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TOPIC · ENTITY #21192

trn2.48xlarge

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2

CODING · 1 SOURCE · AWS Machine Learning

Train Qwen3 on AWS Trainium using torchtitan

A torchtitan walkthrough demonstrates three ways to train Qwen3 on a single AWS Trainium trn2.48xlarge: (1) via Transformers v5 backend (AutoModelForCausalLM) reading/writing Hugging Face safetensors; (2) using torchtitan’s native Qwen3Model via config.model_spec; and (3) adding NKI (Neuron Kernel Interface) mixture-of-experts kernels from torchtitan-neuron and attaching custom kernels with the override registry. The post covers environment setup (Neuron PyTorch container or AWS DL AMI), installing torchtitan-neuron, torchrun launch patterns, checkpoint loading/saving (safetensors), and reports a verified step-1 loss example for Qwen3-4B-Instruct-2507 (6.54 from published weights vs 12.43 from random init).

5.0

MODELS · 1 SOURCE · AWS Machine Learning

i14 trains and serves Laplace 3B linear-attention model on AWS Trainium2 with TorchNeuron Native

i14 ported its Laplace 3B 3B-parameter linear-attention decoder (using Gated Linear Attention and a fixed 29.5 MB recurrent state) to AWS Trainium2 via TorchNeuron Native and native PyTorch operators. On a trn2.48xlarge instance the team reported ~120,000 training tokens/sec and serving throughput of 11,544 output tokens/sec across 1,280 concurrent sequences after replacing a Triton kernel, optimizing MuonFSDP2 collectives, and statically unrolling the GLA recurrence for the Trainium2 compiler.

7.0