Over a two-month period, contributed to the tenstorrent/vllm and jeejeelee/vllm repositories by building and enhancing backend infrastructure for model evaluation and device compatibility. Developed CI/CD pipelines and Docker-based workflows to automate testing, validation, and release processes, ensuring alignment with upstream changes. Implemented a platform hook for TPU-based CPU input handling and introduced benchmarking features for structured output testing, leveraging Python and YAML for configuration and scripting. Addressed a bug in token budgeting by refining prompt truncation logic, improving reliability in data processing. The work emphasized robust API development, backend scalability, and streamlined device configuration for machine learning workflows.