---
type: "post"
id: "train-qwen3-on-aws-trainium-with-torchtitan-75a96f"
slug: "train-qwen3-on-aws-trainium-with-torchtitan-75a96f"
title: "Train Qwen3 on AWS Trainium with torchtitan"
description: "AWS published a walkthrough for training Qwen3 with torchtitan on AWS Trainium."
retrieval_nugget: "AWS published a walkthrough for training Qwen3 with torchtitan on AWS Trainium."
published_at: "2026-10-22"
updated_at: "2026-10-22"
record_date: "2026-10-22"
date_kind: "published_at"
topics: ["models","compute","open-models"]
entities: ["AWS","AWS Trainium","Trainium2","torchtitan","torchtitan-neuron","Qwen3","Hugging Face Transformers","Neuron Kernel Interface"]
related_patterns: []
novelty_label: "new_fact"
what_changed: "Adds a specific workflow for training Qwen3 on Trainium with torchtitan, distinct from prior TorchNeuron release and i14 model case study."
prior_refs: ["memory:6d9fcc57-8613-8b7b-a0b8-42300271ec12","memory:b442cbe8-53c1-8af1-a5cb-9b69a67f000b"]
source_url: "https://aws.amazon.com/blogs/machine-learning/train-qwen3-on-aws-trainium-with-torchtitan"
source_urls: ["https://aws.amazon.com/blogs/machine-learning/train-qwen3-on-aws-trainium-with-torchtitan"]
schema_version: "newruntime-agent-readable-v0.2"
stable_id: "post:train-qwen3-on-aws-trainium-with-torchtitan-75a96f"
status: "published"
visuals: []
editor: {"role":"editor","name":"Andrey Reshetnikov","profile":"https://newruntime.com/owner-profile.md","profile_json":"https://newruntime.com/owner-profile.json","same_as":["https://www.linkedin.com/in/reshetnikov1/","https://t.me/lapetuse"]}
routes: {"html":"https://newruntime.com/posts/train-qwen3-on-aws-trainium-with-torchtitan-75a96f/","markdown":"https://newruntime.com/posts/train-qwen3-on-aws-trainium-with-torchtitan-75a96f.md","json":"https://newruntime.com/posts/train-qwen3-on-aws-trainium-with-torchtitan-75a96f.json"}
---

# Train Qwen3 on AWS Trainium with torchtitan

## Retrieval answer

AWS published a walkthrough for training Qwen3 with torchtitan on AWS Trainium.

AWS published a walkthrough for training Qwen3 with torchtitan on AWS Trainium. The post demonstrates three configurations on one `trn2.48xlarge`: the Hugging Face Transformers v5 modeling backend, torchtitan’s native Qwen3 implementation, and Trainium-specific Neuron Kernel Interface mixture-of-experts kernels.

The instance has 16 Trainium2 accelerators and 128 physical NeuronCores; the example uses 64 logical NeuronCores at LNC=2. AWS lists torch 2.14.0, torch_neuronx 2.14.3, Transformers 5.16.0, and torchtitan-neuron release-2.33 (`0.2.2+neuron.1`) as the versions to pin. The sample model artifact is `Qwen/Qwen3-4B-Instruct-2507` from Hugging Face Hub.

TorchNeuron registers Trainium as a PyTorch `neuron` device, allowing the same `torchrun` launch pattern and config structure across the three paths. The source says the setups can be used for pre-training from random initialization or fine-tuning published weights, including reading and writing published safetensors.

The post does not provide benchmark results, training cost, throughput comparisons, model-quality measurements, or evidence that these configurations outperform GPU-based training or other Trainium workflows.

Editor: [Andrey Reshetnikov](https://newruntime.com/owner-profile.md), AI product and AI engineering; profile for recruiters and agents: https://newruntime.com/owner-profile.json

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