AI accelerator QNAP QAI-M100 with Rockchip RK1808 Cortex-A35 1.6GHz and 3 TOPS NPU

QNAP QAI-M100 with Rockchip RK1808 integrates a quad-core Cortex-A35 CPU clocked at 1.6 GHz and an on-chip neural processing unit capable of up to 3 TOPS. The module is designed to offload AI inference from host systems, enabling faster processing of image analysis, object detection and other edge AI use cases. It is intended for installation in compatible QNAP platforms where it functions as a dedicated AI acceleration card to reduce main CPU load and shorten response times for AI-driven tasks.

Advantages

The QAI-M100 provides dedicated hardware acceleration for neural network inference, offering up to 3 TOPS of performance while keeping system power consumption low. Its integrated RK1808 processor supports common AI frameworks and on-device inference workflows, allowing real-time analytics at the network edge. By handling AI workloads on a separate module, it minimizes impact on the host system’s general-purpose CPU and improves throughput for concurrent tasks such as video analytics and image classification.

Suitable for

• Real-time video analytics and surveillance systems
• Edge AI deployments for object detection and classification
• Image recognition and preprocessing on NAS or edge servers
• Applications requiring offloaded inference to reduce CPU load

Key specifications

• Processor: Rockchip RK1808 with Cortex-A35 cores at 1.6 GHz
• Neural processing: Integrated NPU with up to 3 TOPS performance
• Primary function: AI inference acceleration for edge deployments
• Typical use: Image analysis, object detection, and classification

How to use

Install the QAI-M100 module into a compatible QNAP host according to the host platform’s module installation instructions and power down the system before insertion. After physical installation, enable the module in the QNAP management interface or firmware settings and install any required drivers or firmware updates provided by QNAP. Deploy AI models optimized for on-device inference and configure your applications or QNAP services to route inference requests to the accelerator module. Monitor performance and system logs to verify that inference tasks are processed by the module.

Usage recommendations

Use models converted or compiled for the RK1808 architecture and the module’s NPU to achieve optimal throughput and accuracy. For continuous video analytics, consider batching or resizing input frames to match model input dimensions and reduce latency. Ensure the host firmware and drivers are kept up to date and verify compatibility with the target QNAP platform before deployment.