Wuhui worked on performance optimization and reliability improvements in large language model infrastructure. In the ROCm/aiter repository, Wuhui developed GEMM kernel tuning configurations for Llama and Qwen, introducing Python scripts and CSV-based configuration files to support FP8 and FP4 quantization across multiple modes, which streamlined deployment and enhanced model throughput. Earlier, in vllm-project/vllm-ascend, Wuhui addressed platform configuration validation by adding robust guards against None-type parameter access, reducing runtime errors during configuration checks. The work demonstrated depth in GPU computing, machine learning optimization, and platform configuration, focusing on practical solutions that improved both stability and performance in production environments.