Developed mixed-precision quantized matrix multiplication support for int4xfp8 in the vllm-project/tpu-inference repository, focusing on efficient TPU inference. The work involved implementing kernel modifications in Python and JAX to enable mixed-precision operations, specifically targeting improved throughput and reduced energy consumption for production inference workloads. Tests were added and kernel logic was adjusted to validate and support the new int4xfp8 data type, enhancing both reliability and test coverage. This feature lays the foundation for scalable, cost-effective TPU-backed inference deployments and aligns with broader performance goals in machine learning and quantum computing environments, demonstrating depth in specialized hardware programming.