EC-R3576PC FD Embedded Large-Model Computer - 6 TOPS, 4K@120FPS, Rockchip RK3576, UFS2.0 supports LLMs and traditional CNN neural networks
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Home > Development Board > ARM Boards & SoMs > EC-R3576PC FD Embedded Large-Model Computer - 6 TOPS, 4K@120FPS, Rockchip RK3576, UFS2.0 supports LLMs and traditional CNN neural networks
  • EC-R3576PC FD Embedded Large-Model Computer - 6 TOPS, 4K@120FPS, Rockchip RK3576, UFS2.0 supports LLMs and traditional CNN neural networks
  • EC-R3576PC FD Embedded Large-Model Computer - 6 TOPS, 4K@120FPS, Rockchip RK3576, UFS2.0 supports LLMs and traditional CNN neural networks
  • EC-R3576PC FD Embedded Large-Model Computer - 6 TOPS, 4K@120FPS, Rockchip RK3576, UFS2.0 supports LLMs and traditional CNN neural networks
  • EC-R3576PC FD Embedded Large-Model Computer - 6 TOPS, 4K@120FPS, Rockchip RK3576, UFS2.0 supports LLMs and traditional CNN neural networks
  • EC-R3576PC FD Embedded Large-Model Computer - 6 TOPS, 4K@120FPS, Rockchip RK3576, UFS2.0 supports LLMs and traditional CNN neural networks
  • EC-R3576PC FD Embedded Large-Model Computer - 6 TOPS, 4K@120FPS, Rockchip RK3576, UFS2.0 supports LLMs and traditional CNN neural networks
1/1
  • EC-R3576PC FD Embedded Large-Model Computer - 6 TOPS, 4K@120FPS, Rockchip RK3576, UFS2.0 supports LLMs and traditional CNN neural networks
  • EC-R3576PC FD Embedded Large-Model Computer - 6 TOPS, 4K@120FPS, Rockchip RK3576, UFS2.0 supports LLMs and traditional CNN neural networks
  • EC-R3576PC FD Embedded Large-Model Computer - 6 TOPS, 4K@120FPS, Rockchip RK3576, UFS2.0 supports LLMs and traditional CNN neural networks
  • EC-R3576PC FD Embedded Large-Model Computer - 6 TOPS, 4K@120FPS, Rockchip RK3576, UFS2.0 supports LLMs and traditional CNN neural networks
  • EC-R3576PC FD Embedded Large-Model Computer - 6 TOPS, 4K@120FPS, Rockchip RK3576, UFS2.0 supports LLMs and traditional CNN neural networks
  • EC-R3576PC FD Embedded Large-Model Computer - 6 TOPS, 4K@120FPS, Rockchip RK3576, UFS2.0 supports LLMs and traditional CNN neural networks

EC-R3576PC FD Embedded Large-Model Computer - 6 TOPS, 4K@120FPS, Rockchip RK3576, UFS2.0 supports LLMs and traditional CNN neural networks

SKU: TQTC357603
EC-R3576PC FD Embedded Large-Model Computer - 6 TOPS, 4K@120FPS, Rockchip RK3576, UFS2.0 
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  • EC-R3576PC FD Embedded Large-Model Computer - 6 TOPS, 4K@120FPS, Rockchip RK3576, UFS2.0 supports LLMs and traditional CNN neural networks
  • EC-R3576PC FD Embedded Large-Model Computer - 6 TOPS, 4K@120FPS, Rockchip RK3576, UFS2.0 supports LLMs and traditional CNN neural networks
  • EC-R3576PC FD Embedded Large-Model Computer - 6 TOPS, 4K@120FPS, Rockchip RK3576, UFS2.0 supports LLMs and traditional CNN neural networks
  • EC-R3576PC FD Embedded Large-Model Computer - 6 TOPS, 4K@120FPS, Rockchip RK3576, UFS2.0 supports LLMs and traditional CNN neural networks
  • EC-R3576PC FD Embedded Large-Model Computer - 6 TOPS, 4K@120FPS, Rockchip RK3576, UFS2.0 supports LLMs and traditional CNN neural networks
  • EC-R3576PC FD Embedded Large-Model Computer - 6 TOPS, 4K@120FPS, Rockchip RK3576, UFS2.0 supports LLMs and traditional CNN neural networks
  • EC-R3576PC FD Embedded Large-Model Computer - 6 TOPS, 4K@120FPS, Rockchip RK3576, UFS2.0 supports LLMs and traditional CNN neural networks
  • EC-R3576PC FD Embedded Large-Model Computer - 6 TOPS, 4K@120FPS, Rockchip RK3576, UFS2.0 supports LLMs and traditional CNN neural networks
  • EC-R3576PC FD Embedded Large-Model Computer - 6 TOPS, 4K@120FPS, Rockchip RK3576, UFS2.0 supports LLMs and traditional CNN neural networks
  • EC-R3576PC FD Embedded Large-Model Computer - 6 TOPS, 4K@120FPS, Rockchip RK3576, UFS2.0 supports LLMs and traditional CNN neural networks
  • EC-R3576PC FD Embedded Large-Model Computer - 6 TOPS, 4K@120FPS, Rockchip RK3576, UFS2.0 supports LLMs and traditional CNN neural networks
  • EC-R3576PC FD Embedded Large-Model Computer - 6 TOPS, 4K@120FPS, Rockchip RK3576, UFS2.0 supports LLMs and traditional CNN neural networks
1/1
  • EC-R3576PC FD Embedded Large-Model Computer - 6 TOPS, 4K@120FPS, Rockchip RK3576, UFS2.0 supports LLMs and traditional CNN neural networks
  • EC-R3576PC FD Embedded Large-Model Computer - 6 TOPS, 4K@120FPS, Rockchip RK3576, UFS2.0 supports LLMs and traditional CNN neural networks
  • EC-R3576PC FD Embedded Large-Model Computer - 6 TOPS, 4K@120FPS, Rockchip RK3576, UFS2.0 supports LLMs and traditional CNN neural networks
  • EC-R3576PC FD Embedded Large-Model Computer - 6 TOPS, 4K@120FPS, Rockchip RK3576, UFS2.0 supports LLMs and traditional CNN neural networks
  • EC-R3576PC FD Embedded Large-Model Computer - 6 TOPS, 4K@120FPS, Rockchip RK3576, UFS2.0 supports LLMs and traditional CNN neural networks
  • EC-R3576PC FD Embedded Large-Model Computer - 6 TOPS, 4K@120FPS, Rockchip RK3576, UFS2.0 supports LLMs and traditional CNN neural networks
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EC-R3576PC FD Embedded Large-Model Computer - 6 TOPS, 4K@120FPS, Rockchip RK3576, UFS2.0 


Introduction

EC-R3576PC FD powered by the Rockchip RK3576, 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.


Features

  1. 6 TOPS, 4K@120FPS, Rockchip RK3576,  UFS2.0
  2. supports LLMs and traditional CNN neural networks 


Shipping List

1x EC-R3576PC FD 
1x PSU


Applications

Large-Model Computer, AI Edge kit


Specification

Specifications
Basic Specifications SOC

Rockchip RK3576

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
VPU

Decoding: 8K@30fps/4K@120fps: H.265/HEVC, VP9, AVS2, AV1, 4K@60fps: H.264/AVC

Encoding: 4K@60fps: H.265/HEVC, H.264/AVC
RAM

LPDDR4/LPDDR4x (4GB/8GB optional)
Storage

eMMC (16GB/32GB/64GB/128GB/256GB optional), UFS2.0 (optional)
Storage
Expansion

1 * M.2 (2242 PCIe NVMe/SATA SSD expansion ) (internal device), 1 * TF card slot
Power

DC 12V (5.5mm * 2.1mm, 12V~24V wide input voltage)
OS

Android14、Linux OS、Buildroot+QT
Software
Support

・ The private deployment of ultra-large-scale parameter models under the Transformer architecture,

such as Gemma-2B, LlaMa2-7B, ChatGLM3-6B, Qwen1.5-1.8B, and more

・ Traditional network architectures such as CNN, RNN, and LSTM; a variety of deep learning

frameworks include TensorFlow, PyTorch, MXNet, PaddlePaddle, ONNX, and Darknet

・ Custom operator development

・ Docker container management technology
Power
Consumption

Normal: 1.2W(12V/100mA)，Max: 6W(12V/500mA)，Min: 0.096W(12V/8mA)
Size

116mm * 105.2mm * 31.5mm
Weight

≈0.43kg
Environment

Operating temperature: -20℃- 60℃

Storage humidity: 10%～90%RH (non-condensing)
Interfaces Network

1 * Gigabit Ethernet (1000 Mbps / RJ45),

2.4GHz/5GHz dual-band WiFi (802.11a/b/g/n/ac),

Bluetooth 5.0
Video Input

1 * MIPI CSI DPHY（30Pin-0.5mm, 1*4 lanes/2*2 lanes）
Video Output

1 * HDMI2.1(4K@120fps)、1 * DP1.4 (4K@120fps)
Watchdog

External watchdog
USB

1 * USB3.0、1 * USB2.0
Expansion
Interface

1 * Type-C (OTG/DP1.4), 1 * 3.5mm Audio jack (supporting MIC recording, CTIA standard)


Detailed Descriptions

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