Jordan Dotzel developed enhanced Mixture-of-Experts inference capabilities for the vllm-project/tpu-inference repository, focusing on supporting broader weight formats and improving run-time flexibility. He implemented a new module that enables direct loading of MXFP4 and BF16 weights into MoE inference, incorporating online requantization to dynamically adjust quantized weights during execution. This approach allows the model to efficiently handle different weight formats and blend expert outputs for improved accuracy and efficiency. Jordan utilized Python, JAX, and PyTorch to deliver this feature, demonstrating depth in deep learning and quantization while addressing the need for flexible, high-performance inference in modern machine learning workflows.