Waveshare Jetson Nano Development Board (Alternative B01 Kit)
Waveshare Jetson Nano Development Board (Alternative B01 Kit) - Image 2
Waveshare Jetson Nano Development Board (Alternative B01 Kit) - Image 3
SKU: IOWC-0948
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Waveshare Jetson Nano Development Board (Alternative B01 Kit)

₹6,089.00 (Inclusive of all taxes)
Out of Stock

The Waveshare Jetson Nano Development Board is an advanced AI computing platform powered by a 128-core NVIDIA Maxwell GPU and quad-core ARM Cortex-A57 CPU, designed for embedded machine learning, computer vision, and edge AI applications. It supports booting from a microSD card and offers extensive connectivity via Gigabit Ethernet, USB 3.0, and dual MIPI CSI-2 camera interfaces.

Features

  • NVIDIA Jetson Nano AI Platform: Features a 128-core Maxwell GPU and quad-core ARM Cortex-A57 CPU for efficient AI inferencing and multimedia processing in embedded systems.
  • 4GB LPDDR4 Memory: Provides 25.6 GB/s bandwidth to handle intensive AI workloads like object detection, image recognition, and real-time video analytics.
  • Dual MIPI CSI-2 Camera Support: Enables stereo vision and advanced computer vision projects with two dedicated camera connectors for AI imaging.
  • Extensive I/O & Connectivity: Includes Gigabit Ethernet, 4x USB 3.0 ports, HDMI, DisplayPort, and a 40-pin GPIO header for sensor and peripheral expansion.
  • Compact & Compatible Design: Form-factor compatible with official Jetson Nano Developer Kit (B01) accessories and enclosures for easy integration into existing setups.

Out of stock

Description

The Waveshare Jetson Nano Development Board serves as a robust alternative to the official NVIDIA B01 kit, engineered specifically for edge AI and embedded machine learning applications. Its core architecture combines a 128-core NVIDIA Maxwell GPU with a quad-core ARM Cortex-A57 CPU, delivering the computational power necessary for real-time inferencing, deep learning models, and complex multimedia processing tasks within a compact, low-power footprint.

Designed for system integrators and AI developers, the board provides a comprehensive suite of high-speed interfaces and expansion capabilities. It supports dual MIPI CSI-2 camera inputs for computer vision pipelines, Gigabit Ethernet for network connectivity, and an M.2 Key E slot for adding wireless modules. The inclusion of multiple USB 3.0 ports, display outputs, and a detailed GPIO header allows for seamless hardware prototyping and integration into robotics, industrial automation, or smart surveillance systems.

Key Features

  • High-performance AI computing platform with NVIDIA 128-core Maxwell GPU architecture.
  • Quad-core ARM Cortex-A57 CPU @ 1.43 GHz paired with 4GB LPDDR4 RAM for efficient multitasking.
  • Dual MIPI CSI-2 D-PHY lanes support stereo camera setups for advanced computer vision and AI imaging.
  • Comprehensive connectivity includes Gigabit Ethernet (with PoE support via external module), 4x USB 3.0, HDMI, and DisplayPort.
  • Extensive expansion via 40-pin GPIO header supporting I2C, I2S, SPI, UART, and programmable interfaces.
  • M.2 Key E expansion connector for adding Wi-Fi, Bluetooth, or other PCIe-based modules.
  • MicroSD (TF) card slot for primary boot storage and operating system deployment (card not included).
  • Compact 100x80x25 mm form factor compatible with official Jetson Nano Developer Kit enclosures.

Applications

  • Autonomous Robotics & Drones: For real-time navigation, object avoidance, and AI-driven decision-making.
  • Smart Vision & Surveillance Systems: Deploying edge-based person detection, facial recognition, and activity monitoring.
  • Industrial Automation & IIoT: Machine vision for quality inspection, predictive maintenance, and process control.
  • Edge AI Gateways: Aggregating and processing sensor data from multiple sources before cloud transmission.
  • Embedded AI Prototyping: Developing and testing deep learning models for academic research or product development.

Technical Specifications

SoC / ProcessorNVIDIA Jetson Nano (128-core Maxwell GPU, Quad-core ARM Cortex-A57 CPU)
CPU Clock Speed1.43 GHz
System Memory4 GB 64-bit LPDDR4
Memory Bandwidth25.6 GB/s
Storage InterfacemicroSD (TF) Card Slot (Card Not Included)
Video Encoding1x 4K @ 30fps HEVC / 2x 1080p @ 60fps HEVC
Video Decoding1x 4K @ 60fps HEVC / 2x 4K @ 30fps HEVC
Camera Interface2x MIPI CSI-2 Connectors
Network ConnectivityGigabit Ethernet Port (10/100/1000Base-T)
Expansion ConnectorM.2 Key E Slot, 260-pin SO-DIMM Module Connector
USB Ports4x USB 3.0, 1x Micro-USB (Power/Data)
Display OutputHDMI, DisplayPort
Extension Interfaces40-pin GPIO (I2C, I2S, SPI, UART), 12-pin Multi-function Header
Power Input5V via DC Jack or Micro-USB Port
Fan Headers1.25mm and 2.54mm Headers
Dimensions100 mm x 80 mm x 25 mm
Weight76 g

Frequently Asked Questions

Is this Waveshare board fully compatible with the official NVIDIA Jetson Nano Developer Kit (B01)?

Yes, this Waveshare development board is designed as a direct alternative and is form-factor compatible with the official NVIDIA Jetson Nano B01 kit. It uses the same 260-pin SO-DIMM connector and matches the physical dimensions, allowing it to fit into official Jetson Nano enclosures and use compatible heatsinks, fans, and expansion accessories. The pinout and core specifications align with the reference design for seamless software and hardware integration.

How do I power the Jetson Nano development board and what are the power requirements?

The board accepts a 5V DC power supply through either the barrel jack (DC jack) or the Micro-USB port. A stable 5V/4A (20W) power adapter is recommended for reliable operation under full CPU/GPU load, especially when using power-hungry USB peripherals. The board includes headers for connecting cooling fans. Ensure your power source meets the current requirement to avoid instability or throttling during intensive AI inference tasks.

What operating systems and AI frameworks are supported on this Jetson Nano board?

The board supports NVIDIA's official JetPack SDK, which includes Ubuntu Linux, CUDA, cuDNN, TensorRT, and popular AI frameworks like TensorFlow, PyTorch, and OpenCV. You flash the JetPack OS image to a microSD card, insert it into the board's TF slot, and boot. The environment is optimized for deploying deep learning models for computer vision, natural language processing, and robotics applications directly on the edge device.

Can I connect two cameras simultaneously for stereo vision projects?

Yes, the board features two independent MIPI CSI-2 camera connectors (2-lane D-PHY each), allowing you to connect two compatible Raspberry Pi or NVIDIA-certified camera modules simultaneously. This is ideal for stereo vision, depth perception, and multi-camera AI applications. You can configure the cameras using the NVIDIA V4L2 drivers and process the streams with frameworks like OpenCV or deep learning models within the JetPack environment.

What is the purpose of the M.2 Key E slot on the development board?

The M.2 Key E (PCIe/USB interface) expansion slot allows you to add wireless communication modules, such as Wi-Fi 6 (AX200) or Bluetooth adapters, to the Jetson Nano platform. This is useful for creating wireless AI gateways or robots that require high-speed network connectivity without using a USB port. You need to install the appropriate Linux drivers for your M.2 module after physically installing it into the slot.

Package Includes

  • 1x Waveshare Jetson Nano Development Board
  • 1x Screw Set

Additional Information

Weight 0.113 kg
Dimensions 16 × 11 × 4 cm

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