AI BriefingAccelerating Inference on Intel Sapphire Rapids
Key point
It covers how to optimize inference performance of PyTorch Transformer models using Intel Sapphire Rapids CPUs and the Optimum Intel library.
Details
It explains how to accelerate inference performance of PyTorch-based Transformer models using Intel's 4th-generation Xeon CPU, Sapphire Rapids, and AMX (Advanced Matrix Extensions) instructions.
It presents situations where CPU-based inference should be considered instead of GPUs, such as model size, level of parallelism, cost efficiency, and hardware scalability, and provides selection criteria based on specific business requirements.
It sets up AWS EC2's Ice Lake (c6i.16xlarge) and Sapphire Rapids (r7iz.16xlarge-metal) instances as comparative experiment targets, and optimizes performance using the Optimum Intel library.
It benchmarks latency and batch inference performance according to sentence length for major NLP models such as DistilBERT, BERT, and RoBERTa, demonstrating the degree of performance improvement in real-world environments.
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