NVIDIA TensorRT™ is an SDK for high-performance deep learning inference. It includes a deep learning inference optimizer and runtime that delivers low latency and high-throughput for deep learning inference applications.

TensorRT-based applications perform up to 40x faster than CPU-only platforms during inference. With TensorRT, you can optimize neural network models trained in all major frameworks, calibrate for lower precision with high accuracy, and finally deploy to hyperscale data centers, embedded, or automotive product platforms.

TensorRT is built on CUDA, NVIDIA’s parallel programming model, and enables you to optimize inference for all deep learning frameworks leveraging libraries, development tools and technologies in CUDA-X for artificial intelligence, autonomous machines, high-performance computing, and graphics.

TensorRT provides INT8 and FP16 optimizations for production deployments of deep learning inference applications such as video streaming, speech recognition, recommendation and natural language processing. Reduced precision inference significantly reduces application latency, which is a requirement for many real-time services, auto and embedded applications.

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You can import trained models from every deep learning framework into TensorRT. After applying optimizations, TensorRT selects platform specific kernels to maximize performance on Tesla GPUs in the data center, Jetson embedded platforms, and NVIDIA DRIVE autonomous driving platforms.

With TensorRT developers can focus on creating novel AI-powered applications rather than performance tuning for inference deployment.

TensorRT Optimizations and Performance

Weight & Activation Precision Calibration

Maximizes throughput by quantizing models to INT8 while preserving accuracy

Layer & Tensor Fusion

Optimizes use of GPU memory and bandwidth by fusing nodes in a kernel

Kernel Auto-Tuning

Selects best data layers and algorithms based on target GPU platform

Dynamic Tensor Memory

Minimizes memory footprint and re-uses memory for tensors efficiently

Multi-Stream Execution

Scalable design to process multiple input streams in parallel

TensorRT dramatically accelerates deep learning inference performance on NVIDIA GPUs. See how it can power your inference needs across multiple networks with high throughput and ultra-low latency.

Widely Adopted

Integrated with All Major Frameworks

NVIDIA works closely with deep learning framework developers to achieve optimized performance for inference on AI platforms using TensorRT. If your training models are in the ONNX format or other popular frameworks such as TensorFlow and MATLAB, there are easy ways for you to import models into TensorRT for inference. Below are few integrations with information on how to get started.

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TensorRT and TensorFlow are tightly integrated so you get the flexibility of TensorFlow with the powerful optimizations of TensorRT. Learn more in the TensorRT integrated with TensorFlow blog post.

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MATLAB is integrated with TensorRT through GPU Coder so that engineers and scientists using MATLAB can automatically generate high-performant inference engines for Jetson, DRIVE and Tesla platforms. Learn more in this webinar.

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TensorRT provides an ONNX parser so you can easily import ONNX models from frameworks such as Caffe 2, Chainer, Microsoft Cognitive Toolkit, MxNet and PyTorch into TensorRT. Learn more about ONNX support in TensorRT here.

TensorRT is also integrated with ONNX Runtime, providing an easy way to achieve high-performance inference for machine learning models in the ONNX format. Learn more about ONNX Runtime - TensorRT integration here.

If you are performing deep learning training in a proprietary or custom framework, use the TensorRT C++ API to import and accelerate your models. Read more in the TensorRT documentation.

“In our evaluation of TensorRT running our deep learning-based recommendation application on NVIDIA Tesla V100 GPUs, we experienced a 45x increase in inference speed and throughput compared with a CPU-based platform. We believe TensorRT could dramatically improve productivity for our enterprise customers.”

— Markus Noga, Head of Machine Learning at SAP SAP logo

“By using tensor cores on the V100, the most recently optimized CUDA libraries and the TF-TRT backend we were able to speed up our already fast DL network by a factor of 4x”

— Kris Bhaskar, KLA Senior Fellow, VP AI initiatives, KLA KLA logo

“Criteo uses Nvidia's TensorRT over T4 cards to optimize its deep-learning models for faster inference on GPUs. Now, removing inappropriate images over billions of them is 4 times faster. It also consumes half less energy.”

— Suju Rajan, SVP Research, Criteo Criteo logo

Announcing TensorRT 7.1: What's New

TensorRT 7.1 is optimized for NVIDIA A100 GPUs, and includes new optimizations to accelerate BERT inference using INT8 precision delivering 6x higher performance than V100 GPUs. TensorRT 7.1 is available now.

Additional Resources

You can find additional resources on https://devblogs.nvidia.com/tag/tensorrt/ and interact with the TensorRT developer community on the TensorRT Forum

Get Started With Hands-On Training

The NVIDIA Deep Learning Institute (DLI) offers hands-on training for developers, data scientists, and researchers in AI and accelerated computing.Get hands-on experience with TensorRT in self-paced electives on Optimization and Deployment of TensorFlow Models with TensorRT and Deployment for Intelligent Video Analytics using TensorRT today.


TensorRT is freely available to members of the NVIDIA Developer Program from the TensorRT product page for development and deployment. The latest version of plugins, parsers and samples are also available as open source from the TensorRT github repository.

Developers can also get TensorRT in the TensorRT Container from the NGC container registry.

TensorRT is included in: