During this period, contributed to the red-hat-data-services/vllm-gaudi repository by enhancing the vLLM argument parser to support a block size of 256, directly addressing performance needs for Llama3.1-70B FP8 models. This feature was implemented in Python and focused on argument parsing and model configuration, with the change driven by measured throughput improvements. The work included a targeted, auditable commit linked to a tracked issue, ensuring traceability and maintainability for future development. By aligning the feature with business value and technical requirements, the contribution demonstrated a methodical approach to performance optimization within a production machine learning environment.