---
title: "Speed up BERT inference with Hugging Face Transformers and AWS Inferentia"
url: https://daily.dev/posts/speed-up-bert-inference-with-hugging-face-transformers-and-aws-inferentia-92iokymk2
source_url: https://www.philschmid.de/huggingface-bert-aws-inferentia
type: article
source: "Philipp Schmid"
published: 2026-05-31T07:41:15.807Z
updated: 2026-05-31T08:29:03.583Z
tags: ["aws", "bert"]
reading_time: 7
upvotes: 0
comments: 0
language: en
---

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# Speed up BERT inference with Hugging Face Transformers and AWS Inferentia

**[Philipp Schmid](https://daily.dev/sources/philschmid)** · 7 min read · 0 upvotes · 0 comments

## Summary

A step-by-step tutorial on deploying BERT-based Hugging Face Transformer models on AWS Inferentia via Amazon SageMaker for accelerated, low-cost inference. Covers converting a model to AWS Neuron format using torch_neuron, writing a custom inference.py script, packaging and uploading the model to S3, deploying a real-time SageMaker endpoint, and benchmarking performance. Results show 5-6ms average latency per Neuron core for sequence length 128, with the ability to run 4 models in parallel across Neuron cores for higher throughput than GPUs at lower cost.

## Full article

daily.dev links to this article rather than hosting it. Read it at the original source: <https://www.philschmid.de/huggingface-bert-aws-inferentia>

---

Tags: [#aws](https://daily.dev/tags/aws), [#bert](https://daily.dev/tags/bert)

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