As AI hardware diversification accelerates, developers are increasingly looking beyond NVIDIA GPUs. This post documents the process of adapting llama.cpp for Hygon's K100 AI accelerator, a domestic Chinese chip, and successfully deploying the Keye-VL-2.0-30B-A3B multimodal model. The author shares specific compilation steps, performance observations, and deployment considerations. This is significant because it demonstrates that advanced AI models can run on alternative hardware, which is crucial for cost reduction, supply chain resilience, and regional technology independence. For global developers, it offers a glimpse into the practical challenges and solutions of hardware-software co-design in the AI space. The post also highlights the maturity of the Chinese AI hardware ecosystem, which is becoming a viable option for various workloads. While the specific instructions are tailored to the K100, the general approach of porting and optimizing inference engines for new accelerators is broadly applicable.
A practical guide to compiling llama.cpp for Hygon's K100 AI accelerator and deploying a 30B multimodal model, showcasing the growing ecosystem for domestic Chinese AI chips.