AIO-3576JD4 Low-Power Large-Model Mainboard - AIOT Rockchip RK3576, 4K@120fps, Phoenix connector RS485 Dual RJ45 GbE
4K@120fps
260 PIN SODIMM SOM
The private deployment of large models
LLaMa2、ChatGLM、Qwen
Docker,Android14, Linux OS, and Buildroot+QT
AIO-3576JD4 powered by the Rockchip RK3576 SOM, an octa-core 64-bit AIOT processor, EC-R3576PC FD features an advanced lithography process to deliver high performance while maintaining low power consumption. It is equipped with an ARM Mali G52 MC3 GPU and a 6 TOPS NPU, supporting the private deployment of large-scale models under the Transformer architecture. With support for 4K@120fps decoding/4K@60fps encoding, the computer boasts a powerful display capability with 4K resolution at a high frame rate of 120 fps. Its industrial-grade metal enclosure and fanless design enable passive cooling. With an external watchdog, it provides industrial-grade stability, making it an excellent choice for AI applications requiring local deployment.
SOM: Only Core-3576JD4 SOM
Dev kit: Core-3576JD4 SOM + Carrier Board; 1x PSU
Large-Model Computer, AI Edge kit
| Basic Specifications | SOC |
Rockchip RK3576 |
| CPU |
Octa-core 64-bit processor (4×A72 + 4×A53), up to 2.2GHz |
|
| GPU |
G52 MC3 @ 1GHz, supporting OpenGL ES 1.1/2.0/3.2, OpenCL 2.0, Vulkan 1.1 Built-in high-performance 2D acceleration hardware |
|
| NPU |
6 TOPS NPU, supporting INT4/8/16/FP16/BF16/TF32 mixed operations |
|
| ISP |
The integrated 16-megapixel ISP supports low-light noise reduction, an RGB-IR sensor, and up to 120dB of HDR. The AI-ISP enhances image quality with reduced noise. |
|
| VPU |
Decoding: 4K@120fps: H.265/HEVC, VP9, AVS2, AV1; 4K@60fps: H.264/AVC Encoding: 4K@60fps: H.265/HEVC, and H.264/AVC |
|
| RAM |
LPDDR4/LPDDR4x (4GB/8GB optional) |
|
| Storage |
eMMC (16GB/32GB/64GB/128GB/256GB optional), UFS 2.0 (optional) |
|
| Storage Expansion |
1 * M.2 (SATA3.0/ PCIe NVMe SSD expansion, supporting 2242/2260/2280) |
|
| Power |
DC 12V (5.5mm * 2.1mm, 12V~24V wide input voltage) |
|
| OS |
Android14, Linux OS, and Buildroot |
|
| Software Support |
・ The private deployment of ultra-large-scale parameter models under the Transformer architecture, such as Gemma-2B, LlaMa2-7B, ChatGLM3-6B, and Qwen1.5-1.8B ・ Traditional network architectures such as CNN, RNN, and LSTM; a variety of deep learning frameworks including TensorFlow, PyTorch, MXNet, PaddlePaddle, ONNX and Darknet ・ Custom operator development ・ Docker container management technology |
|
| Size |
122.89mm * 85.04mm * 22.7mm |
|
| Weight |
≈120g |
|
| Environment |
Operating temperature: -20℃~60℃ Storage humidity: 10%~90%RH (non-condensing) |
|
| Interfaces | Network |
Ethernet: 2 * RJ45 (1000Mbps) WiFi: WiFi/BT module expansion available via M.2 E-KEY (2230) interface, supporting 2.4GHz/5GHz dual-band WiFi 6 (802.11a/b/g/n/ac/ax) and BT5.2 4G: 4G LTE expansion available via Mini PCIe (shared with 5G module) 5G: 5G expansion available via M.2 (shared with 4G module) |
| Video Input |
2 * MIPI CSI DPHY (1 * 4 lanes or 2 * 2 lanes) 1 * MIPI CSI D/C PHY (MIPI DPHY (1*4 lanes or 2 * 2 lanes) or MIPI CPHY (3 lanes) ) |
|
| Video Output |
1 * HDMI2.1(4K@120fps) |
|
| Audio Output |
1 * 3.5mm Audio jack, supporting MIC recording, CTIA standard |
|
| Watchdog |
External watchdog |
|
| USB |
2 * USB3.0、1 * USB2.0 |
|
| Other Interface |
1 * Type-C (USB 2.0/DEBUG), 1 * FAN (4Pin-1.25mm), 1 * SIM card 1 * dual-row pin header (2*10-20PIN-2.0mm): USB 2.0, SPI, 2*I2C, Line in, Line out, GPIO 1 * Phoenix connector (2*4Pin, 3.5mm pitch): 1 * RS485, 1 * RS232, 1 * CAN 2.0 |








