During April 2026, Dawn Han developed FP8 activation support for the fused Mixture of Experts (MoE) model in the vllm-project/tpu-inference repository. This feature introduced a lower-precision inference path using FP8 data types, reducing memory usage and potentially increasing throughput for TPU-backed workloads. Dawn’s work required deep knowledge of deep learning, machine learning, and Python scripting, as well as expertise in TPU programming. The implementation established a scalable approach for FP8 precision in MoE inference, enabling the project to support larger models more efficiently. All efforts focused on robust feature delivery, code quality, and collaborative development practices throughout the month.