions 3 dimensions No No No
IRNNv2Layer
  • Data/Hidden/Cell: 2 or more dimensions
  • Seqlen: 0 or more dimensions
Data/Hidden/Cell: 2 or more dimensions No No No
IScaleLayer 3 or more dimensions 3 or more dimensions Yes No No
ISelectLayer 0-7 dimensions 0-7 dimensions No Yes NA
IShapeLayer 1 or more dimensions 1 dimension No No NA
IShuffleLayer 0-7 dimensions 0-7 dimensions No No No
ISliceLayer 1-7 dimensions 1-7 dimensions No No Yes
ISoftMaxLayer 1-7 dimensions 1-7 dimensions No No Yes
ITopKLayer 1-7 dimensions
  • Output1: 1-7 dimensions
  • Output2: 1-7 dimensions
Yes No Yes
ITripLimitLayer 0 dimensions has no outputs No No NA
IUnaryLayer 1-7 dimensions 1-7 dimensions No No No
Note:
  1. Indicates support for broadcast in this layer. This layer allows its two input tensors to be of dimensions [1, 5, 4, 3] and [1, 5, 1, 1], and its output is [1, 5, 4, 3]. The second input tensor has been broadcast in the innermost 2 dimensions.
  2. Indicates support for broadcast across the batch dimension. “NA” in this column means it's not allowed in networks with an implicit batch dimension.
For more information about each of the TensorRT layers, see TensorRT Layers.

3. Layers And Precision

The section 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 modes per TensorRT layer.
Layer FP32 FP16 INT8 INT32 DLA FP16 DLA INT8
IActivationLayer Yes Yes Yes No Yes1 Yes2
IConcatenationLayer Yes Yes Yes Yes Yes3 Yes3
IConstantLayer Yes Yes Yes Yes No No
IConvolutionLayer > 2D Convolution Yes Yes Yes No Yes Yes
IConvolutionLayer > 3D Convolution Yes Yes No No No No
IDeconvolutionLayer > 2D Deconvolution Yes Yes Yes No Yes Yes4
IDeconvolutionLayer > 3D Deconvolution Yes Yes No No No No
IElementWiseLayer Yes Yes No Yes Yes5 Yes6
IFillLayer Yes No No Yes No No
IFullyConnectedLayer Yes Yes Yes No Yes Yes
IGatherLayer Yes Yes No Yes No No
IIdentityLayer Yes Yes Yes Yes No No
IIteratorLayer Yes Yes No Yes No No
ILoopOutputLayer Yes Yes No Yes No No
IPluginV2Layer Yes Yes Yes No No No
ILRNLayer Yes Yes Yes No Yes No
IMatrixMultiplyLayer Yes Yes No No No No
IPaddingLayer Yes Yes Yes No No No
IParametricReluLayer Yes Yes Yes No No No
IPluginLayer Yes Yes No No No No
IPoolingLayer > 2D Pooling Yes Yes Yes No Yes7 Yes7
IPoolingLayer > 3D Pooling Yes Yes No No No No
IRaggedSoftMaxLayer Yes No No No No No
IRecurrenceLayer Yes Yes No Yes No No
IReduceLayer Yes Yes No No No No
IResizeLayer Yes Yes No No No No
IRNNLayer Yes Yes No No No No
IRNNv2Layer Yes Yes No No No No
IScaleLayer Yes Yes Yes No Yes8 Yes8
ISelectLayer Yes Yes No Yes No No
IShapeLayer9 Yes Yes Yes Yes No No
IShuffleLayer Yes Yes Yes Yes No No
ISliceLayer Yes Yes No10 Yes No No
ISoftMaxLayer Yes Yes No No No No
ITopKLayer Yes Yes No No No No
ITripLimitLayer Yes Yes No Yes No No
IUnaryLayer Yes Yes No No No No
Note: DLA with FP16/INT8 precision with some restrictions on layer parameters.

4. Hardware And Precision

The following table lists NVIDIA hardware and which precision modes each hardware supports. TensorRT supports all NVIDIA hardware with capability SM 5.0 or higher. It also lists the availability of Deep Learning Accelerator (DLA) on this hardware. Refer to the following tables for the specifics.
Note: Support for CUDA Compute Capability version 3.0 has been removed. Support for CUDA Compute Capability versions below 5.0 may be removed in a future release and is now deprecated.
Table 4. Supported hardware
CUDA Compute Capability Example Device TF32 FP32 FP16 INT8 FP16 Tensor Cores INT8 Tensor Cores DLA
8.6 GeForce 3090 Yes Yes Yes Yes Yes Yes No
8.0 NVIDIA A100/GA100 GPU Yes Yes Yes Yes Yes Yes No
7.5 Tesla T4 No Yes Yes Yes Yes Yes No
7.2 Jetson AGX Xavier No Yes Yes Yes Yes Yes Yes
7.0 Tesla V100 No Yes Yes Yes Yes No No
6.2 Jetson TX2 No Yes Yes No No No No
6.1 Tesla P4 No Yes No Yes No No No
6.0 Tesla P100 No Yes Yes No No No No
5.3 Jetson TX1 No Yes Yes No No No No
5.2 Tesla M4 No Yes No No No No No
5.0 Quadro K2200 No Yes No No No No No

Deprecated hardware

Table 5. List of supported precision mode per hardware.
CUDA Compute Capability Example Device FP32 FP16 INT8 FP16 Tensor Cores INT8 Tensor Cores DLA
3.7 Tesla K80 Yes No No No No No
3.5 Tesla K40 Yes No No No No No

Removed hardware

Table 6. List of supported precision mode per hardware.
CUDA Compute Capability Example Device FP32 FP16 INT8 FP16 Tensor Cores INT8 Tensor Cores DLA
3.0 Tesla K10 Yes No No No No No

5. Software Versions Per Platform

The section lists the supported software versions based on platform.
Table 7. List of supported platforms per software version.
  Compiler version Python versions
Ubuntu 16.04 x86-64 gcc 5.4.0 2.7, 3.511
Ubuntu 18.04 x86-64 gcc 7.4.0 2.7, 3.611
CentOS 7.6 x86-64 gcc 4.8.5 2.7, 3.611
Windows 10 x64 MSVC 2017u5 N/A
CentOS 8.1 ppc64le gcc 4.8.5 2.7, 3.611
Ubuntu 18.04 ppc64le gcc 7.4.0 2.7, 3.611
Ubuntu 18.04 AArch64

gcc 8.4.0 (SBSA)

gcc 7.4.0

2.7, 3.611

6. Supported Ops

The section lists the operations that are supported in a Caffe or TensorFlow framework and in the ONNX TensorRT parser.

Caffe

These are the operations that are supported in a Caffe framework:
  • BatchNormalization
  • BNLL
  • Clip12
  • Concatenation
  • Convolution
  • Crop
  • Deconvolution
  • Dropout
  • ElementWise
  • ELU
  • InnerProduct
  • Input
  • LeakyReLU
  • LRN
  • Permute
  • Pooling
  • Power
  • Reduction
  • ReLU, TanH, and Sigmoid
  • Reshape
  • SoftMax
  • Scale

TensorFlow

These are the operations that are supported in a TensorFlow framework:
  • Add, Sub, Mul, Div, Minimum and Maximum
  • ArgMax
  • ArgMin
  • AvgPool
  • BiasAdd
  • Clip
  • ConcatV2
  • Const
  • Conv2D
  • ConvTranspose2D
  • DepthwiseConv2dNative
  • Elu
  • ExpandDims
  • FusedBatchNorm
  • Identity
  • LeakyReLU
  • MaxPool
  • Mean
  • Negative, Abs, Sqrt, Recip, Rsqrt, Pow, Exp and Log
  • Pad is supported if followed by one of these TensorFlow layers: Conv2D, DepthwiseConv2dNative, MaxPool, and AvgPool.
  • Placeholder
  • ReLU, TanH, and Sigmoid
  • Relu6
  • Reshape
  • Sin, Cos, Tan, Asin, Acos, Atan, Sinh, Cosh, Asinh, Acosh, Atanh, Ceil and Floor
  • Selu
  • Slice
  • SoftMax
    Note: If the input to a TensorFlow SoftMax op is not NHWC, TensorFlow will automatically insert a transpose layer with a non-constant permutation, causing the UFF converter to fail. It is therefore advisable to manually transpose SoftMax inputs to NHWC using a constant permutation.
  • Softplus
  • Softsign
  • Transpose

For the list of ops supported in UFF, see UFF Operators.

ONNX

Since the ONNX parser is an open source project, the most up-to-date information regarding the supported operations can be found here.

These are the operations that are supported in the ONNX framework:
  • Abs
  • Acos
  • Acosh
  • And
  • Asin
  • Asinh
  • Atan
  • Atanh
  • Add
  • ArgMax
  • ArgMin
  • AveragePool
  • BatchNormalization
  • Cast
  • Ceil
  • Clip
  • Concat
  • Constant
  • ConstantOfShape
  • Conv
  • ConvTranspose
  • Cos
  • Cosh
  • DepthToSpace
  • DequantizeLinear
  • Div
  • Dropout
  • Elu
  • Equal
  • Erf
  • Exp
  • Expand
  • Flatten
  • Floor
  • Gather
  • Gemm
  • GlobalAveragePool
  • GlobalMaxPool
  • Greater
  • GRU
  • HardSigmoid
  • Identity
  • ImageScaler
  • InstanceNormalization
  • LRN
  • LeakyRelU
  • Less
  • Log
  • LogSoftmax
  • Loop
  • LRN
  • LSTM
  • MatMul
  • Max
  • MaxPool
  • Mean
  • Min
  • Mul
  • Neg
  • Not
  • Or
  • Pad
  • ParametricSoftplus
  • Pow
  • PRelu
  • QuantizeLinear
  • RandomUniform
  • RandomUniformLike
  • Range
  • Reciprocal
  • ReduceL1
  • ReduceL2
  • ReduceLogSum
  • ReduceLogSumExp
  • ReduceMax
  • ReduceMean
  • ReduceMin
  • ReduceProd
  • ReduceSum
  • ReduceSumSquare
  • Relu
  • Reshape
  • Resize
  • RNN
  • ScaledTanh
  • Scan
  • Selu
  • Shape
  • Sigmoid
  • Sin
  • Sinh
  • Size
  • Slice
  • Softmax
  • Softplus
  • Softsign
  • SpaceToDepth
  • Split
  • Sqrt
  • Squeeze
  • Sub
  • Sum
  • Tan
  • Tanh
  • ThresholdedRelu
  • Tile
  • TopK
  • Transpose
  • Unsqueeze
  • Upsample
  • Where

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1 Partial support. Yes for ReLU, Clipped ReLU, Sigmoid and TanH activation types only.
2 Partial support. Yes for ReLU, Clipped ReLU activation type only.
3 Partial support. Yes for concatenation across C dimension only.
4 Partial support. Yes for ungrouped deconvolutions and No for grouped.
5 Partial support. Yes for sum, sub, prod, min and max elementwise operations only.
6 Partial support. Yes for sum elementwise operation only.
7 Partial support. Yes for max and average padding inclusive pooling type only.
8 Partial support. DLA does not support power on scale layer.
9 Output is always INT32.
10 Partial support. Yes for unstrided Slice and No for strided.
11 Python versions supported when using Debian or RPM packages. When using Python wheel files, versions 3.4, 3.5, 3.6, and 3.7 are supported.
12 When using the Clip operation, Caffe users must serialize their layers using ditcaffe.pb.h instead of caffe.pb.h in order to import the layer into TensorRT.