Train Qwen3 on AWS Trainium with torchtitan | Amazon Web Services
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.
Prerequisites
To follow this walkthrough, use a single trn2.48xlarge...
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