In December 2025, Jeejee Lee contributed to the jeejeelee/vllm repository by implementing Expert Parallelism with Load Balancing (EPLB) support using NVFP4 FusedMoE, targeting scalable distributed inference. Working primarily in Python, Jeejee integrated EPLB within the ModelOptNvFp4FusedMoE optimizer, enhancing model scalability and performance across multi-GPU deployments. The work included developing comprehensive end-to-end tests to validate EPLB’s interaction with the FusedMoE layer in distributed settings, ensuring correctness and stability. Leveraging skills in distributed systems, machine learning, and model optimization, Jeejee’s feature reduced inference latency and improved throughput, addressing production-scale efficiency for large model deployments.