Optimizing PyTorch Model Inference on CPU

Optimizing AI model inference is critical for runtime performance, and CPUs can be a surprisingly effective choice in certain scenarios. Reasons for using a CPU over a dedicated accelerator include greater accessibility, availability, potentially lower latency in complex pipelines, and better cost-effectiveness for lower inference loads. The post demonstrates optimization techniques for a PyTorch Resnet50 model on a 4th Gen Intel® Xeon® Scalable CPU, which includes built-in accelerators like AMX for AI workloads. By leveraging Intel's optimized software stack, including oneDNN and the Intel Extension for PyTorch (IPEX), significant performance gains can be achieved. The overall goal is to show that meaningful improvements are possible through relatively simple techniques without deep specialization.