# Train Qwen3 on AWS Trainium with torchtitan

> **Publication**: aitalky (Independent AI Journalism)
> **Canonical Citation URL**: https://aitalky.vercel.app/news/train-qwen3-on-aws-trainium-with-torchtitan
> **Original Source**: [AWS ML Blog](https://aws.amazon.com/blogs/machine-learning/train-qwen3-on-aws-trainium-with-torchtitan/)
> **Author**: Josh Longenecker (undefined)
> **Published Date**: Wednesday, October 21, 2026
> **Category**: INDUSTRY
> **Tags**: AI, AWS ML Blog, industry

## Executive Summary
Train Qwen3 on AWS Trainium with torchtitan three ways on a single trn2.48xlarge: the Hugging Face Transformers v5 modeling backend, torchtitan's native Qwen3, and NKI mixture-of-experts kernels.



## Full Coverage
Using torchtitan on AWS Trainium means using it the way you always have. TorchNeuron registers Trainium as a native PyTorch device, which lets torchtitan use it through standard PyTorch interfaces. You can use these configurations to pre-train from random initialization or fine-tune published weights. You can also customize the model implementation and kernels for your workload. In this post, you train Qwen3 three ways on a single trn2.48xlarge

The first method uses Transformers v5 as a modeling backend in torchtitan. It runs Hub models through AutoModelForCausalLM and reads and writes published safetensors directly. The second method switches to torchtitan's native Qwen3 by changing config.model_spec . The third adds Neuron Kernel Interface (NKI) mixture-of-experts (MoE) kernels from the torchtitan-neuron recipes and then uses torchtitan's override registry to attach your own kernel. All three configurations use the same torchrun launch pattern and config structure

To follow this walkthrough, use a single trn2.48xlarge with either the AWS Deep Learning AMI for Neuron or a Neuron PyTorch training container. Both environments provide the Neuron driver, runtime, and compiler required to train on Trainium

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