Abstract

These support matrices provide a look into the supported platforms, features, and hardware capabilities of the NVIDIA TensorRT 8.2.5 APIs, parsers, and layers.

For previously released TensorRT documentation, see TensorRT Archives.

1. Features For Platforms And Software

This section lists the supported NVIDIA® 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
8.2.x 8.2.x 8.0.x 8.2.x
Supported NVIDIA CUDA® versions

11.5 update 21

11.4 update 3

11.3 update 11

11.2 update 21

11.1 update 11

11.0 update 11

10.2

11.5 update 22

11.4 update 3

11.3 update 1

11.2 update 2

11.1 update 1

11.0 update 1

10.2

 11.3 update 1

11.4 update 3

10.2
Supported cuBLAS versions

11.7.3.x

11.6.5.x

11.5.1.109

11.4.1.1043

11.3.0.106

11.2.0.252

10.2.3.254

11.7.3.x

11.6.5.x

11.5.1.109

11.4.1.1043

11.3.0.106

11.2.0.252

10.2.3.254

11.5.1.109

11.6.5.x

10.2.2.214
Supported cuDNN versions cuDNN 8.2.1 cuDNN 8.2.1 cuDNN 8.2.1 cuDNN 8.2.1
TensorRT Python API Yes Yes 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.
  • Refer to the minimum compatible driver versions in the NVIDIA CUDA Release Notes for specific NVIDIA Driver versions.

2. Layers And Features

The section lists the supported TensorRT layers and each of the features.
Note:
  • Supports broadcast 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.
  • Supports broadcast across batch indicates support for broadcast across the batch dimension. “NA” in this column means it's not allowed in networks with an implicit batch dimension.
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 Supports broadcast across batch
IActivationLayer 0-7 dimensions 0-7 dimensions No No No
IAssertionLayer 0-1 dimensions No output No No No
IConcatenationLayer 1-7 dimensions 1-7 dimensions No No No
IConditionLayer 0 No output 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
IDequantizeLayer 2 or more dimensions 2 or more dimensions Yes No No
IEinsumLayer 0-7 dimensions 0-7 dimensions No No Yes
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
IIfConditionalOutputLayer 0-7 dimensions 0-7 dimensions No No No
IIfConditionalInputLayer 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
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
IQuantizeLayer 2 or more dimensions 2 or more dimensions Yes No No
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
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
IScatterLayer 0-7 dimensions 0-7 dimensions No 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

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 NVIDIA TensorRT API Reference documentation.

Table 3. List of supported precision modes per TensorRT layer.
Layer FP32 FP16 INT8 INT32 Bool DLA FP16 DLA INT8
IActivationLayer Yes Yes Yes No No Yes3 Yes4
IAssertionLayer No No No No Yes No No
IConcatenationLayer Yes Yes Yes Yes No Yes5 Yes5
IConditionLayer No No No No Yes No No
IConstantLayer Yes Yes Yes Yes No No No
IConvolutionLayer > 2D Convolution Yes Yes Yes No No Yes Yes
IConvolutionLayer > 3D Convolution Yes Yes Yes No No No No
IDeconvolutionLayer > 2D Deconvolution Yes Yes Yes No No Yes Yes6
IDeconvolutionLayer > 3D Deconvolution Yes Yes No No No No No
IDequantizeLayer No No Yes No No No No
IEinsumLayer Yes Yes No No No No No
IElementWiseLayer Yes Yes No Yes Yes Yes7 Yes8
IFillLayer Yes No No Yes No No No
IFullyConnectedLayer Yes Yes Yes No No Yes Yes
IGatherLayer Yes Yes No Yes No No No
IIdentityLayer Yes Yes Yes Yes No No No
IIfConditionalOutputLayer Yes Yes No Yes Yes No No
IIfConditionalInputLayer Yes Yes No Yes Yes No No
IIteratorLayer Yes Yes No Yes No No No
ILoopOutputLayer Yes Yes No Yes No No No
ILRNLayer Yes Yes Yes No No Yes No
IMatrixMultiplyLayer Yes Yes No No No No No
IPaddingLayer Yes Yes Yes No No No No
IParametricReluLayer Yes Yes Yes No No No No
IPluginV2Layer Yes Yes Yes No No No No
IPoolingLayer > 2D Pooling Yes Yes Yes No No Yes9 Yes9
IPoolingLayer > 3D Pooling Yes Yes No No No No No
IQuantizeLayer Yes No No No No No No
IRaggedSoftMaxLayer Yes No No No No No No
IRecurrenceLayer Yes Yes No Yes Yes No No
IReduceLayer Yes Yes Yes Yes No No No
IResizeLayer Yes Yes No No No No No
IRNNv2Layer Yes Yes No No No No No
IScaleLayer Yes Yes Yes No No Yes10 Yes10
IScatterLayer Yes Yes Yes Yes No No No
ISelectLayer Yes Yes No Yes Yes No No
IShapeLayer11 Yes Yes Yes Yes Yes No No
IShuffleLayer Yes Yes Yes Yes No No No
ISliceLayer Yes Yes No12 Yes No No No
ISoftMaxLayer Yes Yes No No No No No
ITopKLayer Yes Yes No No No No No
ITripLimitLayer Yes Yes No Yes Yes No No
IUnaryLayer Yes Yes No No Yes 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 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 NVIDIA A10 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. Layers For Flow-Control Constructs

The following table lists the TensorRT layers that can be used as interior layers in TensorRT flow-control constructs.

Currently, TensorRT supports loop constructs (via ILoopLayer) and ternary conditional constructs (via IIfConditionalLayer). Interior layers are layers that comprise the body of a loop or one of the two branches of an if-conditional.

An ILoopLayer interior layer may contain other loops and/or if-conditionals. An IIfConditionalLayer branch may contain other if-conditionals and/or loops.

Flow-control constructs do not support INT8 calibration and interior-layers cannot employ implicit-quantization (INT8 is supported only in explicit-quantization mode).

Table 7. List of TensorRT layers that are supported as interior layers of flow-control constructs
Layer Supported
IActivationLayer Yes, when the operation is one of: kRELU, kSIGMOID, kTANH, kELU
IAssertionLayer Yes
IConcatenationLayer Yes
IConditionLayer Yes (for nested conditionals)
IConstantLayer Yes
IConvolutionLayer > 2D Convolution singleton channel and spatial dims, i.e. said dimensions must be static or have a single value in each optimization profile
IConvolutionLayer > 3D Convolution singleton channel and spatial dims
IDeconvolutionLayer > 2D Deconvolution No
IDeconvolutionLayer > 3D Deconvolution No
IDequantizeLayer No
IEinsumLayer Yes
IElementWiseLayer Yes
IFillLayer kRANDOM_UNIFORM only
IFullyConnectedLayer Yes
IGatherLayer Yes
IIdentityLayer Yes
IIfConditionalOutputLayer Yes (for nested conditionals)
IIfConditionalInputLayer Yes (for nested conditionals)
IIteratorLayer Yes (for nested loops)
ILoopOutputLayer Yes (for nested loops)
ILRNLayer No
IMatrixMultiplyLayer Yes
IPaddingLayer No
IParametricReluLayer No
IPluginV2Layer Yes
IPoolingLayer > 2D Pooling No
IPoolingLayer > 3D Pooling No
IQuantizeLayer No
IRaggedSoftMaxLayer No
IRecurrenceLayer Yes
IReduceLayer Yes
IResizeLayer No
IRNNv2Layer No
IScaleLayer Yes
IScatterLayer Yes
ISelectLayer Yes
IShapeLayer Yes
IShuffleLayer Yes
ISliceLayer Yes
ISoftMaxLayer Yes
ITopKLayer No
ITripLimitLayer Yes
IUnaryLayer Yes, when the operation is one of: kABS, kCEIL, kERF, kEXP, kFLOOR, kLOG, kNEG, kNOT, kRECIP, kROUND, kSIGN, kSQRT, kSIN, kCOS, kATAN

6. Compute Capability Per Platform

The section lists the supported compute capability based on platform.
Table 8. Compute capability per platform
Platform Compute capability
Linux x86-64 3.5, 3.7, 5.0, 5.2, 6.0, 6.1, 7.0, 7.5, 8.013, 8.614
Windows 10 x64 3.5, 3.7, 5.0, 5.2, 6.0, 6.1, 7.0, 7.5, 8.013, 8.614
CentOS 8.3 ppc64le 7.0, 7.5, 8.0, 8.6
Ubuntu 20.04 SBSA 7.0, 7.5, 8.0, 8.6
JetPack AArch64 5.3, 6.2, 7.2

7. Software Versions Per Platform

The section lists the supported software versions based on platform.
Table 9. List of supported platforms per software version.
Platform Compiler version Python version
Ubuntu 18.04 x86-64 gcc 8.3.1 3.6
Ubuntu 20.04 x86-64 gcc 8.3.1 3.8
CentOS 7.9 x86-64 gcc 8.3.1 3.6
CentOS 8.3 x86-64 gcc 8.3.1 3.8
SLES 15 x86-64 gcc 8.3.1 N/A
Windows 10 x64 MSVC 2017u5 N/A
CentOS 8.3 ppc64le Clang 10.0.1 3.8
Ubuntu 20.04 SBSA gcc 8.4.0 3.8
JetPack AArch64 gcc 7.5.0 3.6
Note: Python versions supported when using Debian or RPM packages. When using Python wheel files, versions 3.6, 3.7, 3.8, and 3.9 are supported.

8. ONNX Operator Support

The ONNX operator support list for TensorRT can be found here.

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1 These CUDA versions are supported using a single build, built with CUDA toolkit 11.4. It is compatible with all CUDA 11.x versions and only requires driver 450.x.
2 The NVRTC dynamic library from CUDA toolkit version 11.4 is required when using CUDA toolkits newer than 11.4.
3 Partial support. Yes for ReLU, Clipped ReLU, Leaky ReLU, Sigmoid and TanH activation types only.
4 Partial support. Yes for ReLU, Clipped ReLU, Leaky ReLU, Sigmoid and TanH activation types only.
5 Partial support. Yes for concatenation across C dimension only.
6 Partial support. Yes for ungrouped deconvolutions and No for grouped.
7 Partial support. Yes for sum, sub, prod, min and max elementwise operations only.
8 Partial support. Yes for sum elementwise operation only.
9 Partial support. Yes for max and average padding inclusive pooling type only.
10 Partial support. DLA does not support power on scale layer.
11 Output is always INT32.
12 Partial support. Yes for unstrided Slice and No for strided.
13 Requires CUDA toolkit 11.0 or newer and a TensorRT CUDA 11.x build.
14 Requires CUDA toolkit 11.1 or newer and a TensorRT CUDA 11.x build.