NVIDIA's SDK for high-performance deep learning inference optimization and deployment on NVIDIA GPUs.
TensorRT is NVIDIA's SDK for high-performance deep learning inference optimization on NVIDIA GPUs. It takes trained neural network models and optimizes them through techniques like layer fusion, precision calibration, and kernel auto-tuning to achieve low latency and high throughput for production deployment. The open-source components include plugins, parsers, and sample applications that demonstrate TensorRT's capabilities.
AI engineers and developers deploying deep learning models in production environments requiring maximum inference performance on NVIDIA GPUs, including those in data centers, edge devices, and automotive systems.
Developers choose TensorRT for its deep integration with NVIDIA hardware, delivering unmatched inference performance through hardware-aware optimizations, and its support for diverse deployment targets from cloud to embedded systems.
NVIDIA® TensorRT™ is an SDK for high-performance deep learning inference on NVIDIA GPUs. This repository contains the open source components of TensorRT.
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TensorRT aggressively optimizes models via layer fusion and kernel auto-tuning, directly targeting NVIDIA GPU architectures to maximize throughput and minimize latency, as highlighted in its focus on production deployment.
Supports FP32, FP16, INT8, and other precision modes, allowing developers to trade off accuracy for performance and reduce memory footprint, essential for edge deployments like Jetson.
Offers IPluginV3 for custom layers, enabling support for unsupported operations or proprietary algorithms, as shown in the README's emphasis on extensibility for advanced use cases.
Covers diverse platforms from Linux/Windows servers to embedded systems like Jetson and DriveOS, with safety-critical samples for automotive QNX environments, ensuring flexibility across NVIDIA's GPU portfolio.
The README details a lengthy process with prerequisites like specific CUDA versions, containerized builds, and cross-compilation for embedded targets, making initial configuration cumbersome for quick prototyping.
Tightly coupled with NVIDIA GPUs and CUDA, and TensorRT 11.0 will remove legacy APIs like IPluginV2, forcing migrations and limiting portability to other hardware ecosystems.
Requires deep expertise in GPU optimization and inference pipelines, with the README noting API overhauls (e.g., weakly-typed network removal) that add maintenance overhead beyond basic model deployment.