Train Qwen3 on AWS Trainium with torchtitan
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This article demonstrates how to train the Qwen3 model on AWS Trainium hardware using the torchtitan framework. The authors outline three distinct configuration methods: utilizing Hugging Face Transformers v5 as a backend, employing torchtitan’s native Qwen3 implementation, and integrating Neuron Kernel Interface (NKI) kernels for mixture-of-experts models. All approaches run on a single trn2.48xlarge instance with 64 logical NeuronCores, leveraging standard PyTorch interfaces via TorchNeuron. The guide details specific prerequisites, including required software versions and environment setup using the AWS Deep Learning AMI. It provides code examples for fine-tuning from published weights, customizing attention mechanisms, and attaching user-defined NKI kernels through an override registry. The walkthrough emphasizes verifying training stability by comparing loss metrics and ensuring checkpoint compatibility with Hugging Face formats. Finally, it explains parallelism strategies such as data parallel sharding and tensor parallelism to optimize resource utilization across the accelerators.
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