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- TensorRT SDK | NVIDIA Developer
TensorRT provides developers a unified path to deploy intelligent video analytics, speech AI, recommender systems, video conferencing, AI-based cybersecurity, and streaming apps in production
- TensorRT - Get Started | NVIDIA Developer
NVIDIA® TensorRT™ is an ecosystem of APIs for high-performance deep learning inference The TensorRT inference library provides a general-purpose AI compiler and an inference runtime that deliver low latency and high throughput for production applications
- Speeding Up Deep Learning Inference Using TensorRT
This is an updated version of How to Speed Up Deep Learning Inference Using TensorRT This version starts from a PyTorch model instead of the ONNX model, upgrades the sample application to use TensorRT 7, and replaces the ResNet-50 classification model with UNet, which is a segmentation model
- NVIDIA TensorRT 10. 0 Upgrades Usability, Performance, and AI Model . . .
TensorRT includes inference runtimes and model optimizations that deliver low latency and high throughput for production applications This post outlines the key features and upgrades of this release, including easier installation, increased usability, improved performance, and more natively supported AI models
- 使用 NVIDIA TensorRT 加速深度学习推理（更新）
TensorRT 的关键优势在于其灵活性和技术的使用，包括混合精度、所有 GPU 平台上的高效优化，以及跨多种模型类型进行优化的能力。 在本节中，我们将介绍一些技术来提高吞吐量和减少应用程序的延迟。 有关详细信息，请参阅 TensorRT 性能最佳实践 。
- Accelerating Inference Up to 6x Faster in PyTorch with Torch-TensorRT
Torch-TensorRT is an integration for PyTorch that leverages inference optimizations of TensorRT on NVIDIA GPUs With just one line of code, it provides a simple API that gives up to 6x performance speedup on NVIDIA GPUs
- Speeding Up Deep Learning Inference Using NVIDIA TensorRT (Updated)
TensorRT can convert an FP32 network for deployment with INT8 reduced precision while minimizing accuracy loss To achieve this goal, models can be quantized using post training quantization and quantization aware training with TensorRT
- Deploying Deep Neural Networks with NVIDIA TensorRT
Tensor RT automatically optimizes trained neural networks for run-time performance, delivering up to 16x higher energy efficiency (performance per watt) on a Tesla P100 GPU compared to common CPU-only deep learning inference systems (see Figure 1)
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