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Seeed Studio

reComputer RK3576-20 Open Rockchip AI Box for AI Development

SKU: SBC1171

₹ 24,949.99

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reComputer RK3576-20 Open Rockchip AI Box for AI Development₹ 24,949.99
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reComputer RK3576-20 Open Rockchip AI Box for AI Development

This product is more than an open-source 8-core Linux AI box. Built on the powerful Rockchip RK3576 4GB RAM, it is designed to make edge AI development and deployment faster and easier.

To improve the developer experience, Seeed offers reComputer AI Lab — a platform that helps you deploy AI applications with one-click tools and ready-to-use resources. It includes optimized edge AI model demos for CV, LLM, VLM, STT, and TTS, interactive tutorials, deployment tools, containerized applications, and community projects to speed up development on Rockchip.

With rich I/O, multiple OS support, wireless expansion options, and active cooling, this compact AI box is ready for a wide range of edge AI workloads. It is ideal for smart vision systems, voice-enabled devices, AI agents, robotics, and industrial edge applications.

 

Key Features

Feature 1: Based on Powerful RK3576 Processor

The reComputer RK3588 features an advanced CPU architecture with

4 × Cortex-A72 @2.2GHz | 4 × Cortex-A53 @2.0GHz
LPDDR5 (4GB/8GB/16GB) | ARM Mali GPU-G52 MC3

 

Feature 2: 6 TOPS NPU Embedded

With a built-in 6 TOPS NPU, it delivers efficient AI inference directly on the device, reducing latency and enhancing privacy without relying on the cloud.

AI performance highlights include:

Vision CNN: YOLO11@77.9FPS(640*640)
LLM: capable for deepseek-r1-distill-qwen 7b support
VLM: capable for qwen2.5-vl 3b
STT: whisper_base_20s (RTF 0.218) with real-time processing support
TTS: mms_tts_eng_200 (RTF 0.069) with real-time processing support

Check the details of the benchmark.

 

Feature 3: Compute Power, Storage, and Wireless Connectivity Expandable

It offers flexible expansion through

a) 1 × M.2 M Key: PCIe 2.1x1, supports SSD expansion and AI accelerators such as Hailo and Rockchip, up to 26TOPS

b) 1 x miniPCIe for wireless expansion, like 4G LTE, LoRaWAN, Wi-Fi HaLow

 

Feature 4: Broad Model and Framework Support

It also supports a wide range of popular AI models, From computer vision and speech to LLM-powered edge intelligence, including: YOLO, MobileNet V2, RetinaFace, CLIP, Whisper, DeepSeek-R1, Qwen2-VL,...................

Framework support includes: ONNX, PyTorch, TensorFlow, TensorFlow Lite, Caffe, Darknet,...................

Toolchain support: RKNN-Toolkit2

This broad compatibility makes it easy to migrate existing models and build integrated edge AI applications across Perception AI, Generative AI, Agent and Physical AI.

 

Feature 5: Get Started in Seconds with reComputer AI Lab

Deploying AI at the edge is often complicated — from environment setup and model conversion to performance optimization. To simplify this process, Seeed created reComputer AI Lab, a developer platform designed to lower the barrier to edge AI development.

It provides a complete collection of optimized AI model demos for CV, LLM, and VLM, along with interactive tutorials, deployment tools, containerized AI applications, project examples, and community resources. It is built to accelerate AI development and deployment on Rockchip, Raspberry Pi, and NVIDIA Jetson platforms.

For Rockchip users, reComputer AI Lab includes:

Ready-to-Use Models

Access pre-optimized models without starting from scratch. AI Lab includes models such as YOLO11, MobileNet, CLIP, and Whisper, optimized for Rockchip platforms. With the RKLLM toolchain, it also supports edge LLM deployment.

One-Command Deployment Tools

Skip complex build environments and speed up deployment with one-command tools. With Seeed-optimized RKNN-Toolkit2, models can be converted from ONNX to NPU deployment in just minutes, making AI application deployment faster and easier.

Comprehensive Tutorials, Projects, and Community

From basic NPU benchmarking to real-world applications such as YOLO11-based industrial detection, AI Lab provides step-by-step guides, practical project examples, and community support to help developers build faster.

 

Feature 6: Pre-installed Armbian and Multi-OS

The device comes with Armbian pre-installed for a fast out-of-the-box experience.

With the help of Armbian, we are able to provide long-term maintained system images, security updates, encrypted OS options, and OTA upgrade support for production-ready deployment.

It also supports multiple operating systems through the Rockchip ecosystem, giving developers more flexibility across different projects, Ubuntu, Android, Debian.

 

Feature 7: Simultaneous 3-Display Output with 8K Video Capability

The reComputer RK3576 Series supports simultaneous 3-display output, giving developers more flexibility for digital signage, control centers, smart retail, and multimedia systems.

Supported output interfaces include:

HDMI | MIPI DSI | Type-C (DP Alt Mode)

It also offers strong multimedia capabilities:

Up to 4K@60fps video encoding with H.265 / H.264
Up to 8K@30fps video decoding with H.265 / H.264 / AV1 / AVS2

This makes it ideal for high-resolution AI vision and multimedia applications.

 

Technical Specifications

Specification reComputer RK3576 reComputer RK3588
SKU 4GB RAM:100062096
8GB RAM:100052518
8GB RAM:100071234
16GB RAM:100086238
CPU 4x Cortex-A72@2.2GHz
4x Cortex-A53@2.0GHz
4x Cortex-A76@2.4GHz
4x Cortex-A55@1.8GHz
GPU ARM Mali-G52 MC3 ARM Mali-G610 MC4
NPU INT8@6TOPS; Supporting INT4/8/16/FP16/BF16/TF32 mixed operations
Operating System Debian 12
RAM LPDDR5: 4GB/8GB/16GB LPDDR5: 8GB/16GB/32GB
Power Input 9V-19VDC
PoE (as powered device) 1x PoE PD 1x PoE PD
Button 1x Power; 1x Recovery; 1x MaskROM
Ethernet 1x Gigabit Ethernet
1x Gigabit Ethernet with PoE support*
1x 2.5 Gigabit Ethernet
1x 2.5 Gigabit Ethernet with PoE support*
USB 1x Type A USB 3.0
3x Type A USB 2.0
1x Type C for OTG & DP
4x Type A USB 3.0
1x Type C for OTG & DP
HDMI 1x HDMI 2.0 2x HDMI 2.1;1x HDMI 2.0 Input
SIM Card 1x nano SIM Card Slot
SD Card 1 x microSD card slot
SSD Card PCle2.1x 1 for NVMe SSD or Al Accelerator PCle3.0x 4 for NVMe SSD or Al Accelerator
PCle2.1x 1for NVMe SSD or Al Accelerator
LED 1x Power; 1x Status; 1x User
Buzzer 1 1
Wi-Fi Onboard WiFi6 & BT5.4 with FPC Antenna
BLE
LoRa USB LoRa®*/SPI LoRa®* USB LoRa®*/SPI LoRa®*
4G/5G Cellular 4G LTE* 4G LTE*
Certification FCC/CE/TELEC/RoHS
Operating Temperature 0~60°C 0~55°C
Storage Temperature -20~90 °C -20~90 °C
Operating Humidity 10~95% RH 10~95% RH
RTC 1x 2PIN 1x 2PIN
Heat Dissipation Heatsink with Fan
Enclosure Material ABS Plastic
Warranty 1 year

 

Hardware Overview

 

Application

Check More Demos on reComputer AI lab.

 

Documents

reComputer RK35XX Series Flyer

reComputer RK3576 Schematic

recomputer_rk3576 Top Cover ABS

recomputer_rk3576 Bottom Cover ABS

reComputer RK3576 User Manual

 

Part List

reComputer RK3576-20 x1
12V/3A Power Adapter (with 1x US/EU/UK/AU Plugs) x1
32GB microSD Card x1
User Manual x1
What are the main hardware specifications of the reComputer RK3576-20 Open Rockchip AI Box?
The box is based on a RK3576 processor with 8 cores, 4 x Cortex A72 at 2.2 GHz and 4 x Cortex A53 at 2.0 GHz. It uses LPDDR5 RAM with options of 4GB, 8GB or 16GB and includes an ARM Mali GPU G52 MC3 and a 6 TOPS NPU for on device AI.
How does the built in 6 TOPS NPU benefit on device AI inference?
The 6 TOPS NPU enables on device AI inference for fast, private processing without cloud dependence, with capabilities for CV, LLM, VLM, STT and TTS workloads.
What expansion options are available for storage and wireless connectivity?
The device offers one M.2 M Key PCIe 2.1x1 slot for SSD expansion and AI accelerators up to 26 TOPS, plus one miniPCIe for wireless modules such as 4G LTE, LoRaWAN and Wi Fi HaLow.
What AI models and frameworks are supported by this box?
It supports a wide range of AI models for computer vision and speech, and frameworks including ONNX, PyTorch, TensorFlow, TensorFlow Lite, Caffe and Darknet.
What resources exist to help me develop and deploy AI projects on this box?
Seeed provides the reComputer AI Lab with one click tools, ready to use resources, optimized edge AI model demos for CV, LLM, VLM, STT and TTS, tutorials, deployment tools, containerized apps and community projects.
Welcome to the discussion thread for reComputer RK3576-20 Open Rockchip AI Box for AI Development
Feel free to ask questions, share tips or report issues.
Add Post

For more discussion Click Here

Fill out the form below and our team will get back to you with bulk pricing.

Description
Ask AI about this product

reComputer RK3576-20 Open Rockchip AI Box for AI Development

This product is more than an open-source 8-core Linux AI box. Built on the powerful Rockchip RK3576 4GB RAM, it is designed to make edge AI development and deployment faster and easier.

To improve the developer experience, Seeed offers reComputer AI Lab — a platform that helps you deploy AI applications with one-click tools and ready-to-use resources. It includes optimized edge AI model demos for CV, LLM, VLM, STT, and TTS, interactive tutorials, deployment tools, containerized applications, and community projects to speed up development on Rockchip.

With rich I/O, multiple OS support, wireless expansion options, and active cooling, this compact AI box is ready for a wide range of edge AI workloads. It is ideal for smart vision systems, voice-enabled devices, AI agents, robotics, and industrial edge applications.

 

Key Features

Feature 1: Based on Powerful RK3576 Processor

The reComputer RK3588 features an advanced CPU architecture with

4 × Cortex-A72 @2.2GHz | 4 × Cortex-A53 @2.0GHz
LPDDR5 (4GB/8GB/16GB) | ARM Mali GPU-G52 MC3

 

Feature 2: 6 TOPS NPU Embedded

With a built-in 6 TOPS NPU, it delivers efficient AI inference directly on the device, reducing latency and enhancing privacy without relying on the cloud.

AI performance highlights include:

Vision CNN: YOLO11@77.9FPS(640*640)
LLM: capable for deepseek-r1-distill-qwen 7b support
VLM: capable for qwen2.5-vl 3b
STT: whisper_base_20s (RTF 0.218) with real-time processing support
TTS: mms_tts_eng_200 (RTF 0.069) with real-time processing support

Check the details of the benchmark.

 

Feature 3: Compute Power, Storage, and Wireless Connectivity Expandable

It offers flexible expansion through

a) 1 × M.2 M Key: PCIe 2.1x1, supports SSD expansion and AI accelerators such as Hailo and Rockchip, up to 26TOPS

b) 1 x miniPCIe for wireless expansion, like 4G LTE, LoRaWAN, Wi-Fi HaLow

 

Feature 4: Broad Model and Framework Support

It also supports a wide range of popular AI models, From computer vision and speech to LLM-powered edge intelligence, including: YOLO, MobileNet V2, RetinaFace, CLIP, Whisper, DeepSeek-R1, Qwen2-VL,...................

Framework support includes: ONNX, PyTorch, TensorFlow, TensorFlow Lite, Caffe, Darknet,...................

Toolchain support: RKNN-Toolkit2

This broad compatibility makes it easy to migrate existing models and build integrated edge AI applications across Perception AI, Generative AI, Agent and Physical AI.

 

Feature 5: Get Started in Seconds with reComputer AI Lab

Deploying AI at the edge is often complicated — from environment setup and model conversion to performance optimization. To simplify this process, Seeed created reComputer AI Lab, a developer platform designed to lower the barrier to edge AI development.

It provides a complete collection of optimized AI model demos for CV, LLM, and VLM, along with interactive tutorials, deployment tools, containerized AI applications, project examples, and community resources. It is built to accelerate AI development and deployment on Rockchip, Raspberry Pi, and NVIDIA Jetson platforms.

For Rockchip users, reComputer AI Lab includes:

Ready-to-Use Models

Access pre-optimized models without starting from scratch. AI Lab includes models such as YOLO11, MobileNet, CLIP, and Whisper, optimized for Rockchip platforms. With the RKLLM toolchain, it also supports edge LLM deployment.

One-Command Deployment Tools

Skip complex build environments and speed up deployment with one-command tools. With Seeed-optimized RKNN-Toolkit2, models can be converted from ONNX to NPU deployment in just minutes, making AI application deployment faster and easier.

Comprehensive Tutorials, Projects, and Community

From basic NPU benchmarking to real-world applications such as YOLO11-based industrial detection, AI Lab provides step-by-step guides, practical project examples, and community support to help developers build faster.

 

Feature 6: Pre-installed Armbian and Multi-OS

The device comes with Armbian pre-installed for a fast out-of-the-box experience.

With the help of Armbian, we are able to provide long-term maintained system images, security updates, encrypted OS options, and OTA upgrade support for production-ready deployment.

It also supports multiple operating systems through the Rockchip ecosystem, giving developers more flexibility across different projects, Ubuntu, Android, Debian.

 

Feature 7: Simultaneous 3-Display Output with 8K Video Capability

The reComputer RK3576 Series supports simultaneous 3-display output, giving developers more flexibility for digital signage, control centers, smart retail, and multimedia systems.

Supported output interfaces include:

HDMI | MIPI DSI | Type-C (DP Alt Mode)

It also offers strong multimedia capabilities:

Up to 4K@60fps video encoding with H.265 / H.264
Up to 8K@30fps video decoding with H.265 / H.264 / AV1 / AVS2

This makes it ideal for high-resolution AI vision and multimedia applications.

 

Technical Specifications

Specification reComputer RK3576 reComputer RK3588
SKU 4GB RAM:100062096
8GB RAM:100052518
8GB RAM:100071234
16GB RAM:100086238
CPU 4x Cortex-A72@2.2GHz
4x Cortex-A53@2.0GHz
4x Cortex-A76@2.4GHz
4x Cortex-A55@1.8GHz
GPU ARM Mali-G52 MC3 ARM Mali-G610 MC4
NPU INT8@6TOPS; Supporting INT4/8/16/FP16/BF16/TF32 mixed operations
Operating System Debian 12
RAM LPDDR5: 4GB/8GB/16GB LPDDR5: 8GB/16GB/32GB
Power Input 9V-19VDC
PoE (as powered device) 1x PoE PD 1x PoE PD
Button 1x Power; 1x Recovery; 1x MaskROM
Ethernet 1x Gigabit Ethernet
1x Gigabit Ethernet with PoE support*
1x 2.5 Gigabit Ethernet
1x 2.5 Gigabit Ethernet with PoE support*
USB 1x Type A USB 3.0
3x Type A USB 2.0
1x Type C for OTG & DP
4x Type A USB 3.0
1x Type C for OTG & DP
HDMI 1x HDMI 2.0 2x HDMI 2.1;1x HDMI 2.0 Input
SIM Card 1x nano SIM Card Slot
SD Card 1 x microSD card slot
SSD Card PCle2.1x 1 for NVMe SSD or Al Accelerator PCle3.0x 4 for NVMe SSD or Al Accelerator
PCle2.1x 1for NVMe SSD or Al Accelerator
LED 1x Power; 1x Status; 1x User
Buzzer 1 1
Wi-Fi Onboard WiFi6 & BT5.4 with FPC Antenna
BLE
LoRa USB LoRa®*/SPI LoRa®* USB LoRa®*/SPI LoRa®*
4G/5G Cellular 4G LTE* 4G LTE*
Certification FCC/CE/TELEC/RoHS
Operating Temperature 0~60°C 0~55°C
Storage Temperature -20~90 °C -20~90 °C
Operating Humidity 10~95% RH 10~95% RH
RTC 1x 2PIN 1x 2PIN
Heat Dissipation Heatsink with Fan
Enclosure Material ABS Plastic
Warranty 1 year

 

Hardware Overview

 

Application

Check More Demos on reComputer AI lab.

 

Documents

reComputer RK35XX Series Flyer

reComputer RK3576 Schematic

recomputer_rk3576 Top Cover ABS

recomputer_rk3576 Bottom Cover ABS

reComputer RK3576 User Manual

 

Part List

reComputer RK3576-20 x1
12V/3A Power Adapter (with 1x US/EU/UK/AU Plugs) x1
32GB microSD Card x1
User Manual x1
FAQ
What are the main hardware specifications of the reComputer RK3576-20 Open Rockchip AI Box?
The box is based on a RK3576 processor with 8 cores, 4 x Cortex A72 at 2.2 GHz and 4 x Cortex A53 at 2.0 GHz. It uses LPDDR5 RAM with options of 4GB, 8GB or 16GB and includes an ARM Mali GPU G52 MC3 and a 6 TOPS NPU for on device AI.
How does the built in 6 TOPS NPU benefit on device AI inference?
The 6 TOPS NPU enables on device AI inference for fast, private processing without cloud dependence, with capabilities for CV, LLM, VLM, STT and TTS workloads.
What expansion options are available for storage and wireless connectivity?
The device offers one M.2 M Key PCIe 2.1x1 slot for SSD expansion and AI accelerators up to 26 TOPS, plus one miniPCIe for wireless modules such as 4G LTE, LoRaWAN and Wi Fi HaLow.
What AI models and frameworks are supported by this box?
It supports a wide range of AI models for computer vision and speech, and frameworks including ONNX, PyTorch, TensorFlow, TensorFlow Lite, Caffe and Darknet.
What resources exist to help me develop and deploy AI projects on this box?
Seeed provides the reComputer AI Lab with one click tools, ready to use resources, optimized edge AI model demos for CV, LLM, VLM, STT and TTS, tutorials, deployment tools, containerized apps and community projects.
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Welcome to the discussion thread for reComputer RK3576-20 Open Rockchip AI Box for AI Development
Feel free to ask questions, share tips or report issues.
Add Post

For more discussion Click Here

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