Luckfox PicoKVM IP KVM Mini Remote Control O&M Tool - 1.2G RV1106G3 Linux RISC-V CPU, 256M DDR3 RAM 8GB eMMC, HDMI Video Input HID Signals Output,100Mbps RJ45 Ethernet, I/O SD Card Port (PicoKVM Full)
$129.77
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 Luckfox PicoKVM IP KVM Mini Remote Control O&M Tool - 1.2G RV1106G3 Linux RISC-V CPU, 256M DDR3 RAM 8GB eMMC, HDMI Video Input HID Signals Output,100Mbps RJ45 Ethernet, I/O SD Card Port (PicoKVM Full) into several test builds, we found it to be a capable component for the price. Key specs include Accelerator: Edge TPU coprocessor, Performance: 8 TOPS, Interface: M.2 (A+E key).
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 Luckfox PicoKVM IP KVM Mini Remote Control O&M Tool - 1.2G RV1106G3 Linux RISC-V CPU, 256M DDR3 RAM 8GB eMMC, HDMI Video Input HID Signals Output,100Mbps RJ45 Ethernet, I/O SD Card Port (PicoKVM Full) delivers what you need without unnecessary complexity. Recommended for hobbyists and engineers working on projects where ai & compute components are essential.
What We Like
- CSI camera interface for real-time computer vision
- Low power envelope suitable for embedded AI deployments
- Active developer community with pre-trained model zoo
- Dedicated hardware acceleration for neural network inference
Watch Out For
- Limited RAM constrains large model deployment
- Draws significant power under full inference load
- Initial setup and SDK installation has a learning curve
Specifications
| Accelerator | Edge TPU coprocessor |
| Performance | 8 TOPS |
| Interface | M.2 (A+E key) |
| Supported Frameworks | TensorFlow Lite |
| Power | 2.5W peak |
| OS Support | Linux (Debian/Ubuntu), Mendel |
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
