EC-R3576PC FD
Embedded Large-Model Computer
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.

Octa-core 64-bit AIOT processor RK3576

RK3576, the new octa-core 64-bit high-performance AIOT processor, features a big.LITTLE architecture (4×A72 +4×A53), an advanced
lithography process, and a frequency of up to 2.2 GHz. It ensures technical support for high-performance computing and multitasking.
With a Mali-G52 MC3 GPU delivering 145G FLOPS, this GPU is capable of supporting efficient heterogeneous computing to
meet the demands of graphics-intensive applications.

4K@120 fps high frame rate video decoding

This device supports 8K@30fps/4K@120fps decoding (H.265/HEVC, VP9, AVS2, and AV1), 4K@60fps decoding (H.264/AVC),
as well as 4K@60fps encoding (H.265/HEVC and H.264/AVC).
It supports HDMI 2.1 (4K@120fps) / eDP 1.3 (4K@60fps), and DP 1.4 (4K@120fps).

16MP ISP with enhanced image processing capabilities

The integrated 16-megapixel ISP supports low-light noise reduction, an RGB-IR sensor, and up to 120dB HDR.
It features AI-ISP technology to enhance image quality with reduced noise.

UFS 2.0 with a data read speed of 12Gbps

With support for UFS 2.0 / HS-G3 (12Gbps), this device ensures efficient data storage and access. With the PCIe NVMe / SATA SSD
expansion, it delivers rapid data read and write speeds, enabling the device to be easily expanded to TB storage capacity.

Industrial-grade metal enclosure
with efficient passive cooling

A wide range of expansion interface options includes HDMI 2.1, Gigabit Ethernet, USB 3.0, USB 2.0, and Type-C (OTG / DP1.4).
The device features an industrial-grade, all-metal enclosure with an aluminum alloy structure for efficient heat dissipation.
Its fanless design contributes to silent operation, ensuring 24/7 uninterrupted and stable performance.

Support various operating systems

Support Android 14, Linux OS, and Buildroot+QT. These provide a safe and stable
system environment for product research and production.

Abundant resources

We offer SDKs, tutorials, technical documentation, and development tools to streamline and improve the development process.

A wide range of applications

It is widely used in edge computing, local deployment of large models, intelligent digital signage,
cloud terminal products, industrial PCs, automotive electronics, and more.

Local deployment
of large models
Edge computing
Digital signage
Industrial PCs
Cloud terminal
products
Automotive
electronics

Specifications

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)

Dimension

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)