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A capable NPU does not automatically make every AI model run efficiently. Model selection, conversion, quantization, memory management, and application integration all affect the final result.

A typical RK3588 AI deployment process begins with selecting a model that fits the application. Developers then evaluate whether its operators and layers are supported by the relevant Rockchip software stack. The model can be converted into RKNN format using RKNN-Toolkit2, followed by accuracy and performance testing.

Quantization can reduce model size and improve inference efficiency in supported configurations, but developers should validate the effect on accuracy. INT8 quantization is often considered for edge inference because it can offer a useful balance between performance and model quality. Other supported precision formats may be appropriate for particular workloads.

The next step is to optimize the full processing pipeline. For camera-based applications, this may involve coordinating video capture or decoding, image preprocessing, NPU inference, CPU-based postprocessing, and result transmission. Unnecessary memory copies can add latency, while sustained workloads can increase thermal demands.



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