A Practical Guide to Choosing a Carrier Board for Edge AI Applications
If you’re working on facial recognition, visual defect detection, or autonomous robotics with NVIDIA Jetson modules, one question always comes up early:
Which carrier board should I use for running deep learning models in real-world scenarios?
Jetson Xavier NX and Orin NX are powerful edge AI modules—but to make them production-ready, you need a carrier board that can handle real-world deployment: from powering the module to interfacing with sensors, cameras, and motor drivers.
This guide shows how JetCore - a rugged, high-performance Jetson carrier board—can help you deploy AI models with confidence.
Why the Carrier Board Matters for Jetson Projects
When you’re running neural networks on the edge using Jetson, you’re usually doing more than just inference:
- You’re reading camera feeds or LIDAR input.
- You’re sending commands to actuators or motors.
- You’re receiving feedback from sensors.
- You’re processing everything with minimal latency.
A weak or incompatible carrier board will cause:
- Random shutdowns (due to poor power delivery)
- I/O limitations (no CAN/UART/PWM ports)
- Overheating in enclosed spaces
- Debugging nightmares during integration
Meet JetCore: A Developer-Friendly Carrier Board for Jetson AI Projects
JetCore is a compact, rugged carrier board designed by VECROS for Jetson Xavier NX, Orin NX, and Orin Nano. It’s engineered for AI deployment at the edge—where uptime, reliability, and connectivity matter most.
Key Features Developers Love:
- Supports Xavier NX, Orin NX, Orin Nano
- Stable Power Delivery – Ideal for continuous inference workloads
- Rich I/O – USB 3.0, Ethernet, CAN, UART, PWM, GPIO
- Thermal Friendly Design – Built for industrial, robotics, and embedded environments
- Compact Form Factor – Easy to mount in robotics or drone enclosures
How to Deploy AI Models with Jetson + JetCore
Here’s how a typical Jetson AI workflow looks when using JetCore:
1. Train Your AI Model
Use PyTorch, TensorFlow, or YOLOv8 on your PC or cloud (Google Colab, AWS).
2. Optimize for Jetson Deployment
Convert the model to TensorRT for faster inference using torch2trt, ONNX, or NVIDIA tools.
3. Deploy to Jetson with JetCore
Mount the module on JetCore, flash JetPack, and run the model using CUDA + TensorRT.
4. Connect Real-World Hardware
Use JetCore’s GPIO, CAN, UART, PWM, and USB ports to connect cameras, motors, and sensors.
Real-World AI Applications with JetCore
Real-Time Facial Recognition
Jetson processes frames from a USB or CSI camera. JetCore handles the power, camera interface, and connectivity to alert systems—all without the cloud.
Visual Defect Detection in Factories
Mount JetCore in an industrial box. Connect a camera over USB 3.0. Jetson runs CNN models in milliseconds. JetCore provides the power, stability, and sensor inputs needed for factory automation.
Robotics + Autonomous Navigation
JetCore’s PWM controls motor ESCs. CAN/UART handle IMUs or GPS. Jetson runs object detection or SLAM. JetCore ties everything together, from power to communication.
Why JetCore Beats Generic Carrier Boards?
| Feature | Generic Boards | JetCore |
|---|---|---|
| Power Filtering |  Unstable in field use |
 Clean power delivery |
| CAN, UART, PWM | Limited or missing |
Built-in ports |
| Compact Design | Bulky or dev-board style |
Deployment-ready |
| Industrial Use | Not ruggedized |
Shock-tolerant & thermally efficient |
To Build Smarter, Deploy Faster!
Jetson modules are incredibly powerful—but you’ll unlock their true potential only when paired with the right hardware.
Whether you’re working on:
- AI-powered smart cameras
- Autonomous robots
- Drones with onboard inference
- Factory defect detection systems
JetCore gives you the I/O, power, and reliability you need to go from prototype to product.
Ready to build edge AI systems that work in the real world?
Explore JetCore and get started on your next Jetson project.
Discover JetCore