NVIDIA TensorRT
- Android app
- Not listed
- Free plan
- Yes
- Runs on
- Linux, self-hosted, Windows
Summary
NVIDIA TensorRT is an ecosystem of inference compilers, runtimes, and model optimization tools for deep learning inference on NVIDIA GPUs. It uses quantization, layer and tensor fusion, and kernel tuning to optimize inference. TensorRT Model Optimizer supports FP8, FP4, INT8, INT4, and AWQ techniques, while TensorRT-LLM offers an open-source library and Python API for large language model inference on the NVIDIA AI platform. TensorRT works with PyTorch and Hugging Face, imports ONNX models, and connects with MATLAB through GPU Coder. NVIDIA Triton can use TensorRT as a backend for features including dynamic batching and concurrent model execution. The free SDK is available for Linux, Windows, and self-hosted deployment, and targets NVIDIA GPUs across data centers, workstations, laptops, and edge devices. Its support matrix requires compute capability SM 7.5 or higher. Serialized engines do not transfer across platforms such as Linux and Windows. NVIDIA warns that loading an engine from an untrusted source is equivalent to running untrusted native code on the GPU and host, and recommends using only engines you built or received through a trusted, authenticated channel. TensorRT Cloud access is limited to select partners and subject to approval.
Who it is for
TensorRT suits developers optimizing inference on supported NVIDIA hardware, including teams working with ONNX, PyTorch, Hugging Face, or TensorRT-LLM. Users should be prepared to manage platform-specific engines and handle engine files as trusted code.
What is good
- Free development SDK
- Supports FP8, FP4, INT8, and INT4
- Integrates with PyTorch and Hugging Face
- Supports ONNX imports
- Targets data center through edge devices
What to know first
- Requires NVIDIA compute capability SM 7.5 or higher
- Serialized engines are not portable across platforms
- Cloud access is limited to select partners
- Untrusted engines can run untrusted native code
Verdict
TensorRT offers optimization tools, framework connections, and multiple deployment targets for NVIDIA GPU inference. Its hardware requirement, platform-specific engine files, and engine security guidance are important constraints to consider.
NVIDIA TensorRT plans and pricing
All plansCompared on deep learning software
- Free plan
- Yesdeveloper.nvidia.com
- Training mode
- localdeveloper.nvidia.com
- Deployment targets
- multipledeveloper.nvidia.com
- GPU acceleration
- Yesdeveloper.nvidia.com
- Distributed training
- Nodeveloper.nvidia.com
- Supported languages
- C++, Pythondeveloper.nvidia.com
- Model formats
- ONNX; TensorRT engine/plan filesdeveloper.nvidia.com
Facts
- Purpose
- TensorRT is an ecosystem of inference compilers, runtimes, and model optimization tools for high-performance deep learning inference.developer.nvidia.com · 5 Oct 2026
- Optimization
- TensorRT optimizes inference with quantization, layer and tensor fusion, and kernel tuning.developer.nvidia.com · 5 Oct 2026
- Supported precisions
- TensorRT Model Optimizer supports FP8, FP4, INT8, INT4, and AWQ techniques.developer.nvidia.com · 5 Oct 2026
- LLM inference
- TensorRT-LLM is an open-source library with a simplified Python API for accelerating and optimizing large language model inference on the NVIDIA AI platform.developer.nvidia.com · 5 Oct 2026
- Framework integrations
- TensorRT integrates with PyTorch and Hugging Face, imports ONNX models, and connects with MATLAB through GPU Coder.developer.nvidia.com · 5 Oct 2026
- Serving
- NVIDIA Triton includes TensorRT as a backend and supports dynamic batching, concurrent model execution, model ensembling, and streaming audio and video inputs.developer.nvidia.com · 5 Oct 2026
- Deployment range
- TensorRT targets NVIDIA GPUs in data centers, workstations, laptops, and edge devices.developer.nvidia.com · 5 Oct 2026
- Cloud service access
- TensorRT Cloud is available with limited access to select partners, subject to approval.developer.nvidia.com · 5 Oct 2026
- Hardware requirement
- The support matrix states that TensorRT supports NVIDIA hardware with compute capability SM 7.5 or higher.docs.nvidia.com · 5 Oct 2026
- Engine portability
- Serialized TensorRT engines are not portable across platforms such as Linux and Windows.docs.nvidia.com · 5 Oct 2026
- Security
- NVIDIA warns that deserializing an engine from an untrusted source is equivalent to running untrusted native code on the GPU and host.docs.nvidia.com · 5 Oct 2026
- Security guidance
- NVIDIA recommends deserializing only engines built by the user or received through a trusted, authenticated channel.docs.nvidia.com · 5 Oct 2026
- License limitation
- The SDK license says NVIDIA has not tested or certified the SDK for critical applications and places responsibility for applicable legal and regulatory compliance on the user.docs.nvidia.com · 5 Oct 2026
- Support resources
- NVIDIA provides TensorRT documentation, quick-start guides, sample code, and troubleshooting resources.developer.nvidia.com · 5 Oct 2026
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Sources
- developer.nvidia.com/TensorRT· checked 5 Oct 2026
- docs.nvidia.com/deeplearning/tensorrt/latest/getting-st· checked 5 Oct 2026
- docs.nvidia.com/deeplearning/tensorrt/11.3.0/reference/· checked 5 Oct 2026
- docs.nvidia.com/deeplearning/tensorrt/latest/reference/· checked 5 Oct 2026
- developer.nvidia.com/tensorrt-getting-started· checked 5 Oct 2026