The Jetson Carrier functions as a high-performance, entry-level expansion board designed to bridge the gap between theoretical AI concepts and practical edge computing deployment. Built to host NVIDIA Jetson Nano™, Jetson Xavier™ NX, and Jetson TX2™ NX system-on-modules (SoMs), this carrier board unlocks AI performance ranging from 472 GFLOPS up to 21 TOPS, making it capable of handling tasks from basic image classification to complex Generative AI and Deep Learning models. Engineered for cost-effectiveness without compromising on connectivity, the platform integrates a 40-pin GPIO header compatible with the Raspberry Pi ecosystem, high-speed M.2 NVMe storage support, and multiple MIPI-CSI camera interfaces. Whether deployed in ROS robot development, real-time object tracking systems, or automated visual inspection setups, the Jetson carrier board provides a stable, resource-rich foundation for students, makers, and engineers to accelerate their AI development journey.

NOTE: This product is a Carrier Board only. The NVIDIA Jetson System-on-Module (SoM) is NOT included and must be purchased separately to function.

Board Overview of Jetson Carrier Board

Figure: Board Overview of Jetson Carrier Board

High-Performance Computing at an Accessible Entry Point

The Jetson carrier board lowers the barrier to entry for advanced artificial intelligence by offering broad compatibility with the NVIDIA Jetson family. By supporting modules like the Jetson Xavier NX, the hardware facilitates server-class AI compute performance at the edge. This flexibility allows the development platform to scale with user requirements, accommodating everything from lightweight data processing to heavy-duty neural network inference, all while maintaining a price point accessible to educational institutions and individual DIY enthusiasts.

Extensive Interface Expansion for Robotic Ecosystems

To support diverse hardware configurations, the expansion unit features a comprehensive array of peripheral connections. The inclusion of a 40-pin GPIO header ensures direct compatibility with a vast library of Raspberry Pi sensors, servos, and actuators, significantly simplifying the prototyping process. For data-intensive applications, the device includes four USB 3.0 ports, a Gigabit Ethernet controller, and HDMI 2.0 output, ensuring high-speed communication and display capabilities essential for complex ROS (Robot Operating System) projects.

Advanced Vision and Storage Capabilities

Visual perception is critical for modern AI, and this NVIDIA-compatible baseboard addresses this through 2x dual-channel and 1x quad-channel MIPI-CSI interfaces, supporting cameras up to 4K resolution. This multi-camera support is vital for depth sensing, stereo vision, and autonomous navigation. Furthermore, the system addresses the storage limitations of SD cards by integrating an M.2 Key-M slot, allowing for high-speed NVMe SSD installation. An additional M.2 Key-E slot is provided specifically for Wi-Fi and Bluetooth module expansion, ensuring robust wireless connectivity.

Industrial-Grade Power and Reliability

Stability is paramount in embedded systems. The development kit is equipped with a DC 12V-24V wide-voltage power jack and supports the PD 60W protocol, offering flexible powering options for mobile robots and stationary gateways. The compact PCB layout incorporates reverse-connection protection and ESD protection, safeguarding the sensitive SoM and peripherals against common electrical hazards during the experimental phase

Specifications:

Compatible Modules: Jetson Xavier™ NX, Jetson TX2™ NX, Jetson Nano™
Power:

PD 60W protocol support

DC 12V-24V power jack

Image Capture Interfaces:

2x dual-channel MIPI-CSI

1x quad-channel MIPI-CSI

USB: 4x USB 3.0
Network: Gigabit Ethernet port
Video Output: HDMI 2.0
Storage Expansion:

M.2 Key-M (supports NVMe SSD)

MicroSD card slot

Wireless Expansion: M.2 Key-E (for Wi-Fi/Bluetooth module)
Debugging/Flashing: DEBUG and system flashing interface
GPIO: 40-pin GPIO header
Dimensions: 75mm x 120mm

Applications:

ROS Robotics: Building autonomous rovers, robotic arms, and drones with SLAM capabilities.
Edge AI Vision: Real-time object detection, face recognition, and automated license plate recognition (ALPR).
Generative AI: Running localized Large Language Models (LLMs) and image generation tools.
Smart IoT Gateways: Aggregating sensor data and performing local anomaly detection before cloud transmission.
STEM Education: Teaching advanced AI concepts using tangible, industrial-standard hardware.

Documentations: