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Jetson Orin NX 16GB
Jetson Orin NX 16GB.jpg

Feature: Mini AI computer
GPU: 1024-core NVIDIA Ampere architecture GPU with 32 Tensor Cores
CPU: 8-core Arm® Cortex®-A78AE v8.2 64-bit CPU, 2MB L2 + 4MB L3
Memory: 16GB 128-bit LPDDR5, 102.4GB/s




NVIDIA® Jetson Orin™ NX module delivers up to 100 TOPS and 5x the performance of the last generation for multiple concurrent AI inference pipelines, with high-speed interface support for multiple sensors, it is an ideal solution for a new age of robotics. Jetson Orin NX module delivers up to 100 TOPS of AI performance in a compact and small form factor, with power configurable between 10W and 25W, up to 3x the performance of Jetson AGX Xavier and 5x the performance of Jetson Xavier NX. There are currently 8GB and 16GB versions for users to choose from, and their parameters are slightly different, so please pay attention to the parameters before purchasing.

Basic Kit

Based on Jetson Orin NX Module, with JETSON-IO-BASE-B carrier board, Jetson Orin NX 8GB/16GB DEV KIT provides rich peripheral interfaces such as M.2, HDMI, USB, etc., which is more convenient for users to realize the product performance.

This kit includes the Orin NX Module with 16GB memory, no built-in storage module, and provides up to 100 TOPS AI performance. Comes with a free 128 GB NVMe solid state drive, high-speed reading/writing, and meets the needs of large AI project development.


Based on the JETSON-ORIN-NX-16G-DEV-KIT, it is equipped with an 8MP (3280 x 2464) high-definition camera, suitable for AI intelligent applications such as face recognition, road sign recognition, and license plate recognition.


Based on Kit A, it is equipped 13.3-inch Capacitive Touch Screen LCD (with case), 1920 × 1080 resolution, up to 10-point touch, built-in ferrite Hi-Fi speaker. It can display the images taken by the camera in real-time, to achieve more human-computer interaction in artificial intelligence operations.

User Guide

  • Ubuntu 18.04 host or virtual machine is required to burn the system.


  1. Jetson Orin NX board and JETSON-IO-BASE board.
  2. Ubuntu18.04 or ubuntu 20.04 virtual machine (or PC).
  3. Power adapter.
  4. Jumper caps (or jumper wires).
  5. USB cable (Type-C connector for data transmission).

Hardware Configuration (Enter Recovery Mode)


  • Short the FC REC and GND pins with jump caps or Dupont wires, positioned as shown above, located under the module.
  • Connect the DC power supply to the round power port, wait a moment.
  • Connect the micro USB port of the Jetson board to the Ubuntu host with a USB cable (note that it is a data cable).

Software Cofiguration

  • Open the Ubuntu system and open the browser to download SDK Manager.
  • After downloading, we enter the download path to install, and input the following content on the terminal:
sudo dpkg -i sdkmanager_1.6.1-8175_amd64.deb (Enter according to your version)
  • After installing, the system may report the dependency file can not be found, and you can input the following commands to settle this issue:
sudo apt --fix-broken install
  • Open Ubuntu and run SDK Manager.
  • Click "LOGIN", log in to your NVIDIA account, the browser pops up the link, and enter your registered email and password to log in.


  • Then we successfully log in to SDK Manager.


System Installation

  • Open the Ubuntu computer terminal, and run sdkmanager to open the software.
  • Log in.
  • If the Jetson board is properly recognized as shown in the following figure, if it is connected but not displayed, please click refresh.


  • Select the Jetson Orin NX option for the board type, and select the 16GB version or 8GB version according to the actual situation (if you are using the official kit, select Developer Kit).
  • In the JetPack option, select the latest supported systems, leave the other SDKs unchecked, and click Continue.

Orin NX configuration.png

  • Select Jetson OS, and remove the option of Jetson SDK Components. Check the first agreement at the bottom.

Orin NX Details and License.png

  • Finally, click "Continue" and wait for the programming to finish.
    1. Here, the development board type is selected by default. Be careful not to make a mistake when selecting the type of development board earlier.
    2. Here select Manual Setup-Jetson... (Different motherboard suffix prompts are different.)
    3. Here select NVME.
    4. Here you can choose runtime or preconfig. If you choose runtime, you need to manually configure the system (username, password, language, etc.) later. If you choose preconfig, you can fill in the username and password (you can define it yourself), and nano will be automatically configured during the startup process.
  • After the programming is finished, remove the jumper cap of the carrier board, connect to the monitor, power on again, and follow the prompts to configure the boot (if it is pre-config set, it will directly enter the system after power on).


SDK Installation

Jetpack mainly includes system images, libraries, APIs, developer tools, examples, and some documentation.
The SDK includes TensorRT, cuDNN, CUDA, Multimedia API, Computer Vision, and Developer Tools.

  • TensorRT : High-performance deep learning inference runs for image classification, segmentation, and object detection neural networks, which speeds up deep learning inference and reduces the runtime memory footprint of convolutional and deconvolutional neural networks.
  • cuDNN: The CUDA deep neural network library provides high-performance primitives for deep learning frameworks, including support for convolution, activation functions, and tensor transforms.
  • CUDA : The CUDA Toolkit provides a comprehensive development environment for C and C++ developers building GPU-accelerated applications. The toolkit includes a compiler for NVIDIA GPUs, math libraries, and tools for debugging and optimizing application performance.
  • Multimedia API: The Jetson Multimedia API provides a low-level API for flexible application development.
  • Computer Vision: VPI (Vision Programming Interface) is a software library that provides computer vision/image processing algorithms implemented on PVA1 (Programmable Vision Accelerator), GPUs, and CPUs, where OpenCV, the leading open source library for computer vision, image processing, and machine learning, now features GPU acceleration for real-time operation, where VisionWorks2 is a software development kit for computer vision (CV) and image processing.
  • Developer Tools: Developer Tools CUDA Toolkit provides a comprehensive development environment for C and C++ developers building GPU-accelerated applications. The toolkit includes a compiler for NVIDIA GPUs, math libraries, and tools for debugging and optimizing application performance.

These are some of the features of the SDK.

Install SDK with Commands

sudo apt update
sudo apt install nvidia-jetpack


Install jtop:

sudo apt install python3-pip
sudo pip3 install jetson-stats
sudo reboot

Input after booting:


Maximize the window and use the mouse to select "CTRL 6." Mouse-select the "manual" option within "profiles," then click the [+] symbol next to "speed" to adjust the fan intensity.

NVIDIA Official Resources



Yes, the flashing operation is the same as in the tutorial above.



Technical Support

If you need technical support or have any feedback/review, please click the Submit Now button to submit a ticket, Our support team will check and reply to you within 1 to 2 working days. Please be patient as we make every effort to help you to resolve the issue.
Working Time: 9 AM - 6 PM GMT+8 (Monday to Friday)