Aarush contributed to the pytorch/ao repository by developing a hardware-specific optimization for per-tensor scaled weights on NVIDIA B200 and GB200 GPUs. He implemented a kernel selection flow in Python that avoids using MSLK on these GPUs, instead preferring the TORCH backend to maintain compatibility and performance. His approach included robust guardrails, such as explicit warnings for unsupported kernel requests and adjustments to AUTO behavior, ensuring correct operation across hardware variants. Aarush also enhanced the testing infrastructure, expanding coverage for kernel preferences and improving code maintainability, demonstrating depth in GPU programming, quantization, and rigorous software testing practices.