1. [Topic](/topics/) [AI Inference](/topics/ai/ai-inference/) TensorRT # NVIDIA TensorRT SDKs and Libraries NVIDIA TensorRT™ is an ecosystem of tools for developers to achieve high-performance deep learning inference. It includes inference compilers, runtimes, and model optimizations that deliver low latency and high throughput for production applications. The TensorRT ecosystem includes TensorRT SDK, TensorRT LLM, NVIDIA Model Optimizer, and TensorRT for NVIDIA RTX™. [Download TensorRT SDK Now](https://developer.nvidia.com/tensorrt/download)[Documentation ](https://docs.nvidia.com/deeplearning/tensorrt/)[GitHub](https://github.com/NVIDIA/TensorRT) * * * ## How TensorRT Works Speed up inference by 36x compared to CPU-only platforms. Built on the NVIDIA CUDA™ parallel programming model, TensorRT SDK includes libraries that optimize neural network models trained on all major frameworks, calibrate them for lower precision with high accuracy, and deploy them to hyperscale data centers, workstations, laptops, and edge devices. TensorRT SDK optimizes inference using quantization, layer and tensor fusion, and kernel tuning techniques. NVIDIA Model Optimizer provides easy-to-use quantization techniques, including post-training quantization and quantization-aware training to compress your models. FP8, FP4, INT8, INT4, and advanced techniques such as AWQ are supported for your deep learning inference optimization needs. Quantized inference significantly minimizes latency and memory bandwidth, both of which are required for many real-time services and autonomous and embedded applications. ![](https://developer.download.nvidia.com/images/tensorrt/how-tensor-rt-works.jpg) ### Read the Introductory TensorRT Blog Learn how to apply TensorRT optimizations and deploy a deep learning PyTorch model to GPUs. [Read Blog](https://developer.nvidia.com/blog/speeding-up-deep-learning-inference-using-tensorrt-updated/) ### Watch On-Demand TensorRT Sessions From GTC Learn more about TensorRT and its features from a curated list of webinars at GTC. [Watch Sessions](https://www.nvidia.com/en-us/on-demand/playlist/playList-53110dbc-c11d-4619-b821-987015090afa/) ### Get the Complete Developer Guide See how to get started with TensorRT in this step-by-step developer and API reference guide. [Read Guide](https://docs.nvidia.com/deeplearning/tensorrt/developer-guide/index.html) ### Navigate AI Infrastructure and Performance Learn how to lower your cost per token and get the most out of your AI models with our ebook. [View Ebook](https://www.nvidia.com/en-us/solutions/ai/inference/balancing-cost-latency-and-performance-ebook/) * * * ## TensorRT Ecosystem of Tools ### Large Language Model Inference [NVIDIA TensorRT-LLM](https://developer.nvidia.com/blog/optimizing-inference-on-llms-with-tensorrt-llm-now-publicly-available/) is an open-source library that accelerates and optimizes inference performance of large language models (LLMs) on the NVIDIA AI platform with a simplified Python API. Developers accelerate LLM performance on NVIDIA GPUs in the data center or on workstation GPUs. ### Optimize Neural Networks [NVIDIA Model Optimizer](https://developer.nvidia.com/blog/accelerate-generative-ai-inference-performance-with-nvidia-tensorrt-model-optimizer-now-publicly-available/) is a unified library of state-of-the-art model optimization techniques, including quantization, pruning, speculation, sparsity, and distillation. It compresses deep learning models for downstream deployment frameworks like TensorRT-LLM, TensorRT, vLLM, and SGLang to efficiently optimize inference on NVIDIA GPUs. NVIDIA Model Optimizer also supports training for inference techniques such as Speculative Decoding Module Training, Pruning/Distillation, and Quantization Aware Training through NeMo and Hugging Face frameworks. ### Major Framework Integrations TensorRT integrates directly into [PyTorch](https://developer.nvidia.com/blog/accelerating-inference-up-to-6x-faster-in-pytorch-with-torch-tensorrt/) and [Hugging Face](http://hf.co/blog/optimum-nvidia) to achieve 6X faster inference with a single line of code. TensorRT provides an [ONNX](https://docs.nvidia.com/deeplearning/tensorrt/developer-guide/index.html#fit) parser to import [ONNX](https://github.com/NVIDIA/TensorRT/blob/release/10.9/quickstart/IntroNotebooks/2.%20Using%20PyTorch%20through%20ONNX.ipynb) models from popular frameworks into TensorRT. [MATLAB](https://www.mathworks.com/help/gpucoder/ug/tensorrt-target.html) is integrated with TensorRT through GPU Coder to automatically generate high-performance inference engines for NVIDIA Jetson™, NVIDIA DRIVE™, and data center platforms. ### Deploy, Run, and Scale With NVIDIA Dynamo and Dynamo-Triton Optimized models are deployed, run, and scaled with [NVIDIA Dynamo and Triton](https://www.nvidia.com/en-us/ai-data-science/products/triton-inference-server/) Inference Server inference-serving software that includes TensorRT as a backend. The advantages of using Triton include high throughput with dynamic batching, concurrent model execution, model ensembling, and streaming audio and video inputs. ### Simplify AI deployment on RTX TensorRT for RTX offers an optimized inference deployment solution for NVIDIA RTX GPUs. It enables faster engine build times within 15 to 30 seconds, allowing apps to build inference engines directly on target RTX PCs during app installation or on first run. With a total library footprint of under 200 MB, it also minimizes memory footprint. Engines built with TensorRT for RTX are cross-OS and cross-GPU portable, ensuring a build once, deploy anywhere workflow. ### Accelerate Every Inference Platform Optimize models for applications across the edge, laptops, desktops, and data centers. It powers key NVIDIA solutions—such as NVIDIA TAO, NVIDIA DRIVE, NVIDIA Clara™, and NVIDIA JetPack™—and is integrated with application-specific SDKs, such as NVIDIA NIM™, NVIDIA DeepStream, NVIDIA Riva, NVIDIA Merlin™, NVIDIA AI for Media, NVIDIA Morpheus, and NVIDIA Broadcast Engine. * * * ## Get Started Build and deploy high-performance deep learning and generative AI inference with the TensorRT ecosystem of tools. It offers a variety of inference solutions for different developer requirements. | Use Case | Deployment Platform | Solution | | --- | --- | --- | | Inference for LLMs | Data center GPUs like GB100, H100, A100, etc. | Download TensorLLM SDK TensorRT-LLM is available for free on [GitHub](https://github.com/NVIDIA/TensorRT-LLM/tree/rel). [Download on (GitHub)](https://github.com/NVIDIA/TensorRT-LLM/tree/rel) [Documentation](https://nvidia.github.io/TensorRT-LLM) | | Inference for non-LLMs such as CNNs, diffusions, and transformers Safety-compliant and high-performance inference for automotive embedded systems Inference for non-LLMs in robotics and edge applications in C++ stacks AI model inferencing on RTX PCs | Data center GPUs, embedded, and edge platforms Automotive platform: NVIDIA DRIVE AGX Edge Platform: Jetson, NVIDIA IGX™, etc. | Download TensorRT SDK The TensorRT inference library provides a general-purpose AI compiler and an inference runtime that delivers low latency and high throughput for production applications. [Download SDK](https://developer.nvidia.com/nvidia-tensorrt-download) [Download Container](https://catalog.ngc.nvidia.com/orgs/nvidia/containers/tensorrt) | | AI model inferencing on RTX PCs | NVIDIA GeForce RTX™ and RTX Pro™ GPUs in laptops and desktops | Download TensorRT for RTX TensorRT for RTX is a dedicated inference deployment solution for RTX GPUs. [Download SDK](/tensorrt-rtx) [Documentation](https://docs.nvidia.com/deeplearning/tensorrt-rtx/latest/index.html) | | Model optimizations such as quantization, distillation, and Sparsity. | Data center GPUs such as GB100, H100 | Download NVIDIA Model Optimizer TensorRT Model Optimizer is free on NVIDIA PyPI, with examples and recipes on [GitHub](https://github.com/NVIDIA/TensorRT-Model-Optimizer). [Download on (GitHub)](https://github.com/NVIDIA/TensorRT-Model-Optimizer) [Documentation](https://nvidia.github.io/TensorRT-Model-Optimizer) | * * * ## Get Started With TensorRT Frameworks TensorRT Frameworks add TensorRT compiler functionality to frameworks like PyTorch. ![TensorRT speeds up inference by 36X](https://developer.download.nvidia.com/icons/m48-download.svg) ### Download ONNX and Torch-TensorRT The TensorRT inference library provides a general-purpose AI compiler and an inference runtime that delivers low latency and high throughput for production applications. **ONYX:** [Documentation](https://github.com/NVIDIA/TensorRT/blob/release/10.9/quickstart/IntroNotebooks/2.%20Using%20PyTorch%20through%20ONNX.ipynb) **Torch-TensorRT:** [Download Container](https://catalog.ngc.nvidia.com/orgs/nvidia/containers/pytorch) [Documentation](https://pytorch.org/TensorRT/) ![TensorRT speeds up inference by 36X](https://developer.download.nvidia.com/icons/m48-accellerated-computing-with-cuda-python-256px-blk.png) ### Experience Tripy: Pythonic Inference With TensorRT Experience high-performance inference and excellent usability with Tripy. Expect intuitive APIs, easy debugging with eager mode, clear error messages, and top-notch documentation to streamline your deep learning deployment. [Documentation](https://nvidia.github.io/TensorRT-Incubator/index.html) [Examples](https://github.com/NVIDIA/TensorRT-Incubator/tree/main/tripy/examples) [Contribute](https://github.com/NVIDIA/TensorRT-Incubator/blob/main/tripy/CONTRIBUTING.md) ![TensorRT speeds up inference by 36X](https://developer.download.nvidia.com/icons/m48-digital-deep-learning-institute-talks-training.svg) ### Deploy Get a free license to try [NVIDIA AI Enterprise](https://www.nvidia.com/en-us/data-center/products/ai-enterprise/) in production for 90 days using your existing infrastructure. [Request a 90-Day License](https://enterpriseproductregistration.nvidia.com/?LicType=EVAL&ProductFamily=NVAIEnterprise) * * * ## World-Leading Inference Performance TensorRT was behind NVIDIA’s wins across all [inference performance](https://developer.nvidia.com/blog/tag/inference-performance/) tests in the industry-standard benchmark for [MLPerf Inference](https://www.nvidia.com/en-us/data-center/mlperf/). TensorRT-LLM accelerates the latest large language models for [generative AI](https://www.nvidia.com/en-us/ai-data-science/generative-ai/), delivering up to 8x more performance, 5.3x better TCO, and nearly 6x lower energy consumption. [See All Benchmarks](/deep-learning-performance-training-inference/ai-inference) ### 8x Increase in GPT-J 6B Inference Performance ![TensorRT-LLM on H100 has 8X increase in GPT-J 6B inference performance](https://developer.download.nvidia.com/images/gpt-j-6b-630x354-1.jpg) ### 4x Higher Llama2 Inference Performance ![TensorRT-LLM on H100 has 4X Higher Llama2 Inference Performance](https://developer.download.nvidia.com/images/llama-2-70b-630x354-1.jpg) ### Total Cost of Ownership Lower is better ![TensorRT-LLM has lower total cost of ownership than GPT-J 6B and Llama 2 70B](https://developer.download.nvidia.com/images/cost-of-ownership-630x354-1.jpg) ### Energy Use Lower is better ![TensorRT-LLM has lower energy use than GPT-J 6B and Llama 2 70B](https://developer.download.nvidia.com/images/energy-use-630x354-1.jpg) * * * ## Starter Kits ### Beginner Guide to TensorRT SDK - [View Quick-Start Guide](/tensorrt-getting-started) - [View Quick-Start Notebooks](https://docs.nvidia.com/deeplearning/tensorrt/latest/getting-started/quick-start-guide.html) - Read Blog: [Speeding Up Deep Learning Inference Using NVIDIA TensorRT](/blog/speeding-up-deep-learning-inference-using-tensorrt-updated/) - Read Blog: [Optimizing and Serving Models With TensorRT and Triton](/blog/optimizing-and-serving-models-with-nvidia-tensorrt-and-nvidia-triton/) - Watch Video: [Getting Started With NVIDIA TensorRT](https://www.youtube.com/watch?v=SlUouzxBldU) ### Beginner Guide to TensorRT-LLM SDK - [View Quick-Start Guide](/tensorrt-getting-started) - [View Quick-Start Notebooks](https://nvidia.github.io/TensorRT-LLM/quick-start-guide.html) - Read Blog: [Speeding Up Deep Learning Inference Using NVIDIA TensorRT](/blog/speeding-up-deep-learning-inference-using-tensorrt-updated/) - Read Blog: [Optimizing and Serving Models With TensorRT and Triton](/blog/optimizing-and-serving-models-with-nvidia-tensorrt-and-nvidia-triton/) - Watch Video: [Getting Started With NVIDIA TensorRT](https://www.youtube.com/watch?v=SlUouzxBldU) ### Beginner Guide to NVIDIA Model Optimizer - [Reference Architecture](https://docs.omniverse.nvidia.com/simready/latest/sim-needs/synth-data-gen.html) - [Workflow Guide & Documentation](https://docs.omniverse.nvidia.com/extensions/latest/ext_product-configurator.html) - [Training Courses](https://learn.nvidia.com/courses/course-detail?course_id=course-v1:DLI+S-OV-14+V1) - [NVIDIA Omniverse Blueprint for Precise Visual Generative AI](https://build.nvidia.com/nvidia/conditioning-for-precise-visual-generative-ai) ### Beginner Guide to Torch-TensorRT - Watch Video: [Getting Started With NVIDIA Torch-TensorRT](https://www.youtube.com/watch?v=TU5BMU6iYZ0) - Read Blog: [Accelerate Inference up to 6x in PyTorch](/blog/accelerating-inference-up-to-6x-faster-in-pytorch-with-torch-tensorrt/) - Download Notebook: [Object Detection With SSD](https://github.com/NVIDIA/Torch-TensorRT/blob/master/notebooks/ssd-object-detection-demo.ipynb) (Jupyter Notebook) ### Beginner Guide to TensorRT for RTX - [View Quick Start Guide ](https://docs.nvidia.com/deeplearning/tensorrt-rtx/latest/installing-tensorrt-rtx/installing.html) - [Access Samples and Demos](https://github.com/NVIDIA/TensorRT-RTX/tree/main) - [Read Blog: # Run High-Performance AI Applications with NVIDIA TensorRT for RTX ](https://developer.nvidia.com/blog/run-high-performance-ai-applications-with-nvidia-tensorrt-for-rtx/) - [Access TensorRT for RTX through WindowsML ](https://learn.microsoft.com/en-us/windows/ai/new-windows-ml/get-started?tabs=csharp) * * * ## TensorRT Suite of Products Learning Library OSS (Github) Quantization Quickstart **NVIDIA TensorRT-LLM** The [PyTorch backend](https://nvidia.github.io/TensorRT-LLM/torch.html#quantization) supports FP8 and NVFP4 quantization. Explore [GitHub](https://nvidia.github.io/TensorRT-LLM/torch.html#quantization) to pass quantized models in the Hugging Face model hub, which are generated by TensorRT Model Optimizer. [Link to GitHub](https://nvidia.github.io/TensorRT-LLM/torch.html#quantization) [Link to PyTorch Documentation](https://nvidia.github.io/TensorRT-Model-Optimizer/guides/_pytorch_quantization.html) OSS (Github) Adding a New Model in PyTorch Backend This guide provides a step-by-step process for adding a new model in PyTorch Backend. [Link to GitHub](https://nvidia.github.io/TensorRT-LLM/torch/adding_new_model.html) OSS (Github) Using TensoRT-Model Optimizer for Speculative Decoding ModelOpt’s Speculative Decoding module enables your model to generate multiple tokens in each generation step. This can be useful for reducing the latency of your model and speeding up inference. [Link to GitHub](https://nvidia.github.io/TensorRT-Model-Optimizer/guides/7_speculative_decoding.html) * * * ## TensorRT Ecosystem Ecosystem Widely adopted across industries ![NVIDIA TensorRT is widely adopted by top companies across industries](https://d29g4g2dyqv443.cloudfront.net/sites/default/files/akamai/tensorrt/Logo_farm_GTC.png) * * * ## More Resources ![NVIDIA Developer Forums](https://developer.download.nvidia.com/icons/m48-people-group.svg) ### Explore the Community ![NVIDIA Training and Certification](https://developer.download.nvidia.com/icons/m48-certification-ribbon-2.svg) ### Get Training and Certification ![NVIDIA Inception Program for Startups](https://developer.download.nvidia.com/images/isaac/m48-ai-startup-256px-blk.png) ### Read Top Stories and Blogs * * * ## Ethical AI NVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. When downloaded or used in accordance with our terms of service, developers should work with their supporting model team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse. For more detailed information on ethical considerations for this model, please see the Model Card++ Explainability, Bias, Safety & Security, and Privacy Subcards. Please report security vulnerabilities or NVIDIA AI Concerns [here](https://www.nvidia.com/en-us/support/submit-security-vulnerability/). **Get started with TensorRT SDK today.** [Download Now ](https://developer.nvidia.com/tensorrt/download)