NVIDIA Jetson Nano is an embedded system-on-module (SoM) and developer kit from the NVIDIA Jetson family, including an integrated 128-core Maxwell GPU, quad-core ARM A57 64-bit CPU, 4GB LPDDR4 memory, along with support for MIPI CSI-2 and PCIe Gen2 high-speed I/O.
Useful for deploying computer vision and deep learning, Jetson Nano runs Linux and provides 472 GFLOPS of FP16 compute performance with 5-10W of power consumption.
Jetson Nano is currently available as the Jetson Nano Developer Kit for $99, with the production compute module coming in June 2019. See the wiki of the other Jetson's here.
Technical Blog — NVIDIA Jetson Nano Brings AI to Everyone
- 1 Jetson Nano Developer Kit
- 2 Software Support
- 3 Guides and Tutorials
- 4 Ecosystem Products and Sensors
- 5 Getting Help
Jetson Nano Developer Kit
The Jetson Nano Developer Kit is an easy way to get started using Jetson Nano, including the module, carrier board, and software. It costs $99 and is available from distributors worldwide.
- 80x100mm Reference Carrier Board
- Jetson Nano Module with passive heatsink
- Pop-Up Stand
- Getting Started Guide
(the complete devkit with module and heatsink weighs 138 grams)
What You Will Need
- Power Supply
- 5V⎓2A Micro-USB adapter (see Adafruit GEO151UB)
- 5V⎓4A DC barrel jack adapter, 5.5mm OD x 2.1mm ID x 9.5mm length, center-positive (see Adafruit 1446)
- See Power Supply Considerations for more information.
- MicroSD card (16GB UHS-1 recommended minimum)
Ports & Interfaces
- 4x USB 3.0 A (Host)
- USB 2.0 Micro B (Device)
- MIPI CSI-2 x2 (15-position Camera Flex Connector)
- HDMI 2.0
- Gigabit Ethernet (RJ45)
- M.2 Key-E with PCIe x1
- MicroSD card slot
- (3x) I2C, (2x) SPI, UART, I2S, GPIOs
- Follow the Getting Started with Jetson Nano Guide to setup your devkit and format the MicroSD card.
- Plug in an HDMI display into Jetson, attach a USB keyboard & mouse, and apply power to boot it up.
- Visit the Embedded Developer Zone and Jetson Nano Developer Forum to access the latest documentation & downloads.
The devkit is available for $99 from the NVIDIA webstore and global distributors, including:
For the full list, refer to the Region Selector.
- JetPack 4.2
- Linux4Tegra R32.1 (L4T)
- Linux kernel 4.9
- Ubuntu 18.04 LTS aarch64
- CUDA Toolkit 10.0
- cuDNN 7.3.1
- TensorRT 5.0.6
- TensorFlow 1.31.1
- VisionWorks 1.6
- OpenCV 3.3.1
- OpenGL 4.6
- OpenGL ES 3.2
- EGL 1.5
- Vulkan 1.1
- GStreamer 1.14.1
- V4L2 media controller support
Guides and Tutorials
This section contains recipes for following along on Jetson Nano.
- L4T Kernel Development Guide
- Power Supply Considerations
- Upstream Development Guide
- CUDA and VisionWorks Samples
- Preliminary 3D CAD Model
- Mounting a SWAP File
- GPIO Header Pin-out
- Reading Serial Number
- jetson_easy - automatic setup/scripting
- jetson_stats - jtop, service and other tools
- RidgeRun's GstInterpipe (GStreamer plug-in for communication between two or more independent pipelines)
- RidgeRun's GstRRWebRTC (GStreamer plug-in that turns pipelines into WebRTC compliant endpoints)
- RidgeRun's GstRTSPSink (GStreamer element for high performance streaming to multiple computers using the RTSP/RTP protocols)
- RidgeRun's Gstreamer Daemon - GstD (GStreamer framework for controlling audio and video streaming using TCP connection messages)
- RidgeRun's GstCUDA (RidgeRun CUDA ZeroCopy for GStreamer)
- RidgerRun's GstPTZR (GStreamer Pan Tilt Zoom and Rotate Element)
- RidgeRun's GstColorTransfer (GStreamer plug-in that transfers the color scheme from a reference to a target image)
- Hello AI World (jetson-inference)
- TensorFlow 1.13.1 Installer (pip wheel)
- PyTorch 1.1 Installer (pip wheel)
- MXNet 1.4 Installer (pip wheel)
- Deep Learning Inference Benchmarking Instructions
- TensorFlow Object Detection With TensorRT (TF-TRT)
- RidgeRun's GstInference
- RidgeRun's R2Inference
See the NVIDIA AI-IoT GitHub for other coding resources on deploying AI and deep learning.
- NVIDIA JetBot (AI-powered robotics kit)
- jetbot_ros (ROS nodes for JetBot)
- ROS Melodic (ROS install guide)
- ros_deep_learning (jetson-inference nodes)
Ecosystem Products and Sensors
The following are 3rd-party accessories, peripherals, and cameras available for Jetson Nano.
- e-con Systems e-CAM30_CUNANO (3.4 MP MIPI Camera)
- Logitech C920 (USB webcam)
- Leopard Imaging LI-IMX219-MIPI-FF-NANO (IMX219 sensor)
- Raspberry Pi Camera v2 (IMX219 sensor)
- Stereolabs ZED (stereo camera)
- Antmicro Jetson Nano Baseboard (module carrier)
- Auvidea JN30 (module carrier)
- Auvidea JN30-LC (module carrier)
- ConnectTech Nano-Pac (3D-printable enclosure)
- Jetson Nano Case (3D-printable enclosure)
- Jetson NanoMesh (3D-printable enclosure)
- Jetson NanoMesh Mini (3D-printable enclosure)
- jetson_nano_enc (3D-printable enclosure)
- Geekworm Jetson Nano Case (metal enclosure)
See the Power Supply section for more information about selecting proper power adapters.
- M.2 Key-E to Mini-PCIe (PCIe adapter)
- M.2 Key-E to Key-M (PCIe adapter)
- Noctua NF-A4x20 5V PWM (optional fan)
See the Jetson Nano Supported Components List for devices that have been qualified by NVIDIA to work with Jetson Nano.
If you have a technical question or bug report, please visit the Jetson Nano Developer Forum and search or start a new topic.
See the official Support page on Embedded Developer Zone for warranty and RMA information.
For NVIDIA webstore Customer Service, please see the My Account page or contact 1-800-797-6530.