The RK3588 system-on-chip integrates a 6 TOPS NPU, making it a compelling platform for edge AI inference. This guide walks through the development workflow using RKNN-Toolkit2 for computer vision models like YOLOv5 and RKLLM-Toolkit for large language models such as DeepSeek-R1-1.5B. Developers can leverage these tools to convert, optimize, and deploy models directly onto the NPU, achieving low-latency inference without cloud dependency. The guide covers environment setup, model conversion, and runtime integration, providing a practical starting point for building intelligent edge devices. As edge AI continues to grow, mastering vendor-specific NPU toolchains becomes a key skill for embedded engineers.
A detailed guide for deploying YOLOv5 and DeepSeek-R1 on the RK3588 NPU using Rockchip's toolkits. Essential reading for edge AI developers.