Worked on the jeejeelee/vllm repository to enhance the distributed GPU model runner, focusing on correctness and performance during asynchronous scheduling. Addressed a bug in the sampling-token reception path by updating the conditional logic to ensure sampled token IDs are only received from non-last ranks, which eliminated invalid inter-rank communications and improved reliability in distributed inference. Utilized Python for backend development, applying asynchronous programming techniques and rigorous testing to validate the fix. Maintained clear commit messages and signed-off contributions for traceability, demonstrating attention to code hygiene and robust debugging practices in distributed systems and high-throughput LLM serving environments.