> This page is for version 9.2 (default). > For other versions, use one of these documentation indexes: > - 9.2 (default): https://docs.nvidia.com/deepstream/9.2/llms.txt > - Archives: https://docs.nvidia.com/deepstream/archives/llms.txt > For clean Markdown of any page, append .md to the page URL. > For a complete documentation index, see https://docs.nvidia.com/deepstream/llms.txt. > For AI client integration (Claude Code, Cursor, etc.), connect to the MCP server at https://docs.nvidia.com/deepstream/_mcp/server. # Installation NVIDIA ® DeepStream Software Development Kit (SDK) is an accelerated AI framework to build intelligent video analytics (IVA) pipelines. DeepStream runs on NVIDIA ® T4, NVIDIA® Hopper, NVIDIA ® Blackwell, NVIDIA ® Ampere, NVIDIA ® ADA, NVIDIA® DGX Spark, and platforms such as NVIDIA ® Jetson AGX Thor, NVIDIA ® Jetson Orin and NVIDIA ® IGX Thor. For dGPU platforms Enterprise GPUs are highly recommended for deployments that are expected to run 24x7. Gaming GPUs are not designed to perform in such type of environments. ## Install from the GitHub Mono-repo (Build from Source) The DeepStream mono-repo at [https://github.com/NVIDIA/DeepStream](https://github.com/NVIDIA/DeepStream) is the recommended starting point for DeepStream 9.2. It contains the open-source components (GStreamer plugins, utilities, sample, reference and TAO applications, and Service Maker) together with `build/build.sh`, a single script that downloads the prebuilt proprietary runtime from the GitHub release, installs the open-source dependencies, builds the source components, and installs everything to `/opt/nvidia/deepstream/deepstream-9.2/`. ### Choose an installation method | Method | Use when | | ----------------------------------- | ------------------------------------------------------------------------------------------------------------------------ | | Mono-repo build (`build/build.sh`) | You want the sources, or need to modify, rebuild or extend DeepStream components. | | Release packages (`.deb` / `.tbz2`) | You want a binary installation without building. See the platform sections below. | | Container | You want a ready-made environment with the compute stack installed. See [Docker Containers](/text/DS_docker_containers). | ### Prerequisites * The NVIDIA compute stack (driver, CUDA, cuDNN and TensorRT) for your platform. Install it by following the Jetson, dGPU or dGPU on ARM sections below, and match the versions listed in [Platform and OS Compatibility](#platform-and-os-compatibility). * `git`, `git-lfs`, `cmake` and `wget`. * SBSA / DGX Spark: bare-metal installation is not supported. Build inside the DeepStream SBSA container instead. See [FAQ for DeepStream GitHub Mono-repo](/text/DS_Monorepo_FAQ). For the complete list of system packages per platform, see `build/BUILD.md` in the mono-repo. ### Build and install ```bash # Install Git LFS (required for LFS-tracked sample media) sudo apt-get install git-lfs && git lfs install # Clone the repo with submodules git clone --recurse-submodules https://github.com/NVIDIA/DeepStream.git && cd DeepStream # Build and install. The script prompts for sudo only when writing to system paths. bash build/build.sh ``` `build/build.sh` detects the platform and runs these stages in order: 1. `artifacts`: downloads the prebuilt runtime libraries and sample data from the [DeepStream GitHub release](https://github.com/NVIDIA/DeepStream/releases) and installs them (x86 and Jetson). 2. `deps`: builds and installs the open-source dependencies. 3. Source stages: `gst-utils`, `utils`, `gst-plugins`, `sample_apps`, `yolo`, `tao_apps`, `reference_apps` and `service-maker`. 4. `install.sh`: registers the binaries and installs the `pyservicemaker` Python wheel. A transcript of each run is written to `build/build.log`. Commonly used options: | Option | Description | | --------------------------------- | ----------------------------------------------------------------------------------------- | | `--only=STAGE[,STAGE]` | Build only the named component stage(s). | | `--resume` | Skip stages already completed, after an interrupted build. | | `--skip-deps`, `--skip-artifacts` | Skip the dependency or artifact install when it is already done. | | `--install-method=` | Install the release assets from Debian packages (`deb`, the default) or tarballs (`tar`). | | `--keep-assets` | Keep the downloaded release assets in `artifacts/` after a successful build. | | `-j N` | Number of parallel build jobs. | | `--verbose` | Show sub-make errors. | The defaults are `CUDA_VER=13.2` (13.0 on IGX) and `NVDS_VERSION=9.2.0`. Run `bash build/build.sh --help` for all options. ### Verify the installation After the first install, clear the GStreamer plugin cache, then run the reference application with a sample configuration: ```bash rm -rf ~/.cache/gstreamer-1.0/ cd /opt/nvidia/deepstream/deepstream-9.2/samples/configs/deepstream-app deepstream-app -c source30_1080p_dec_infer-resnet_tiled_display.txt ``` ### Notes * `build/build.sh` does not build `gst-nvdsudp`, `gst-dsexample-cuda` or `gst-nvdstextembedder`. Build these separately by following the README in each component directory. * Some sample applications are platform-specific and are skipped automatically on platforms where they are not supported. * On Jetson Orin, use external storage: the downloaded release assets can exhaust internal storage. * On SBSA / DGX Spark, to modify a single open-source component, run `make` in that component's directory inside the container. See [FAQ for DeepStream GitHub Mono-repo](/text/DS_Monorepo_FAQ). For the full build reference, including uninstall, Service Maker local compilation and packaging, see `build/BUILD.md` in the mono-repo. ## Jetson Setup This section explains how to prepare a Jetson device before installing the DeepStream SDK. > **Note** > > Steps to install DeepStream SDK locally, assume that `~/.local/bin/` has been added to the `~/.bashrc` or `~/.profile` ### Install Jetson SDK components Download NVIDIA SDK Manager from [https://developer.nvidia.com/embedded/jetpack](https://developer.nvidia.com/embedded/jetpack). You will use this to install JetPack 7.2.1 GA (corresponding to L4T 39.2 release) * NVIDIA SDK Manager is a graphical application which flashes and installs the JetPack packages. * The flashing procedure takes approximately 10-30 minutes, depending on the host system. ### Install Dependencies #### Install prerequisite packages Enter the following commands to install the prerequisite packages: ``` $ sudo apt install \ libssl3 \ libssl-dev \ libcurl4-openssl-dev \ libgstreamer1.0-0 \ gstreamer1.0-tools \ gstreamer1.0-plugins-good \ gstreamer1.0-plugins-bad \ gstreamer1.0-plugins-ugly \ gstreamer1.0-libav \ libgstreamer-plugins-base1.0-dev \ libgstrtspserver-1.0-0 \ libjansson4 \ libyaml-cpp-dev \ libmosquitto1 ``` > **Note** > > Sometimes with RTSP streams the application gets stuck on reaching EOS. This is because of an issue in rtpjitterbuffer component. > To fix this issue, > On baremetal, run a script "update_rtpmanager.sh" at `/opt/nvidia/deepstream/deepstream/`. The script should be executed once user installs packages mentioned under "Install Dependencies" section of Quickstart Guide. > On docker, users need to run user_additional_install.sh script to use the prebuilt binaries. ##### Install OpenTelemetry Enter the following commands to install OpenTelemetry: ``` $ cd ~ $ mkdir otel_src && cd otel_src && git clone --branch v1.23.0 --recurse-submodules https://github.com/open-telemetry/opentelemetry-cpp $ cd opentelemetry-cpp $ mkdir build && cd build $ sudo apt install cmake $ cmake .. -DBUILD_SHARED_LIBS=ON -DWITH_OTLP_GRPC=ON -DWITH_OTLP_HTTP=ON -DBUILD_TESTING=OFF $ cmake --build . --parallel $(nproc) $ ctest -V (Optional: run tests for verification) $ sudo cmake --install . ``` > **Note** > > For more information, refer to the OpenTelemetry C++ installation guide: > [https://github.com/open-telemetry/opentelemetry-cpp/blob/v1.23.0/INSTALL.md](https://github.com/open-telemetry/opentelemetry-cpp/blob/v1.23.0/INSTALL.md) #### Install librdkafka (to enable Kafka protocol adaptor for message broker) 1. Install confluent-platform: ``` sudo mkdir -p /etc/apt/keyrings wget -qO - https://packages.confluent.io/deb/8.0/archive.key | gpg \ --dearmor | sudo tee /etc/apt/keyrings/confluent.gpg > /dev/null CP_DIST=$(lsb_release -cs) echo "Types: deb URIs: https://packages.confluent.io/deb/8.0 Suites: stable Components: main Architectures: $(dpkg --print-architecture) Signed-by: /etc/apt/keyrings/confluent.gpg Types: deb URIs: https://packages.confluent.io/clients/deb/ Suites: ${CP_DIST} Components: main Architectures: $(dpkg --print-architecture) Signed-By: /etc/apt/keyrings/confluent.gpg" | sudo tee /etc/apt/sources.list.d/confluent-platform.sources > /dev/null sudo apt-get update && sudo apt-get install confluent-platform ``` 2. Install librdkafka: ``` sudo apt-get install librdkafka-dev ``` #### Install latest NVIDIA BSP packages Installation of JetPack 7.2.1 GA will ensure that latest NVIDIA BSP packages are installed. ### Install the DeepStream SDK > **Note** > > **Recommended for new users:** use **Method 4 (Docker container)** for the > quickest, dependency-free setup. For native installs, the Debian (Method 3) > and tar (Method 2) packages are published as GitHub release assets at > [https://github.com/NVIDIA/DeepStream/releases/tag/v9.2.0](https://github.com/NVIDIA/DeepStream/releases/tag/v9.2.0). * **Method 1**: Using SDK Manager Select `DeepStreamSDK` from the `Additional SDKs` section along with JP 7.2.1 GA software components for installation. * **Method 2**: Using the DeepStream tar package: [https://github.com/NVIDIA/DeepStream/releases/tag/v9.2.0](https://github.com/NVIDIA/DeepStream/releases/tag/v9.2.0) 1. Download the DeepStream 9.2 Jetson tar package `deepstream_sdk_v9.2.0_jetson.tbz2` to the Jetson device. 2. Enter the following commands to extract and install the DeepStream SDK: ``` $ sudo tar -xvf deepstream_sdk_v9.2.0_jetson.tbz2 -C / $ cd /opt/nvidia/deepstream/deepstream-9.2 $ sudo ./install.sh $ sudo ldconfig ``` * **Method 3**: Using the DeepStream Debian package: [https://github.com/NVIDIA/DeepStream/releases/tag/v9.2.0](https://github.com/NVIDIA/DeepStream/releases/tag/v9.2.0) Download the DeepStream 9.2 Jetson Debian package `deepstream-9.2_9.2.0-1_arm64.deb` to the Jetson device. Enter the following command: ``` $ sudo apt-get install ./deepstream-9.2_9.2.0-1_arm64.deb ``` * **Method 4**: Use Docker containers DeepStream Docker container images are distributed through the [DeepStream package on GitHub Container Registry (GHCR)](https://github.com/orgs/NVIDIA/packages/container/package/deepstream) under the `ghcr.io/nvidia/deepstream` image namespace. See the [Docker Containers](/text/DS_docker_containers) section to learn about developing and deploying DeepStream using docker containers. > **Note** > > For scripted or headless downloads of the release assets using `curl` or `wget`, see [FAQ for DeepStream GitHub Mono-repo](/text/DS_Monorepo_FAQ). > **Note** > > Verification: Once DeepStream SDK installation is successful, refer to [Expected output (deepstream-app)](/text/DS_Quickstart#expected-output-deepstream-app) for the expected output. ## dGPU Setup for Ubuntu This section explains how to prepare an `Ubuntu x86_64` system with NVIDIA dGPU devices before installing the DeepStream SDK. > **Note** > > Steps to install DeepStream SDK locally, assume that `~/.local/bin/` has been added to the `~/.bashrc` or `~/.profile` > **Note** > > This document uses the term dGPU (“discrete GPU”) to refer to NVIDIA GPU expansion card products such as NVIDIA Tesla ® T4, NVIDIA ® Hopper, NVIDIA ® Blackwell, NVIDIA ® Ampere, NVIDIA ® ADA, NVIDIA GeForce ® RTX 2080, NVIDIA GeForce ® RTX 3080, NVIDIA GeForce ® RTX 4080, NVIDIA GeForce ® RTX pro 4500 and GeForce ®/NVIDIA RTX/QUADRO. > This version of DeepStream SDK runs on GPUs supported by NVIDIA driver 595.58.03 and NVIDIA TensorRT™ 10.16.0.72 and later versions. ### Prerequisites You must install the following components: * Ubuntu 24.04 * GStreamer 1.24.2 * NVIDIA driver 595.58.03 * CUDA 13.2 * TensorRT 10.16.0.72 #### Remove all previous DeepStream installations Enter the following commands to remove all previous DeepStream 3.0 or prior installations: ``` $ sudo rm -rf /usr/local/deepstream /usr/lib/x86_64-linux-gnu/gstreamer-1.0/libgstnv* /usr/bin/deepstream* /usr/lib/x86_64-linux-gnu/gstreamer-1.0/libnvdsgst* /usr/lib/x86_64-linux-gnu/gstreamer-1.0/deepstream* /opt/nvidia/deepstream/deepstream* $ sudo rm -rf /usr/lib/x86_64-linux-gnu/libv41/plugins/libcuvidv4l2_plugin.so ``` To remove DeepStream 4.0 or later installations: 1. Open the `uninstall.sh` file in `/opt/nvidia/deepstream/deepstream/` 2. Run the following script as `sudo ./uninstall.sh` ### Install Dependencies #### Install prerequisite packages Enter the following commands to install the necessary packages before installing the DeepStream SDK: ``` $ sudo apt install \ libssl3 \ libssl-dev \ libcurl4-openssl-dev \ libgles2-mesa-dev \ libgstreamer1.0-0 \ gstreamer1.0-tools \ gstreamer1.0-plugins-good \ gstreamer1.0-plugins-bad \ gstreamer1.0-plugins-ugly \ gstreamer1.0-libav \ libgstreamer-plugins-base1.0-dev \ libgstrtspserver-1.0-0 \ libjansson4 \ libyaml-cpp-dev \ libjsoncpp-dev \ protobuf-compiler \ libmosquitto1 \ gcc \ make \ git \ python3 ``` > **Note** > > Sometimes with RTSP streams the application gets stuck on reaching EOS. This is because of an issue in rtpjitterbuffer component. > To fix this issue, > On baremetal, run a script "update_rtpmanager.sh" at `/opt/nvidia/deepstream/deepstream/`. The script should be executed once user installs packages mentioned under "Install Dependencies" section of Quickstart Guide. > On docker, users need to run user_additional_install.sh script to use the prebuilt binaries. > **Note** > > For OpenTelemetry installation, follow the steps in [Install OpenTelemetry](#install-opentelemetry) > under [Jetson Setup](#jetson-setup). #### Install CUDA Toolkit 13.2 Run the following commands (reference, [https://developer.nvidia.com/cuda-downloads](https://developer.nvidia.com/cuda-downloads)): ``` $ sudo apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2404/x86_64/3bf863cc.pub $ sudo add-apt-repository "deb https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2404/x86_64/ /" $ sudo apt-get update $ sudo apt-get install cuda-toolkit-13-2 ``` > **Note** > > If you observe following errors while CUDA installation, refer to [https://developer.nvidia.com/blog/updating-the-cuda-linux-gpg-repository-key/](https://developer.nvidia.com/blog/updating-the-cuda-linux-gpg-repository-key/). > > ```text > W: GPG error: https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2404/x86_64 InRelease: The following signatures couldn't be verified because the public key is not available: NO_PUBKEY A4B469963BF863CC > E: The repository 'https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2404/x86_64 InRelease' is no longer signed. > ``` #### Install NVIDIA driver 595.58.03 * Download and install using NVIDIA driver 595.58.03 from NVIDIA Unix drivers page at: [https://www.nvidia.com/en-us/drivers/details/266021/](https://www.nvidia.com/en-us/drivers/details/266021/) * Run the following commands: ``` $ chmod 755 NVIDIA-Linux-x86_64-595.58.03.run $ sudo ./NVIDIA-Linux-x86_64-595.58.03.run --no-cc-version-check ``` > **Note** > > Ensure gdm, lightdm or Xorg service is stopped while installing nvidia driver > > Use command : `sudo service gdm stop` `sudo service lightdm stop` `sudo pkill -9 Xorg` #### Install TensorRT 10.16.0.72 Run the following command to install TensorRT 10.16.0.72: ``` version="10.16.0.72-1+cuda13.2" sudo apt-get install libnvinfer-dev=${version} libnvinfer-dispatch-dev=${version} \ libnvinfer-dispatch10=${version} libnvinfer-headers-dev=${version} libnvinfer-headers-plugin-dev=${version} \ libnvinfer-safe-headers-dev=${version} libnvinfer-lean-dev=${version} libnvinfer-lean10=${version} \ libnvinfer-plugin-dev=${version} libnvinfer-plugin10=${version} libnvinfer-vc-plugin-dev=${version} \ libnvinfer-vc-plugin10=${version} libnvinfer10=${version} libnvonnxparsers-dev=${version} \ libnvonnxparsers10=${version} tensorrt-dev=${version} \ libnvinfer-headers-python-plugin-dev=${version} libnvinfer-win-builder-resource10=${version} ``` > **Note** > > * It is assumed, "deb [https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2404/x86_64/](https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2404/x86_64/)" has been added, as mentioned in CUDA Toolkit 13.2 installation step. > * libnvparsers are deprecated since TRT 8.0. #### Install librdkafka (to enable Kafka protocol adaptor for message broker) 1. Install confluent-platform: ``` sudo mkdir -p /etc/apt/keyrings wget -qO - https://packages.confluent.io/deb/8.0/archive.key | gpg \ --dearmor | sudo tee /etc/apt/keyrings/confluent.gpg > /dev/null CP_DIST=$(lsb_release -cs) echo "Types: deb URIs: https://packages.confluent.io/deb/8.0 Suites: stable Components: main Architectures: $(dpkg --print-architecture) Signed-by: /etc/apt/keyrings/confluent.gpg Types: deb URIs: https://packages.confluent.io/clients/deb/ Suites: ${CP_DIST} Components: main Architectures: $(dpkg --print-architecture) Signed-By: /etc/apt/keyrings/confluent.gpg" | sudo tee /etc/apt/sources.list.d/confluent-platform.sources > /dev/null sudo apt-get update && sudo apt-get install confluent-platform ``` 2. Install librdkafka: ``` sudo apt-get install librdkafka-dev ``` ### Install the DeepStream SDK > **Note** > > **Recommended for new users:** use **Method 3 (Docker container)** for the > quickest, dependency-free setup. For native installs, the Debian (Method 1) > and tar (Method 2) packages are published as GitHub release assets at > [https://github.com/NVIDIA/DeepStream/releases/tag/v9.2.0](https://github.com/NVIDIA/DeepStream/releases/tag/v9.2.0). * **Method 1**: Using the DeepStream Debian package Download the DeepStream 9.2 dGPU Debian package `deepstream-9.2_9.2.0-1_amd64.deb` : [https://github.com/NVIDIA/DeepStream/releases/tag/v9.2.0](https://github.com/NVIDIA/DeepStream/releases/tag/v9.2.0) Enter the command: ``` $ sudo apt-get install ./deepstream-9.2_9.2.0-1_amd64.deb ``` * **Method 2**: Download the DeepStream tar package: [https://github.com/NVIDIA/DeepStream/releases/tag/v9.2.0](https://github.com/NVIDIA/DeepStream/releases/tag/v9.2.0) Navigate to the location of the downloaded DeepStream package to extract and install the DeepStream SDK: ``` $ sudo tar -xvf deepstream_sdk_v9.2.0_x86_64.tbz2 -C / $ cd /opt/nvidia/deepstream/deepstream-9.2/ $ sudo ./install.sh $ sudo ldconfig ``` * **Method 3**: Use Docker containers DeepStream Docker container images are distributed through the [DeepStream package on GitHub Container Registry (GHCR)](https://github.com/orgs/NVIDIA/packages/container/package/deepstream) under the `ghcr.io/nvidia/deepstream` image namespace. See the [Docker Containers](/text/DS_docker_containers) section to learn about developing and deploying DeepStream using docker containers. > **Note** > > For scripted or headless downloads of the release assets using `curl` or `wget`, see [FAQ for DeepStream GitHub Mono-repo](/text/DS_Monorepo_FAQ). > **Note** > > Verification: Once DeepStream SDK installation is successful, refer to [Expected output (deepstream-app)](/text/DS_Quickstart#expected-output-deepstream-app) for the expected output. ## dGPU on ARM Setup for Ubuntu This section explains how to prepare an `Ubuntu aarch64` system with NVIDIA dGPU devices before installing the DeepStream SDK. You must install the following components: * NVIDIA driver 595.58.03 * Docker * Nvidia Container Toolkit ### Install NVIDIA driver 595.58.03 * Download and install using NVIDIA driver 595.58.03 from NVIDIA Unix drivers page at: [https://www.nvidia.com/en-us/drivers/details/266021/](https://www.nvidia.com/en-us/drivers/details/266021/) * Run the following commands: ``` $ chmod 755 NVIDIA-Linux-aarch64-595.58.03.run $ sudo ./NVIDIA-Linux-aarch64-595.58.03.run --no-cc-version-check ``` > **Note** > > Ensure gdm, lightdm or Xorg service is stopped while installing Nvidia driver > > Use command : `sudo service gdm stop` `sudo service lightdm stop` `sudo pkill -9 Xorg` ### Run dGPU on ARM Docker (SBSA) > **Note** > > A compatible NVIDIA driver must be installed on the host for SBSA. The driver is not included in the Docker image; the NVIDIA Container Toolkit makes the host driver available to the container. 1. Pull the DeepStream Triton Inference Server docker ``` docker pull ghcr.io/nvidia/deepstream:9.2-triton-sbsa-dgx-spark ``` 2. Start the docker ``` sudo docker run -it --rm --runtime=nvidia --network=host -e NVIDIA_DRIVER_CAPABILITIES=compute,utility,video,graphics --gpus all -e DISPLAY=:0 -v /tmp/.X11-unix:/tmp/.X11-unix -v /etc/X11:/etc/X11 ghcr.io/nvidia/deepstream:9.2-triton-sbsa-dgx-spark ``` > **Note** > > `--privileged` is omitted above; add it only if the container needs access to all host devices or low-level driver operations. It grants near-root host access, so on shared or production systems prefer `--device` / `--cap-add` instead. ## DGX Spark Setup for Ubuntu This section explains how to prepare a [NVIDIA DGX Spark](https://www.nvidia.com/en-us/products/workstations/dgx-spark/) system before installing DeepStream SDK. DGX Spark is an aarch64-based desktop AI system running Ubuntu with an integrated NVIDIA Grace Blackwell GPU. > **Note** > > Native DeepStream SDK installation on DGX Spark is **not** supported. Use the Docker > container method described below. > **Note** > > A compatible NVIDIA driver must be installed on the host for DGX Spark. The driver is not included in the Docker image; the NVIDIA Container Toolkit makes the host driver available to the container. Refer [NVIDIA DGX Spark Release Notes](https://docs.nvidia.com/dgx/dgx-spark/release-notes.html#current-software-versions) for stack details. ### Run DGX Spark Docker 1. Pull the DeepStream Triton Inference Server docker ``` docker pull ghcr.io/nvidia/deepstream:9.2-triton-sbsa-dgx-spark ``` 2. Start the docker ``` sudo docker run -it --rm --runtime=nvidia --network=host \ -e NVIDIA_DRIVER_CAPABILITIES=compute,utility,video,graphics \ --gpus all \ -e DISPLAY=:0 \ -v /tmp/.X11-unix:/tmp/.X11-unix \ -v /etc/X11:/etc/X11 \ ghcr.io/nvidia/deepstream:9.2-triton-sbsa-dgx-spark ``` > **Note** > > * `--privileged` is omitted above; add it only if the container needs access to all host devices or low-level driver operations. It grants near-root host access, so on shared or production systems prefer `--device` / `--cap-add` instead. > * The triton docker for DGX Spark is based on the SBSA Tritonserver image and has Ubuntu 24.04. > * When the triton docker is launched for the first time, it might take a few minutes to start since it has to generate compute cache. > * DGX Spark docker currently supports only nv3dsink for video display. nveglglessink is not supported. > * VIC is not supported on DGX Spark. Set tiler `compute-hw=1` (GPU) in your application config. ## dGPU Setup for RedHat Enterprise Linux (RHEL) DeepStream for RHEL is not supported in this release. ## Platform and OS Compatibility The following table provides information about platform and operating system compatibility in the current and earlier versions of DeepStream. **Jetson model Platform and OS Compatibility** | DS release | DS 6.1.1 | DS 6.2 | DS 6.3 | DS 6.4 | DS 7.0 | DS 7.1 | DS 8.0 | DS 9.0 | DS 9.1 | DS 9.2 | | ---------------- | ---------------------------------- | ------------------------------------------ | ----------------------------------------------------- | ---------------------------- | ---------------------------- | ---------------------------- | ---------------- | ---------------- | --------------------- | ------------------------------- | | Jetson platforms | AGX Xavier, Jetson NX, Jetson Orin | Xavier AGX , Xavier NX, AGX Orin, Orin NX | Xavier AGX , Xavier NX, AGX Orin, Orin NX, Orin nano | AGX Orin, Orin NX, Orin nano | AGX Orin, Orin NX, Orin nano | AGX Orin, Orin NX, Orin nano | AGX Thor | AGX Thor | AGX Thor, Jetson Orin | AGX Thor, Jetson Orin, IGX Thor | | OS | L4T Ubuntu 20.04 | L4T Ubuntu 20.04 | L4T Ubuntu 20.04 | L4T Ubuntu 22.04 | L4T Ubuntu 22.04 | L4T Ubuntu 22.04 | L4T Ubuntu 24.04 | L4T Ubuntu 24.04 | L4T Ubuntu 24.04 | L4T Ubuntu 24.04 | | JetPack release | 5.0.2 GA Revision 1 | 5.1 GA | 5.1.2 GA | 6.0 DP | 6.0 GA | 6.1 GA | 7.0 GA | 7.1 GA | 7.2 GA | 7.2.1 GA | | L4T release | 35.1 | 35.2.1 | 35.4 | 36.2 | 36.3 | 36.4 | 38.2 | 38.4 | 39.2 | 39.2 | | CUDA release | CUDA 11.4 | CUDA 11.4 | CUDA 11.4 | CUDA 12.2 | CUDA 12.2 | CUDA 12.6 | CUDA 13.0 | CUDA 13.0 | CUDA 13.2 | CUDA 13.2 | | cuDNN release | cuDNN 8.4.1.50+ | cuDNN 8.6.0.166+ | cuDNN 8.6.0.166+ | cuDNN 8.9.4.25+ | cuDNN 8.9.4.25+ | cuDNN 9.3.0 | CuDNN 9.12.0 | CuDNN 9.12.0 | CuDNN 9.20.0.46 | CuDNN 9.20.0.48 | | TensorRT release | TRT 8.4.1.5 | TRT 8.5.2.2 | TRT 8.5.2.2 | TRT 8.6.2.3 | TRT 8.6.2.3 | TRT 10.3.0.31 | TRT 10.13.2.6 | TRT 10.13.2.6 | TRT 10.16.1.7 | TRT 10.16.1.7 | | OpenCV release | OpenCV 4.2.0 | OpenCV 4.2.0 | OpenCV 4.5.4 | OpenCV 4.8.0 | OpenCV 4.8.0 | OpenCV 4.8.0 | OpenCV 4.8.0 | OpenCV 4.8.0 | OpenCV 4.8.0 | OpenCV 4.8.0 | | VisionWorks | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | | GStreamer | GStreamer 1.16.2 | GStreamer 1.16.3 | GStreamer 1.16.3 | GStreamer 1.20.3 | GStreamer 1.20.3 | GStreamer 1.20.3 | GStreamer 1.24.2 | GStreamer 1.24.2 | GStreamer 1.24.2 | GStreamer 1.24.2 | | Docker image | deepstream-l4t:6.1.1 | deepstream-l4t:6.2 | deepstream-l4t:6.3 | deepstream:6.4 | deepstream:7.0 | deepstream:7.1 | deepstream:8.0 | deepstream:9.0 | deepstream:9.1 | deepstream:9.2 | **dGPU model Platform and OS Compatibility** | DS release | DS 6.1.1 | DS 6.2 | DS 6.3 | DS 6.4 | DS 7.0 | DS 7.1 | DS 8.0 | DS 9.0 | DS 9.1 | DS 9.2 | | ----------------- | -------------------------------------------------------- | --------------------------------------------------------------------- | --------------------------------------------------------------------- | --------------------------------------------------------------------- | --------------------------------------------------------------------- | --------------------------------------------------------------- | -------------------------------------- | -------------------------------------- | -------------------------------------- | -------------------------------------- | | GPU platforms | T4, V100, A2, A10, A30, A100, RTX Ampere (Ax000/RTX30x0) | T4, V100, A2, A10, A30, A100, RTX Ampere (Ax000/RTX30x0), Hopper, ADA | T4, V100, A2, A10, A30, A100, RTX Ampere (Ax000/RTX30x0), Hopper, ADA | T4, V100, A2, A10, A30, A100, RTX Ampere (Ax000/RTX30x0), Hopper, ADA | T4, V100, A2, A10, A30, A100, RTX Ampere (Ax000/RTX30x0), Hopper, ADA | T4, A2, A10, A30, A100, RTX Ampere (Ax000/RTX30x0), Hopper, ADA | Turing, Ampere, Hopper, ADA, Blackwell | Turing, Ampere, Hopper, ADA, Blackwell | Turing, Ampere, Hopper, ADA, Blackwell | Turing, Ampere, Hopper, ADA, Blackwell | | OS | Ubuntu 20.04 | Ubuntu 20.04 | Ubuntu 20.04 | Ubuntu 22.04 | Ubuntu 22.04 | Ubuntu 22.04 | Ubuntu 24.04 | Ubuntu 24.04 | Ubuntu 24.04 | Ubuntu 24.04 | | GCC | GCC 9.4.0 | GCC 9.4.0 | GCC 9.4.0 | GCC 11.4.0 | GCC 11.4.0 | GCC 11.4.0 | GCC 11.4.0 | GCC 11.4.0 | GCC 11.4.0 | GCC 11.4.0 | | CUDA release | CUDA 11.7.1 | CUDA 11.8 | CUDA 12.1 | CUDA 12.2 | CUDA 12.2 | CUDA 12.6 | CUDA 12.8 | CUDA 13.1 | CUDA 13.2 | CUDA 13.2 | | cuDNN release | cuDNN 8.4.1.50+ | cuDNN 8.7.0.84-1+ | cuDNN 8.8.1.3-1+ | cuDNN 8.9.4.25-1+ | cuDNN 8.9.6.50-1+ | cuDNN 9.3.0 | CuDNN 9.7.1 | CuDNN 9.18.0 | CuDNN 9.20.0.48 | CuDNN 9.20.0.48 | | TRT release | TRT 8.4.1.5 | TRT 8.5.2.2 | TRT 8.5.3.1 | TRT 8.6.1.6 | TRT 8.6.1.6 | TRT 10.3.0.26 | TRT 10.9.0.34 | TRT 10.14.1.48 | TRT 10.16.0.72 | TRT 10.16.0.72 | | Display Driver | R515.65.01 | R525.85.12 | R525.125.06 | R535.104.12 | R535.161.08 | R535.183.06(Data Center GPUs), R560.35.03(RTX GPUs) | R570.133.20 | R590.48.01 | R595.58.03 | R595.58.03 | | VideoSDK release | SDK 9.1 | SDK 9.1 | SDK 9.1 | SDK 9.1 | SDK 9.1 | SDK 9.1 | SDK 9.1 | SDK 9.1 | SDK 9.1 | SDK 9.1 | | OFSDK release | 2.0.23 | 2.0.23 | 2.0.23 | 2.0.23 | 2.0.23 | 2.0.23 | 2.0.23 | 2.0.23 | 2.0.23 | 2.0.23 | | GStreamer release | GStreamer 1.16.2 | GStreamer 1.16.3 | GStreamer 1.16.3 | GStreamer 1.20.3 | GStreamer 1.20.3 | GStreamer 1.20.3 | GStreamer 1.24.2 | GStreamer 1.24.2 | GStreamer 1.24.2 | GStreamer 1.24.2 | | OpenCV release | OpenCV 4.2.0 | OpenCV 4.2.0 | OpenCV 4.2.0 | OpenCV 4.5.4 | OpenCV 4.5.4 | OpenCV 4.5.4 | OpenCV 4.6 | OpenCV 4.6 | OpenCV 4.6 | OpenCV 4.6 | | Docker image | deepstream:6.1.1 | deepstream:6.2 | deepstream:6.3 | deepstream:6.4 | deepstream:7.0 | deepstream:7.1 | deepstream:8.0 | deepstream:9.0 | deepstream:9.1 | deepstream:9.2 | | NVAIE release | NA | NVAIE-3.x | NVAIE-3.x | NVAIE-4.x | NA | NA | NA | NA | NA | NA | > **Note** > > By default, OpenCV has been deprecated. However, OpenCV can be enabled in plugins such as nvinfer (nvdsinfer) and dsexample (gst-dsexample) by setting `WITH_OPENCV=1` in the Makefile of these components. Please refer component README for more instructions. > > NA: "Not Applicable" DeepStream 9.2 support is currently enabled by SBSA triton docker. SBSA category currently supports GH200, GB200, and DGX Spark.