We present Bianbu LXQt, a user-oriented desktop environment for RISC-V platforms built on a deeply adapted LXQt software stack, optimized for real hardware such as SpacemiT’s K1 and K3 SoCs. Unlike straightforward ports that assume x86-like hardware standardization, this work addresses common RISC-V Linux challenges, including fragmented peripheral support and the absence of a unified hardware abstraction layer. SpacemiT's CPUs integrate AI-oriented instruction extensions such as IME, enabling CPU-based inference without discrete GPUs or NPUs, requiring coordinated adaptation across the OS and AI frameworks.
Preserving LXQt’s lightweight design, we redesigned the UI and interaction logic to improve responsiveness and visual consistency on resource-constrained RISC-V systems. Development was accelerated using AI-assisted tooling, while continuous feedback from educators and early adopters guided iterative fixes for lag, crashes, and complex configuration—letting users focus on creation, learning, and development rather than system tuning.
The full software stack is open source with reproducible builds and modular components. We proved educational AI examples covering image recognition, speech processing, video analysis, and large language model inference, all with intuitive GUIs. Frameworks including ONNX Runtime, llama.cpp, and Ollama run reliably, demonstrating the feasibility of RISC-V systems for AI deployment and local AI development.
Through practical system integration, community-driven iteration, and accessible AI tooling, this work shows RISC-V can deliver a polished, daily-driver desktop environment—moving beyond a demo into a trusted open platform for developers, educators, and innovators.