Guangyun Huang developed and integrated the Gated Delta Rule (GDN) for Hopper architectures in the flashinfer-ai/flashinfer repository, focusing on production-ready delta-rule workflows for deep learning models. Leveraging CUDA and GPU programming in both C++ and Python, Guangyun implemented a Python API for GDN prefill, including chunked and host-launched variants, and exported the functionality via FFI. The work included SM90-optimized performance enhancements, comprehensive benchmarks measuring runtime, TFLOPs, and bandwidth, and thorough test coverage to validate correctness. This feature established a robust foundation for delta-rule operations on Hopper-enabled systems, aligning with Qwen-next-like model requirements and production deployment standards.