Shijie Feng developed an FP8 paged multi-query attention logits optimization for the ROCm/aiter repository, focusing on deep learning performance at scale. Leveraging Triton kernels and CUDA, Shijie implemented context-split optimization to improve efficiency on FP8 data paths, addressing the computational demands of modern attention workloads. The work included comprehensive testing and performance benchmarking in Python and C++, ensuring the new feature met throughput and scalability targets. By delivering end-to-end functionality with validation against performance metrics, Shijie contributed depth to ROCm/aiter’s FP8 ecosystem, demonstrating expertise in deep learning optimization and low-precision computation within a high-performance engineering context.