Xue delivered an FP8 precision casting configuration for the pytorch/ao repository, enabling low-precision workflows within the PyTorch AO Library. Using Python and leveraging deep learning and machine learning expertise, Xue implemented a dedicated configuration that supports FP8 (Float8) casting, allowing for more memory-efficient and higher-throughput model training and inference. The work involved integrating the new configuration into the existing AO build, validating its compatibility, and ensuring it aligns with the library’s performance and cost-reduction objectives. This contribution established a foundation for broader low-precision optimization in the AO ecosystem, addressing both developer needs and production workload requirements.