Accelerate CPU-based AI inference workloads using Intel AMX on Amazon EC2

This article demonstrates how to accelerate CPU-based AI inference on Amazon EC2 using Intel Advanced Matrix Extensions (AMX), achieving up to 76% performance improvements through hardware acceleration and optimized precision formats.

• Intel AMX accelerates matrix operations directly on CPU cores using specialized hardware
• BF16 precision with AMX delivers 21-72% latency improvements at batch sizes 8+
• EC2 m8i instances provide 9-14% better performance than m7i across tested models
• Optimal batch sizes of 4-16 maximize AMX benefits for different model architectures
• Combined m8i + BF16 AMX optimization achieves up to 76% improvement vs m7i FP32
• CPU inference cost-effective for batch processing, small-medium models, variable workloads
• PyTorch automatically leverages AMX with minimal code changes via environment variables
• Benchmarked across six models: BigBird, DialoGPT, Gemma, DeepSeek, Llama, YOLOv5
• M8i delivers up to 13% better price-performance than m7i for inference workloads

This guide enables organizations to optimize CPU-based AI inference costs while maintaining performance through Intel AMX acceleration on modern EC2 instances.