During February 2026, Jeejee Lee developed and integrated Generalized Query Attention (GQA) zero-copy support for multimodal processing on CPU within the jeejeelee/vllm repository. This work focused on reducing tensor duplication and improving inference efficiency by enabling zero-copy data paths in attention mechanisms. Jeejee Lee refactored existing functions, introduced new parameters to facilitate GQA, and ensured compatibility with current model workflows. The implementation leveraged Python and PyTorch, demonstrating expertise in deep learning and neural networks. Although no major bugs were addressed, the feature delivered measurable improvements in CPU throughput and streamlined the adoption of multimodal processing in the codebase.