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i14 migrated its custom architecture to AWS Trainium2. According to information released by AWS Machine Learning on October 21, 2026, i14 collaborated with AWS to train and deploy the Gated Linear Attention-based Laplace 3B model on AWS Trainium2 using TorchNeuron Native. Training throughput on a single trn2.48xlarge is approximately 120,000 tokens/s; the model's recurrent state is fixed at 29.5 MB per sequence and does not grow with context length; inference reaches 11,544 output tokens/s at 1,280 concurrent sequences. The team also reworked Muon's all_to_all communication, reducing per-card communication volume by 24x and increasing training throughput by 1.63x. There are currently no subsequent developments.
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i14 Trains and Deploys the Laplace 3B Linear Attention Model on AWS Trainium2 Using Native PyTorchDevelopments
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- Source:AWS Machine Learning
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Oct 21
AWS Machine LearningFirst-party
i14 Trains and Deploys the Laplace 3B Linear Attention Model on AWS Trainium2 Using Native PyTorch
i14 partnered with AWS to train and deploy Laplace 3B, based on Gated Linear Attention, on AWS Trainium2 using TorchNeuron Native. A single trn2.48xlarge achieves a training throughput of about 120,000 tokens/s. The model's recurrent state per sequence is fixed at 29.5 MB and does not grow with context length; inference reaches 11,544 output tokens/s at 1,280 concurrent sequences. The team also reworked Muon's all_to_all communication to cut per-GPU communication volume by 24x and raise training throughput by 1.63x.