The latest version NVIDIA® Jetson Nano™ Developer Kit-B01 with upgraded functionality is now available. There are now 2-lanes CSI, instead of the previous 1-lane on the carrier board, which allows users to easily play around with binocular vision 


Raspberry Pi Camera Module V2 is released recently, it is perfectly compatible with NVIDIA Jetson Nano Development Kit-B01. High resolution in images and videos and easy to be plugged in, get one and have the best experience for your media project.  


The NVIDIA® Jetson Nano™ Developer Kit delivers the compute performance to run modern AI workloads at unprecedented size, power, and cost. Developers, learners, and makers can now run AI frameworks and models for applications like image classification, object detection, segmentation, and speech processing.


The developer kit can be powered by micro-USB and comes with extensive I/Os, ranging from GPIO to CSI. This makes it simple for developers to connect a diverse set of new sensors to enable a variety of AI applications. It’s incredibly power-efficient, consuming as little as 5 watts.


Jetson Nano is also supported by NVIDIA JetPack, which includes a board support package (BSP), Linux OS, NVIDIA CUDA®, cuDNN, and TensorRT™ software libraries for deep learning, computer vision, GPU computing, multimedia processing, and much more. The software is even available using an easy-to-flash SD card image, making it fast and easy to get started.


The same JetPack SDK is used across the entire NVIDIA Jetson™ family of products and is fully compatible with NVIDIA’s world-leading AI platform for training and deploying AI software. This proven software stack reduces complexity and overall effort for developers. 


Key Features

 Jetson Nano Module

  • 128-core NVIDIA Maxwell™ GPU
  • Quad-core ARM® A57 CPU
  • 4 GB 64-bit LPDDR4
  • 10/100/1000BASE-T Ethernet


 Power Options

  • Micro-USB 5V 2A
  • DC power adapter 5V 4A



  • USB 3.0 Type A
  • USB 2.0 Micro-B
  • HDMI/DisplayPort
  • M.2 Key E
  • Gigabit Ethernet
  • GPIOs, I2 C, I2 S, SPI, UART
  • MIPI-CSI camera connector
  • Fan connector
  • PoE connector

 Kit Contents

  • NVIDIA Jetson Nano module and carrier board
  • Quick Start Guide and Support Guide  

Create more AI possibilities with Grove PiHAT and NVIDIA Jetson Nano

If you want to use Grove sensors with Jetson Nano, grab the grove.py Python library and get your sensors up in running in minutes! Currently, there are more than 20 Grove modules supported on Jetson Nano and we will keep adding more. You can connect Grove modules using Base HAT for Raspberry Pi or Raspberry Pi Zero with Jetson Nano.

Cooling solution for your Jetson Nano

Here we prepared the cooling solution for your Jetson Nano! Over-heating sometimes may cause a shutdown problem. These two cases will help to improve the stability of Jetson Nano.

Add A Camera

The Raspberry Pi Camera Module V2 can work with Jetson Nano well, it will be a perfect camera in your AI project.



We provide a wide selection of AI related products including Machine Learning, Computer Vision, Edge Computing, Speech Recognition & NLP and Neural Networks Acceleration. Check here for more products you may need

We are also calling for feedback and inputs from the developers. Any suggestions on the product features are welcomed at Seeed Forum!


GPU 128-core Maxwell
CPU Quad-core ARM A57 @ 1.43 GHz
Memory 4 GB 64-bit LPDDR4 25.6 GB/s
Storage microSD (not included)
Video Encoder 4K @ 30 | 4x 1080p @ 30 | 9x 720p @ 30 (H.264/H.265)
Video Decoder 4K @ 60 | 2x 4K @ 30 | 8x 1080p @ 30 | 18x 720p @ 30|
Camera 1x MIPI CSI-2 DPHY lanes
Connectivity Gigabit Ethernet, M.2 Key E
Display HDMI 2.0 and eDP 1.4
USB 4x USB 3.0, USB 2.0 Micro-B
Others GPIO, I2C, I2S, SPI, UART
Mechanical 100 mm x 80 mm x 29 mm

Part List:

1 x NVIDIA® Jeston Nano™ Developer Kit


HSCODE 8543709990
USHSCODE 8473301180



Explore and learn from Jetson projects created by us and our community. These projects will help you quickly get started with Jetson Nano.
If you want to use Grove sensors with Jetson Nano, grab the grove.py Python library and get your sensors up and running in minutes!


Write Your Own Review
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  1. Rating
    Funciona perfectamente!
    Llegué temprano y estaba completamente sellado. Debido a la potencia de los gráficos, se calentará un poco, por lo que requiere un ventilador.
  2. Rating
    Fast sucker
    This is hands down the fastest ARM-based device ever used with Ubuntu running on it.

    It has to use a 4amp power support like https://www.amazon.com/gp/product/B01N4HYWAM

    And also have to set the power jumper to use that jack with these
  3. Rating
    Great hardware
    Nice quick quad core arm64 SBC with a decent amount of computing power available from the nVidia GPU.
    It is definitely a specialised board. While it can be used for watching videos, or running OpenGL or GLES games it's really not for that.

    It takes a while to load what it needs to utilise CUDA as a compute device, so the overhead on short tests is large. Once it is loaded it's quite fast, so it's good in real world application.

    One thing to be wary of is the OS image is set to full speed 10 watt mode, but the hardware is configured to use MicroUSB for power ie 5W mode. Either set the OS to lower power mode or use a jumper (not included) to set the board to use a higher wattage power supply via the barrel connector.
    If either of these is not done, the board will crash under load because of insufficient available current.
  4. Rating
    Not too bad, nice package
    I'm pretty happy so far. Setting up python enviro nment a little finicky - suggest you use virtualenv.
    Don't try 16GB, you will run out of space quickly. Get a 160MB/s microSD card at least 32GB. I created a 4GB swapfile which improved dev env build speed
  5. Rating
    Very nice unit
    Early days still, but the Nano is going great. DHL delivery took too long, but worth the wait.


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