在 CPU 上擴展 BERT 推論效能（第一部分）
Original: Scaling-up BERT Inference on CPU (Part 1)
In many real-world enterprise production environments, although GPUs offer extremely high throughput for deep learning inference, CPUs…
本文為 Hugging Face 與 Intel 合作的 CPU 優化指南首篇。文章深入探討 CPU 物理核心與超線程對深度學習的影響，並詳細說明如何透過正確配置 PyTorch 的 Intra-op/Inter-op 執行緒與環境變數（如 OMP_NUM_THREADS）來避免資源競爭。最後介紹了利用 ONNX Runtime 進行算子融合，為 BERT 在 CPU 上的部署奠定高效基礎。
In many real-world enterprise production environments, although GPUs offer extremely high throughput for deep learning inference, CPUs remain indispensable due to their deployment flexibility, low cost, and large memory capacity (suitable for handling long texts or large batches). However, deploying Hugging Face's BERT model directly in a default CPU environment often leads to excessively high latency. This article is the first part of a series guide co-authored by Hugging Face and Intel, aimed at guiding developers on how to significantly improve BERT inference performance on CPUs through a deeper understanding of hardware architecture and software configuration — without changing the model architecture itself.
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