Developed and integrated the OpenVINO backend for the pytorch/executorch repository, enabling optimized inference across Intel CPUs, GPUs, and NPUs. This work involved implementing OpenVINO quantization to improve model performance and reduce memory footprint, as well as creating comprehensive end-to-end examples and unit tests to validate functionality across diverse model types. Leveraging C++, Python, and CMake, the integration enhanced cross-platform compatibility and hardware-accelerated deployment for deep learning models. The approach focused on reliability and performance, ensuring that the new backend delivered measurable improvements in throughput and latency while maintaining robust testing standards for production-ready deployment scenarios.