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Jetson Nano production modules will now be available until January 2027, an extension of two years. JetPack 4 sustaining releases will continue to support Jetson Nano devkits and production modules.

For customers looking for a production module with a 5-10 year operating life in a production environment, we recommend you check out reComputer of Jetson full system. With the same carrier board dimension as Jetson Dev Kit,reComputer J1010 and J1020, powered by Jetson Nano, built with rich I/Os of USB3, HDMI, GbE, M.2 key E and . If you are also looking for Jetson Xavier NX module-powered full system, please jump to reComputer J2021!

Description

Time to bring AI power to the edge with advanced embedded systems! Jetson Nano will be your choice for developing systems for robotics, drone, video analytics, traffic management with the vehicle and people detection, retail automatic check-out machine, or terminologies like image classification, object detection, segmentation, and speech processing you must have heard about! Learn more about what is and why edge AI in this blog.

NVIDIA® Jetson Nano™ Developer Kit brings modern AI to makers, learners, and all embedded developers within compact size and powerful processing that lets you run multiple neural networks in parallel.

Seeed is a reseller of NVIDIA embedded systems and a preferred partner of the NVIDIA Jetson ecosystem for edge AI. Explore more carrier boards, full system devices, customization services, use cases, and developer tools at Seeed Jetson Ecosystem!

Compare reComputer J1020v2 and Nano B01

Product

reComputer J1020 v2

NVIDIA Jetson Nano Developer Kit-B01

Module

Jetson Nano 4GB (production)

Nano (not production)

Storage

16 GB eMMC

MicroSD slot(on the module)

Video Encoder

4K30 | 2x1080p60 | 4x1080p30 | 4x720p60 | 9x720p30

(H.265 & H.264)

4Kp30 | 4x 1080p30 | 9x 720p30 (H.264/H.265)

Video Decoder

4K60 | 2x 4K30 | 4x 1080p60 | 8x 1080p30 | 9x 720p60

(H.265 & H.264)

4Kp60 | 2x 4Kp30 | 8x 1080p30 | 18x 720p30 (H.264/H.265)

Gigabit Ethernet

1*RJ45 Gigabit Ethernet Connector (10/100/1000)

USB

4 * USB 3.0 Type-A ;

1 * Micro-USB port for device mode;

4 * USB 3.0 Type-A;

1 * Micro-USB port for 5V power input or for device mode

CSI Camera Connect

2*CSI Camera (15 pos, 1mm pitch, MIPI CSI-2 )

Display

1*HDMI Type A;

1*DP

1*HDMI Type A;

1*DP

FAN

1* FAN (5V PWM)

M.2 KEY E

1*M.2 Key E (Disabled)

1*M.2 Key E

M.2 KEY M

1*M.2 Key M

-

RTC

1*RTC Socket

-

Multifunctional port

1* 40-Pin header

Power

DC Jack 12V/2A

DC Jack 5V⎓4A;

Micro-USB 5V⎓2A

Mechanical

130mm x120mm x 50mm (with case)

100 mm x 80 mm x 29 mm

For customers looking for a production module with a 5-10 year operating life in a production environment, we recommend you check out reComputer J1020v2 full system, with the same carrier board dimension as Jetson Dev Kit, If you are also looking for Jetson Xavier NX powered full system, please jump to reComputer J2021!

Get started with Jetson Nano and build your first AI project

AI is not hard anymore to get started. Just insert a microSD card with the system image, boot the developer kit, and begin using the same NVIDIA JetPack SDK used across the entire NVIDIA Jetson platform. JetPack is compatible with NVIDIA’s world-leading AI platform for training and deploying AI software, reducing complexity and effort for developers.

Want to take your next project to the next level with AI? How long time from unknown about AI to building AI? Two days are enough! NVIDIA prepared this deep learning tutorial of Hello AI World and Two Days to a Demo.

Besides grabbing Jetson Nano Dev Kit or reComputer J1010/J1020, you might need to connect with cameras, off-the-shelf Grove sensors, or controlling actuators with GPIO.

From 0.1 to ∞, unlock more AI possibilities!

Bring AI to market easier with these tools:

  • Edge AI no code Vision tool, Seeed latest open-source project for deploying AI application within 3 nodes.

  • NVIDIA DeepStream SDK delivers a complete streaming analytics toolkit for AI-based multi-sensor processing and video and image understanding on Jetson.

  • NVIDIA TAO tool kit, built on TensorFlow and PyTorch, is a low-code version of the NVIDIA TAO framework that accelerates the model training

  • alwaysAI: build, train, and deploy computer vision applications directly at the edge of reComputer. Get free access to 100+ pre-trained Computer Vision Models and train custom AI models in the cloud in a few clicks via enterprise subscription. Check out our wiki guide to get started with alwaysAI.

  • Edge Impulse: the easiest embedded machine learning pipeline for deploying audio, classification, and object detection applications at the edge with zero dependencies on the cloud.

  • Roboflow provides end-to-end computer vision experience from datasets to deployment. Roboflow Universe provides 90,000+ datasets with 66+ million images available for building computer vision models.

  • YOLOv5 by Ultralytics: use transfer learning to realize 21 FPS at the edge with YOLOv5 and fewer datasets. Have a look our step-by-step wiki tutorials.

  • Deci: optimize your models' performance. Check webinar at Automatically Benchmark and Optimize Runtime Performance on NVIDIA Jetson Nano and Xavier NX Devices

Remote Fleet Management

Enable secure OTA and remote device management with Allxon. Unlock 90 days free trial with code H4U-NMW-CPK.

Robot and ROS Development

  • The NVIDIA® Isaac ROS is a collection of hardware accelerated packages that make it easier for ROS developers to build high-performance solutions on NVIDIA hardware.

  • Cogniteam Nimbus is a cloud-based solution that allows developers to manage autonomous robots more effectively. Nimbus platform supports NVIDIA® Jetson™ and ISAAC SDK and GEMs out-of-the-box. Check out our webinar on Connect your ROS Project to the Cloud with Nimbus.

No matter whether you want to build up your first AI project or working on AI development and looking for a reliable hardware choice for mass production, Seeed's Jetson powered products will help you from edge AI 0.1 to ∞.

For ISV and solution integrator enterprises, welcome checkout our free Edge AI Partner Program to deliver AI solutions together with Seeed's licensing, customization and manufacturing service.

ECCN/HTS

HSCODE 8543709990
USHSCODE 8471490000
UPC
EUHSCODE 8543709099
COO CHINA
RoHS 1

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REVIEWS

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  1. Product Quality
    100%
    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 curre
    By
  2. Product Quality
    80%
    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
    https://www.amazon.com/gp/product/B00N552DWK
    By
  3. Product Quality
    100%
    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
    By
  4. Product Quality
    80%
    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.
    By
  5. Product Quality
    100%
    Great products
    It is important to connect both the usb and network when setting it up as the USB connection is used for flashing the OS onto Xavier whereas the network connection is needed for building the other Nvidia components.
    By default it runs with only 4 cores enabled and the other 4 need to be enabled.
    Note that the device gets hot and the fan does not kick in so if you added the m2 pci storage then get a small fan to cool the device as the heat will kill the m2 module.
    By

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