NVIDIA TensorRT-based applications perform up to 36X faster than CPU-only platforms during inference, enabling developers to optimize neural network models trained on all major frameworks, calibrate for lower precision with high accuracy, and deploy to hyperscale data centers, embedded platforms, or automotive product platforms.
TensorRT, built on the NVIDIA CUDA® parallel programming model, enables developers to optimize inference by leveraging libraries, development tools, and technologies in CUDA-X™ for AI, autonomous machines, high performance computing, and graphics. With new NVIDIA Ampere Architecture GPUs, TensorRT also uses sparse tensor cores for an additional performance boost.
TensorRT provides INT8 using quantization aware training and post-raining quantization, and FP16 optimizations for production deployments of deep learning inference applications, such as video streaming, speech recognition, recommendation, fraud detection, text generation, and natural language processing. Reduced precision inference significantly minimizes application latency, which is a requirement for many real-time services, as well as autonomous and embedded applications.
With TensorRT, developers can focus on creating novel AI-powered applications rather than inference optimization. TensorRT optimized models can then be deployed with NVIDIA Triton™, an open-source inference serving software that includes TensorRT as one of its backends.
World-Leading Inference Performance
TensorRT powered NVIDIA’s wins across all performance tests in the industry-standard MLPerf Inference benchmark. It also accelerates every workloadacross the data center and edge in computer vision, automatic speech recognition, natural language understanding (BERT), text-to-speech, and recommender systems.
Supports All Major Frameworks
TensorRT is integrated with PyTorch and TensorFlow so you can achieve 6X faster inference with a single line of code. 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.
Below are a few integrations with information on how to get started.
Accelerate PyTorch models using the new Torch-TensorRT Integration with just one line of code. Get 6X faster inference using the TensorRT optimizations in a familiar PyTorch environment.LEARN MORE
TensorRT and TensorFlow are tightly integrated so you get the flexibility of TensorFlow with the powerful optimizations of TensorRT like 6X the performance with one line of code.LEARN MORE
TensorRT provides an ONNX parser so you can easily import ONNX models from popular frameworks into TensorRT. It’s also integrated with ONNX Runtime, providing an easy way to achieve high-performance inference in the ONNX format.LEARN MORE
MATLAB is integrated with TensorRT through GPU Coder so you can automatically generate high performance inference engines for NVIDIA Jetson™, NVIDIA DRIVE®, and data center platforms.LEARN MORE
Accelerates Every Inference Platform
TensorRT can optimize and deploy applications to the data center, as well as embedded and automotive environments. It powers key NVIDIA solutions such as NVIDIA TAO, NVIDIA DRIVE™, NVIDIA Clara™, and NVIDIA Jetpack™.
TensorRT is also integrated with application-specific SDKs, such as NVIDIA DeepStream, NVIDIA Riva, NVIDIA Merlin™, NVIDIA Maxine™, NVIDIA Modulus, NVIDIA Morpheus, and Broadcast Engine to provide developers with a unified path to deploy intelligent video analytics, speech AI, recommender systems, video conference, AI based cybersecurity, and streaming apps in production.
Accelerate your deep learning inference today with NVIDIA TensorRT.
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Widely-Adopted Across Industries
Learn how to apply TensorRT optimizations and deploy a PyTorch model to GPUs.
Learn more about TensorRT and its new features from a curated list of webinars of GTC 2022.
Need enterprise support? NVIDIA global support is available for TensorRT with NVIDIA AI software suite.