During April 2026, this developer delivered AMD Zen CPU backend support for the vLLM project, contributing to both the jeejeelee/vllm and red-hat-data-services/vllm-cpu repositories. The work focused on enabling Zen-optimized tensor data types, specifically bfloat16 and float32, through integration with the Zentorch library. Using Python and PyTorch, the developer implemented and aligned backend support across both repositories, ensuring a unified CPU inference path and improved performance for AMD Zen hardware. The enhancements addressed CPU utilization and cost efficiency, preparing the codebase for broader production deployment without introducing new bugs or regressions during the development period.