Deyu Foo developed distributed optimizer capabilities for the NVIDIA/Megatron-LM repository, focusing on scalable training for large deep learning models. He introduced a layer-wise distributed optimizer and a muon optimizer, both designed to improve parameter update efficiency and enhance tensor parallelism in multi-node, multi-GPU environments. Using Python and PyTorch, Deyu engineered these optimizers to distribute weights across ranks, accelerating large-scale optimization and increasing training throughput. His work integrated seamlessly with Megatron-LM’s existing distributed pipeline, laying a technical foundation for scaling to larger architectures. The project demonstrated depth in distributed computing and optimizer design, addressing core challenges in model scalability.