ROC-RK3576-PC AIOT Single Board Computer | Firefly SBC

ROC-RK3576-PC AIOT ARM Single Board Computer

$259.00

$259.00 $139.00

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ROC-RK3576-PC Motherboard

Equipped with Rockchip octa-core AI processor RK3576, 6 TOPS NPU for computing power, supports private deployment of modern mainstream large models, and allows for massive storage expansion via M.2 PCIe/SATA SSDs. It boasts 4K high-frame-rate video decoding capabilities and features a compact credit-card-sized design.

AI Processor RK3576

Features the octa-core 64-bit high-performance AIoT processor RK3576, utilizing a big.LITTLE architecture with a main frequency of up to 2.2GHz. Equipped with the Mali-G52 MC3 GPU, its 145 GFLOPS performance enables efficient heterogeneous computing, providing robust support for high-performance computing and multitasking.

Private Deployment

Supports private deployment of ultra-large-scale parameter models based on the Transformer architecture, such as the Gemma series, ChatGLM series, Qwen series, Phi series, and other large language models.

4K Video Decoding

Supports 8K@30fps / 4K@120fps decoding (VP9, AVS2, AV1) and 4K@60fps decoding (H.264/AVC), as well as 4K@60fps encoding (H.264/AVC). Features HDMI 2.1 (4K@120fps), eDP 1.3 (4K@60fps), DP 1.4 (4K@120fps), and MIPI DSI (2560×1600@60fps) interfaces, supporting triple independent display.

LPDDR5 Memory

LPDDR5 offers larger memory capacity, higher bandwidth, faster data transfer rates, lower power consumption, and more advanced Error Correction Code (ECC) technology. Meets the memory space and response speed requirements for private deployment of large models.

Image Processing

Built-in 16-megapixel ISP with support for low-light noise reduction, RGB-IR sensors, and up to 120dB HDR. AI-ISP enhances image quality in low-noise conditions. Supports MIPI-CSI input.

Credit Card Size

Overall dimensions of 93.00mm × 60.15mm, similar to a credit card in size. The compact structure is built with high-specification electronic components and multiple circuit optimization designs to enhance overall system stability.

Multiple Operating Systems

Supports an RTLinux kernel with excellent real-time performance, making it widely suitable for industrial applications. It is compatible with Android 14, Linux OS, and Buildroot, providing a secure and stable system environment for product development and manufacturing.

Development Materials

Includes complete source code, tutorials, technical documentation, and development tools, enabling users to efficiently perform secondary development and quickly create independently controllable products.

6 TOPS NPU Computing Power

Built-in NPU with computing power of up to 6 TOPS, supporting INT4/INT8/INT16/FP16/BF16/TF32 mixed operations. Capable of intelligent data processing, voice recognition, and image analysis, meeting the edge computing AI application needs of most terminal devices.

Application Scenarios

Widely applicable to edge computing, private deployment of large models, smart commercial display, cloud terminal products, industrial control host, automotive electronics and other industries.

Edge Computing

Private Deployment of Large Models

Smart Commercial Display

Cloud Terminal Products

Industrial Control Host

Automotive Electronics

Interfaces

Specifications

ROC-RK3576-PC
Basic Specifications	SOC	Rockchip RK3576
	CPU	Octa-core 64-bit processor (4×A72 + 4×A53), frequency is 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
	Decoding/ Encoding	Decoding: 8K@30fps/4K@120fps: VP9, AVS2, AV1, 4K@60fps: H.264/AVC Encoding: 4K@60fps: H.264/AVC
	RAM	LPDDR4/LPDDR5 (4GB/8GB/16GB optional)
	Storage	eMMC (16GB/32GB/64GB/128GB/256GB optional), UFS3.1 (optional)
	Storage expansion	1 × M.2 (Scalable 2242 PCIe NVMe/SATA SSD), 1 × TF Card
	OS	Android 14, Linux OS, and 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	DC 12V (5.5mm × 2.1mm, 12V~24V wide input voltage)
	Power consumption	Normal: 1.2W(12V/100mA)，Max: 6W(12V/500mA)，Min: 0.096W(12V/8mA)
	Dimension	93.00mm × 60.15mm × 12.49mm
	Weight	≈50g
	Environment	Operating Temperature: -20℃ ~ 60℃, Storage Temperature: -20℃ ~ 70℃, Storage Humidity: 10%～90%RH (non-condensing)
Interface Specifications	Internet	1 × Gigabit Ethernet (1000Mbps/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, 14 lanes/22 lanes)
	Video output	1 × HDMI2.1 (4K@120Hz), 1 × DP1.4 (4K@120Hz), 1 × MIPI-DSI DPHY (2560×1600@60Hz，1×4lanes，30Pin-0.5mm)
	Audio output	1 × 3.5mm Audio jack (Support MIC recording，American Standard CTIA), 1 × MIC (2Pin-1.25mm)
	USB	1 × USB3.0, 1 × USB2.0, 1 × Type-C (OTG/DP1.4)
	Watchdogs	External watchdogs
	Extension interface	1 × FAN (4Pin-1.25mm), 1 × Debug(3Pin-2mm), 1 × Double-row pin headers (20Pin-2.0mm, Lead-out: USB2.0, I2C, SPI, SARADC, UART, LineOut)