Worked on the ROCm/jax repository to deliver a TPU 8I capacity upgrade, increasing the number of accumulators from 128 to 256 to enhance compute throughput for TPU-based workloads. This feature was implemented using Python and required a deep understanding of TPU architecture and machine learning workloads. The code was integrated into the mainline branch following thorough validation and code review, ensuring readiness for upcoming performance tests. By expanding accumulator capacity, the work improved scalability and enabled greater parallelism, supporting faster experimentation with larger models and aligning with the project’s roadmap for high-performance, scalable machine learning infrastructure on TPUs.