Developed and delivered an FP8 precision casting configuration for the PyTorch AO Library, enabling support for low-precision workflows within the pytorch/ao repository. The work focused on adding a dedicated configuration to facilitate FP8 (Float8) casting, which allows model developers to leverage faster throughput and reduced memory usage during training and inference. Implemented in Python using deep learning and machine learning techniques, the solution validated the integration path for FP8 within the AO build. This contribution established a foundation for broader low-precision optimization in the AO ecosystem, aligning with ongoing efforts to improve performance and cost efficiency for production workloads.