Lets know about what is NVIDIA Jetson Nano Developer Kit:

NVIDIA-Jetson-Nano-Developer-Kit

Getting started with NVIDIA jetson Nano Developer Kit, It has System on a module which is SOM. and it also have carrier board. Its CPU is Quard core 1.43GHz A57 ARM. GPU Maxwell NVIDIA is 128 CUDA core. Decoder specifications are H.264 & H.265 and decode up to 4K 60fps.
Encoder specifications are H.264 & H.265 and encode upto 4k 30fps. its RAM is 4GB 64 bit LPDDR4. You can insert microSD for storage. We have 260 pin edge connector. M.2 (E-keyed) for a wireless card. its Serial port header is UART. Buttons Headers are inc power and reset. We can use Rasp Pi V2 camera on MIPI CSI.

Here is the jumper for Power Selection. Fan header is 4 pin. its also have Power over Ethernet (POE) header.
GPIO (Rasp Pi compatible) is 40 pin. it has micro USB or barrel jack power. Jetson Nano SOM is rated at 5-10 watt. Carrier board is rated 0.5-1.25 watts. it has 4 ports of USB 3.0 which is Gigabit Ethernet. Both HDMI and DisplayPort
are 4K.

NVIDIA Jetson Nano Developer Kit Specifications and Key Features:

Use of the NVIDIA Jetson Nano is:

1. It runs multiple neural networks in parallel.
2. It do Image classification and also segmentation.
3. It do Object detection
4. it also processing Speech.

NVIDIA Jetson Nano Developer Kit Specifications and Key Features:

      Jetbot Nano Module

it has GPU NVIDIA of 128 core

its CPU is Quad core ARM

It is 4 GB and 64 bit.

its ethernet is 10/100/1000BASE-T.

       Power Options

• Micro USB is (5V 2A)
• DC Power adapter of 5V and 4A.

       I/O

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

       Kit Contents

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

Comparison of Raspberry Pi and Jetson Nano

NVIDIA Jetson Nano Developer Kit:

1.Quard core A57 CPU at 1.43 GHz
2.4GB DDR4 RAM
3.128 CUDA core Maxwell
4.1xHDMI and 1xDP upto 4K ,60fps
5.Gigabit Ethernet (and E-keyed M.2)
6.4x USB 3.0
7.MIPI CSI
8.Micro USB and Barrel jack

Raspberry Pi:

1.Quard core A72 CPU at 1.5 GHz
2.4GB DDR4 RAM
3.Broadcom VideoCore VI
4.2 X micro HDMI upto 4K ,60fps
5.Gigabit Ethernet and wifi and BT 5.0
6.2 x USB 2.0 and USB 3.0
7.MIPI CSI and DSI
8.USB-C power

Similarities of Jetson Nano and Raspberry Pi :

Customization Options for Open Source SBCs

If you’re considering single board computers (SBCs) for commercial or industrial projects, customization is very much possible. Many manufacturers offer flexible solutions based on open source platforms like the BeagleBone series and others. These boards can be tailored for specific deployment scenarios—whether you need custom connectivity, specialized I/O, or more robust operation for long-term use in demanding environments.

Some open source SBCs provide industrial-grade reliability and can be adapted with features such as wireless capabilities, additional interface headers, or unique power options. This makes them an excellent choice when your application requires something beyond the standard out-of-the-box board.

Supported AI Software and Frameworks

When it comes to software, Jetson Nano really opens the doors for running a wide variety of AI frameworks and applications. It supports popular frameworks like TensorFlow, PyTorch, Caffe, MXNet, and Keras, which means you’re covered whether you’re tinkering with image classification, object detection, neural network segmentation, or dabbling in speech processing.

All of this is possible thanks to the JetPack SDK, which bundles everything needed—including the Linux OS, CUDA, cuDNN, and TensorRT—for deep learning, computer vision, and even multimedia tasks. The best part? Setting up is a breeze with an SD card image, so you’ll be up and running fast, no advanced wrestling with dependencies needed.

The same software stack is used across all Jetson modules, so projects and workflows can move easily between different Jetson devices. This ensures that training and deployment are consistent, streamlined, and scalable, which is especially handy if you decide to take your AI experiments to the next level.

Cooling Solutions for the NVIDIA Jetson Nano Developer Kit

If you’re running your Jetson Nano for long periods or handling intensive workloads, you’ll want to keep things cool to prevent unexpected shutdowns and maintain stability. There are a couple of solid options out there:

• Case with Adjustable Fan: Some cases come equipped with built-in PWM fans. These fans can ramp up or down depending on your Jetson Nano’s temperature, helping manage heat quietly and efficiently.
• Aluminum Enclosure: An all-aluminum case acts like a giant heatsink, drawing heat away from sensitive components. This type of enclosure keeps temperatures down, especially useful if you’re working in warmer environments or stacking multiple boards.

Both of these solutions are relatively affordable and easy to install, ensuring your Jetson Nano stays cool and keeps performing at its best.

Suggested AI Accessories and Upgrades

To get the most out of your NVIDIA Jetson Nano Developer Kit, consider adding a few popular AI-focused accessories and modules. These upgrades can enhance your projects in machine learning, computer vision, and edge computing:

• Machine Learning Expansion Boards: Look for boards like the Google Coral USB Accelerator or Intel Neural Compute Stick 2 for extra neural network performance.
• High-Resolution Camera Modules: Compatible options include the Raspberry Pi Camera Module v2 or Arducam IMX477 High Quality Camera for accurate image capture.
• Wireless Connectivity Add-ons: M.2 Wi-Fi and Bluetooth cards help with remote project control and cloud integration.
• External Microphones and Audio Boards: For advanced speech recognition and audio processing, consider USB microphones or audio HATs.
• Edge Computing Sensors: LIDAR units, depth cameras, and IMUs can elevate robotics, automation, and real-time AI applications.
• Compact Displays and Touchscreens: Small HDMI or MIPI DSI displays make standalone interfaces much easier to develop.
• AI-Optimized Storage Cards: High-speed, endurance-rated microSD cards ensure reliable data logging for intensive workloads.

By combining your Jetson Nano with these recommended components, you’ll create a flexible, robust platform for experimenting with state-of-the-art AI technologies.

Using Grove Sensors with the NVIDIA Jetson Nano Developer Kit

To incorporate Grove sensors with your NVIDIA Jetson Nano Developer Kit, you need a few simple tools and steps. Start by using a compatible HAT—such as the Base HAT for Raspberry Pi, which works seamlessly with the Jetson Nano’s GPIO header. This HAT provides the necessary connectors to plug in various Grove sensors directly.

For software support, you’ll want to install the grove.py Python library. This library makes it easy to interface with a growing collection of Grove modules—currently, over twenty types are supported for the Jetson Nano, with more on the way.

Getting started is straightforward:

• Attach the Grove HAT to the Nano’s 40-pin GPIO header.
• Connect the sensor modules you want to use.
• Install the grove.py Python package.
• Start interacting with the sensors right from your Python scripts.

With this setup, you can quickly expand the capabilities of your Jetson Nano, from reading environmental data to building interactive AI-powered projects, without complex wiring or additional hardware.

You can insert microSD for storage. We have 260 pin edge connector. M.2 (E-keyed) for a wireless card. Its Serial port header is UART. Buttons Headers are inc power and reset. We can use Rasp Pi V2 camera on MIPI CSI.
Add a Camera
The Raspberry Pi Camera Module V2 is fully compatible with the Jetson Nano and works seamlessly for AI projects. Simply connect the camera to the MIPI CSI interface, and you’re set for image capture, video streaming, or computer vision tasks. With its high-quality sensor, the Pi Camera V2 is a great choice for prototyping anything from object detection to time-lapse photography.

Kit Contents
• NVIDIA Jetson Nano module and its carrier board
• it has Quick Start and Support Guide
Beyond the hardware, the Jetson Nano is supported by the NVIDIA JetPack SDK, which provides a comprehensive board support package (BSP), a Linux OS environment, and industry-leading software libraries like CUDA®, cuDNN, and TensorRT™. These tools empower developers to dive into deep learning, computer vision, GPU computing, and multimedia processing right out of the box. Getting started is simple with an easy-to-flash SD card image, so you can focus on building your AI projects without hassle.
The JetPack SDK is consistent across the entire family of Jetson products, ensuring compatibility and a smooth workflow for both beginners and experienced developers. This unified platform streamlines the process of developing, training, and deploying AI applications, making the Jetson Nano a versatile choice for a wide variety of innovative projects.