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Convert NVIDIA TensorRT guide to Jupyter notebook format
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‎articles/run-nvidia.ipynb‎

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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Optimizing OpenAI GPT-OSS Models with NVIDIA TensorRT-LLM"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"This notebook provides a step-by-step guide on how to optimizing `gpt-oss` models using NVIDIA's TensorRT-LLM for high-performance inference. TensorRT-LLM provides users with an easy-to-use Python API to define Large Language Models (LLMs) and support state-of-the-art optimizations to perform inference efficiently on NVIDIA GPUs. TensorRT-LLM also contains components to create Python and C++ runtimes that orchestrate the inference execution in performant way.\n",
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"\n",
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"\n",
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"TensorRT-LLM supports both models:\n",
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"- `gpt-oss-20b`\n",
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"- `gpt-oss-120b`\n",
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"\n",
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"In this guide, we will run `gpt-oss-20b`, if you want to try the larger model or want more customization refer to [this](https://github.com/NVIDIA/TensorRT-LLM/tree/main/docs/source/blogs/tech_blog) deployment guide."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Prerequisites"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Hardware\n",
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"To run the 20B model and the TensorRT-LLM build process, you will need an NVIDIA GPU with at least 20 GB of VRAM.\n",
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"\n",
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"> Recommended GPUs: NVIDIA RTX 50 Series (e.g.RTX 5090), NVIDIA H100, or L40S.\n",
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"\n",
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"### Software\n",
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"- CUDA Toolkit 12.8 or later\n",
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"- Python 3.12 or later\n",
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"- Access to the Orangina model checkpoint from Hugging Face"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Installling TensorRT-LLM"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Using NGC\n",
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"\n",
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"Pull the pre-built TensorRT-LLM container for GPT-OSS from NVIDIA NGC.\n",
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"This is the easiest way to get started and ensures all dependencies are included.\n",
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"\n",
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"`docker pull nvcr.io/nvidia/tensorrt-llm/release:gpt-oss-dev`\n",
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"`docker run --gpus all -it --rm -v $(pwd):/workspace nvcr.io/nvidia/tensorrt-llm/release:gpt-oss-dev`\n",
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"\n",
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"## Using Docker (build from source)\n",
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"\n",
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"Alternatively, you can build the TensorRT-LLM container from source.\n",
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"This is useful if you want to modify the source code or use a custom branch.\n",
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"See the official instructions here: https://github.com/NVIDIA/TensorRT-LLM/tree/feat/gpt-oss/docker\n",
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"\n",
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"The following commands will install required dependencies, clone the repository,\n",
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"check out the GPT-OSS feature branch, and build the Docker container:\n",
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" ```\n",
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"#Update package lists and install required system packages\n",
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"sudo apt-get update && sudo apt-get -y install git git-lfs build-essential cmake\n",
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"\n",
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"# Initialize Git LFS (Large File Storage) for handling large model files\n",
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"git lfs install\n",
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"\n",
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"# Clone the TensorRT-LLM repository\n",
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"git clone https://github.com/NVIDIA/TensorRT-LLM.git\n",
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"cd TensorRT-LLM\n",
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"\n",
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"# Check out the branch with GPT-OSS support\n",
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"git checkout feat/gpt-oss\n",
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"\n",
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"# Initialize and update submodules (required for build)\n",
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"git submodule update --init --recursive\n",
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"\n",
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"# Pull large files (e.g., model weights) managed by Git LFS\n",
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"git lfs pull\n",
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"\n",
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"# Build the release Docker image\n",
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"make -C docker release_build\n",
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"\n",
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"# Run the built Docker container\n",
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"make -C docker release_run \n",
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"```"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"TensorRT-LLM will be available through pip soon"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"> Note on GPU Architecture: The first time you run the model, TensorRT-LLM will build an optimized engine for your specific GPU architecture (e.g., Hopper, Ada, or Blackwell). If you see warnings about your GPU's CUDA capability (e.g., sm_90, sm_120) not being compatible with the PyTorch installation, ensure you have the latest NVIDIA drivers and a matching CUDA Toolkit version for your version of PyTorch."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Verifying TensorRT-LLM Installation"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"from tensorrt_llm import LLM, SamplingParams"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Utilizing TensorRT-LLM Python API"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"In the next code cell, we will demonstrate how to use the TensorRT-LLM Python API to:\n",
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"1. Download the specified model weights from Hugging Face (using your HF_TOKEN for authentication).\n",
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"2. Automatically build the TensorRT engine for your GPU architecture if it does not already exist.\n",
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"3. Load the model and prepare it for inference.\n",
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"4. Run a simple text generation example to verify everything is working.\n",
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"\n",
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"**Note**: The first run may take several minutes as it downloads the model and builds the engine.\n",
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"Subsequent runs will be much faster, as the engine will be cached."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"llm = LLM(model=\"openai/gpt-oss-20b\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"prompts = [\"Hello, my name is\", \"The capital of France is\"]\n",
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"sampling_params = SamplingParams(temperature=0.8, top_p=0.95)\n",
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"for output in llm.generate(prompts, sampling_params):\n",
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" print(f\"Prompt: {output.prompt!r}, Generated text: {output.outputs[0].text!r}\")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Conclusion and Next Steps\n",
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"Congratulations! You have successfully optimized and run a large language model using the TensorRT-LLM Python API.\n",
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"\n",
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"In this notebook, you have learned how to:\n",
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"- Set up your environment with the necessary dependencies.\n",
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"- Use the `tensorrt_llm.LLM` API to download a model from the Hugging Face Hub.\n",
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"- Automatically build a high-performance TensorRT engine tailored to your GPU.\n",
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"- Run inference with the optimized model.\n",
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"\n",
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"\n",
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"You can explore more advanced features to further improve performance and efficiency:\n",
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"\n",
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"- Benchmarking: Try running a [benchmark](https://nvidia.github.io/TensorRT-LLM/performance/performance-tuning-guide/benchmarking-default-performance.html#benchmarking-with-trtllm-bench) to compare the latency and throughput of the TensorRT-LLM engine against the original Hugging Face model. You can do this by iterating over a larger number of prompts and measuring the execution time.\n",
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"\n",
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"- Quantization: TensorRT-LLM [supports](https://github.com/NVIDIA/TensorRT-Model-Optimizer) various quantization techniques (like INT8 or FP8) to reduce model size and accelerate inference with minimal impact on accuracy. This is a powerful feature for deploying models on resource-constrained hardware.\n",
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"\n",
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"- Deploy with NVIDIA Dynamo: For production environments, you can deploy your TensorRT-LLM engine using the [NVIDIA Dynamo](https://docs.nvidia.com/dynamo/latest/) for robust, scalable, and multi-model serving.\n",
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"\n"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.12.3"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 4
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}

‎articles/run-nvidia.md‎

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‎authors.yaml‎

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# You can optionally customize how your information shows up cookbook.openai.com over here.
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# If your information is not present here, it will be pulled from your GitHub profile.
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jayrodge:
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name: "Jay Rodge"
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website: "https://www.linkedin.com/in/jayrodge/"
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avatar: "https://developer-blogs.nvidia.com/wp-content/uploads/2024/05/Jay-Rodge.png"
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rajpathak-openai:
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name: "Raj Pathak"
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website: "https://www.linkedin.com/in/rajpathakopenai/"

‎registry.yaml‎

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# should build pages for, and indicates metadata such as tags, creation date and
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# authors for each page.
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- title: Using NVIDIA TensorRT-LLM to run the 20B model
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path: examples/articles/run-nvidia.ipynb
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date: 2025-08-05
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authors:
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- jayrodge
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tags:
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- nvidia
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- tensorrt-llm
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- title: Temporal Agents with Knowledge Graphs
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path: examples/partners/temporal_agents_with_knowledge_graphs/temporal_agents_with_knowledge_graphs.ipynb
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date: 2025-07-22

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