Optimizing PyTorch Model Inference on AWS Graviton

AWS Graviton processors present a powerful CPU alternative for AI model inference, featuring custom Arm-based hardware with dedicated engines for vector and matrix operations. Significant performance gains can be unlocked by applying a series of optimizations to PyTorch models running on this architecture. The most impactful techniques involve memory optimizations, such as switching to the channels-last memory format and enabling automatic mixed precision with the bfloat16 data type. By combining these methods with batched inference and specific runtime configurations, a model's throughput can be more than quadrupled, demonstrating the substantial benefits of hardware-specific tuning.