Shifengmin worked on the jd-opensource/xllm repository, delivering an NPU DeepSeek Context Parallelism and cp_size enhancement for the deepseek v32/GLM 5 model. He implemented support for a cp_size parameter across multiple components, enabling context partitioning and load balancing to process several contexts in parallel on NPU hardware. Using C++ and leveraging expertise in deep learning and parallel computing, Shifengmin’s work improved throughput potential and resource utilization for multi-context inference. The feature was co-authored with teammates and focused on robust, high-quality code, laying a solid foundation for scalable production workloads without introducing major bugs during the development period.