In March 2026, this developer implemented Qwen3-MoE data-parallel support within the Xlite framework for the vllm-project/vllm-ascend repository, enabling scalable processing of large mixture-of-experts models on Ascend hardware. Leveraging Python and expertise in data parallelism and model optimization, they configured both data- and tensor-parallel settings to improve throughput without introducing user-facing changes. Their backend enhancements were validated against the vLLM baseline v0.16.0, ensuring stability and compatibility. By documenting deployment and bench-testing procedures, they improved repeatability and onboarding for future contributors. This work established a robust foundation for enterprise-scale LLM workloads and future optimization efforts.