# Hugging Face — Accelerating PyTorch Transformers with Intel Sapphire Rapids - part 2

- Company: Hugging Face (huggingface.co)
- Announced: 2023-02-06
- Category: not stated
- Coverage: not counted
- Announcement: no
- Group: routine
- Source: https://huggingface.co/blog/intel-sapphire-rapids-inference
- Record: https://forck.live/items/2102-accelerating-pytorch-transformers-with-intel-sapphire-rapids-part-2
- Subject: Platform

This is a technical blog post about accelerating PyTorch Transformers inference on Intel Sapphire Rapids CPUs using the Optimum Intel library. It benchmarks models like distilbert-base-uncased, bert-base-uncased, and roberta-base on Ice Lake and Sapphire Rapids servers, measuring latency for short and long token sequences. It does not announce a new product, model, API, or any of the other specific categories.

## Evidence

Verbatim from https://huggingface.co/blog/intel-sapphire-rapids-inference:

> In this post, we're going to focus on inference. Working with popular HuggingFace transformers implemented with PyTorch, we'll first measure their performance on an Ice Lake server for short and long NLP token sequences. Then, we'll do the same with a Sapphire Rapids server and the latest version of Hugging Face Optimum Intel, an open-source library dedicated to hardware acceleration for Intel platforms.

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Record: https://forck.live/items/2102-accelerating-pytorch-transformers-with-intel-sapphire-rapids-part-2
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