Dhiraj worked on enhancing multi-GPU support in the flashinfer-ai/flashinfer repository, focusing on deep learning workloads using CUDA and PyTorch. He refactored cuDNN handle management to create a dedicated handle for each GPU device, ensuring correct device and stream binding for improved performance and reliability. His approach included implementing a bounded caching strategy for compute handles and execution plans, which stabilized cross-device operations and reduced runtime errors. Dhiraj also introduced diagnostic hooks to aid troubleshooting in production environments. All updates were thoroughly tested, with new tests covering multi-GPU paths, reflecting a deep and methodical approach to engineering reliability.