In the development of open source and RISC-V ecosystem, domestic operating systems have achieved a key technical breakthrough. Recently, with the full support of the deepin-ports SIG (Special Interest Group), Deepin Operating Systemdeepin 25 has successfully completed a deep adaptation to the next-generation flagship RISC-V chip SpacemiT K3 by Jindie Space. With the upgrade of the underlying toolchain and the integration of the local AI runtime, the system not only smoothly runs the local AI model "Xiao Wu" on small hardware platforms, but also marks the first time that the desktop operating system on the RISC-V platform truly possesses the capability for offline large model inference out-of-the-box. As an RVA23 specification computing platform specifically designed for the AI era, Jindie Space K3 adopts advanced RISC-V heterogeneous fusion computing technology. Inside a single chip, it cleverly integrates three complementary core types, building an efficient "general + AI + real-time" three-domain collaborative architecture. Among them, 8 X100 general-purpose computing cores have a maximum clock speed of 2.4 GHz and about 130 KDMIPS of overall general computing power, responsible for handling system control and daily desktop interactions; 8 A100 AI computing cores specially designed for AI workloads are equipped with a self-developed matrix operation extension (IME) instruction set, providing about 60 TOPS of native AI computing power, and can maintain a smooth inference speed of more than 10 tokens per second when running a 30 billion parameter large model. In addition, the chip is equipped with 2 RT24 real-time cores for system management, a 3D GPU, and a video encoding engine. With a typical power consumption of 15 to 25 watts, it balances high energy efficiency and rich application scalability. To make the new hardware perform at its best, deepin 25 has made comprehensive deep optimizations in the system architecture. In addition to maintaining the 6.18 kernel branch, u-boot, and dedicated GPU drivers, the development team has innovatively introduced the Next repository as an upper-layer overlay repository. This repository fully enables RVA23 global optimization for the riscv64 architecture, upgrading the core toolchain to natively support the RVA23 Profile GCC 15/16, Binutils 2.46, Glibc 2.42, LLVM 21/22, and OpenSSL 3.5 with various algorithm assembly-level optimizations. This higher baseline basic runtime environment not only lays a solid foundation for fully tapping into the hardware instruction potential of the upper software, but also accumulates valuable technical verification experience for deepin's subsequent cross-major version migration. In the highly anticipated local AI capabilities construction, this adaptation has successfully打通 the complete chain from the underlying toolchain to the application layer. The built-in Xiao Wu student deeply integrates with the RVA23 and SpacemiT matrix operation extension through deepin-modelhub, and can automatically schedule the optimal operator according to the chip architecture. At the same time, the widely used GGML computing library for mainstream model runtime such as llama.cpp has fully opened support for SpacemiT-related instruction sets, achieving complete vector and matrix computation acceleration. Test results show that when users run local AI models in the system, the system can intelligently dispatch computing tasks to the 8 A100 AI cores for efficient processing. Currently, the latest deepin 25 RISC-V experience image adapted to K3 has been officially released in the deepin-ports image list. Developers and open source enthusiasts can download and complete the flashing through official channels. With the continuous improvement of the Next repository's basic construction, this breakthrough not only allows RISC-V desktop devices to truly have the ability to run large models offline, but also opens up broad space for the collaborative innovation of domestic open source software and hardware ecosystems.