During December 2025, Zhen Xu enhanced quantization support for Hexagon NPU inference in both the llama.cpp and ggml repositories. He implemented true Q8_0 quantization with configurable FP32 group sizes, introducing build-time flexibility through CMake. His work focused on improving the accuracy and performance of mixed-precision matrix multiplication, adding inline optimizations and utilities to streamline the quantization path. By aligning quantization logic across both repositories, Zhen established feature parity and laid the foundation for production-ready tuning. The engineering effort demonstrated depth in C programming, embedded systems, and performance optimization, addressing the need for flexible, high-accuracy quantization in embedded AI workloads.