ModelsModel releaseLarge Language Models · Efficiency & Inference · Hardware & Compute1 source · Oct 20, 2026

Beyond the KV cache: Training and serving i14’s Laplace 3B linear attention model on AWS Trainium2 with native PyTorch

Most large language models (LLMs) use transformer architectures with softmax attention.

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During inference, their key-value (KV) cache grows with the conversation or document context.
i14 designed Laplace 3B as a linear attention alternative.
Working with AWS, i14 used native PyTorch support through TorchNeuron Native to bring this custom architecture to AWS Trainium2.
On one trn2.48xlarge instance, Laplace 3B trained at about 120,000 tokens per second.

[1]Beyond the KV cache: Training and serving i14’s Laplace 3B linear attention model on AWS Trainium2 with native PyTorch
AWS Machine Learning Blog · Oct 20, 09:14 PM
Most large language models (LLMs) use transformer architectures with softmax attention.
During inference, their key-value (KV) cache grows with the conversation or document context.

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