Worked on performance optimization for the jeejeelee/vllm repository, focusing on improving token ID retrieval during next-token preparation in the spec decode path. Addressed memory overhead by removing per-step numpy allocations, which reduced the memory footprint and increased throughput for token generation. This optimization was implemented using Python and involved careful profiling and memory management to support higher concurrency and lower latency in large language model serving. The work did not involve bug fixes but concentrated on enhancing performance and code hygiene, resulting in faster token generation, lower memory usage, and improved scalability for production machine learning deployments.