Contributed a performance-focused feature to the jeejeelee/vllm repository by optimizing the fused_topk_bias operation for ROCm environments. The work involved replacing fallback torch operations with an asynchronous iterator (aiter), which accelerated inference and improved the efficiency of expert group handling within the model. This enhancement was implemented using Python and leveraged deep learning and machine learning expertise, with a strong emphasis on performance optimization for GPU-accelerated workflows. The codebase was updated to include ROCm-specific optimization guidelines, ensuring maintainability and clarity for future contributors. The deliverable was completed within a month and included a signed-off commit for traceability.