Worked on the jeejeelee/vllm repository to deliver performance optimizations for Qwen3.5 inference on NVIDIA H20 GPUs. Focused on improving inference throughput and GPU resource utilization, the work enhanced compatibility with NVIDIA hardware and enabled more efficient, scalable deployments. Leveraged CUDA and Python to implement targeted changes within the vLLM internals, addressing bottlenecks and optimizing resource allocation for machine learning workloads. Collaborated across teams through code review to ensure robust integration of the new feature. No major bugs were addressed during this period, with efforts concentrated on delivering a single, impactful feature that improved cost-effectiveness and deployment readiness.