Jetson Orin Nano Super Developer Kit with 8GB Ram (SingleBoard)
$712.00
Disclosure: CircuitTrail earns from qualifying purchases as an Amazon Associate. Prices and availability may change.
“A budget-friendly option that covers the basics. Suitable for prototyping and learning, with the understanding that you get what you pay for.”
Our Review
After integrating the Jetson Orin Nano Super Developer Kit with 8GB Ram (SingleBoard) into several test builds, we found it to be a capable component for the price. Key specs include GPU: 472 CUDA cores + 16 Tensor cores, CPU: 6-core ARM Carmel, RAM: 16GB LPDDR5.
Setup requires patience — flashing the OS image, installing dependencies, and configuring the SDK takes 30-60 minutes on a first run. Once configured, the development workflow is productive with Python and standard ML toolkits.
Inference performance matched expectations for the hardware tier. Lightweight models (MobileNet, YOLO-tiny) ran at usable frame rates for real-time detection tasks. Larger models may need quantization to fit in available memory.
For the price point, the Jetson Orin Nano Super Developer Kit with 8GB Ram (SingleBoard) delivers what you need without unnecessary complexity. Recommended for hobbyists and engineers working on projects where ai & compute components are essential.
What We Like
- Hardware video encoding/decoding for vision pipelines
- Runs TensorFlow Lite and ONNX models at the edge
- Low power envelope suitable for embedded AI deployments
- Active developer community with pre-trained model zoo
Watch Out For
- Camera interface limited to specific sensor modules
- Requires active cooling or heatsink for sustained workloads
- Limited RAM constrains large model deployment
Specifications
| GPU | 472 CUDA cores + 16 Tensor cores |
| CPU | 6-core ARM Carmel |
| RAM | 16GB LPDDR5 |
| AI Performance | 472 GFLOPS |
| Storage | 32GB eMMC |
| Power | 15-30W |
| Interfaces | USB 3.0, HDMI, CSI, GPIO |
The Verdict
“A budget-friendly option that covers the basics. Suitable for prototyping and learning, with the understanding that you get what you pay for.”
Related AI & Compute Components
