Training and deploying Laplace 3B model on AWS Trainium2 with PyTorch
In brief
- Laplace 3B uses linear attention with a fixed recurrent state of 29.5 MB per sequence.
- Deployed on AWS Trainium2 with native PyTorch, no external dependencies.
- Avoids expanding transformer key-value caches, reducing serving memory overhead.
i14 announces it has trained and deployed Laplace 3B, a linear attention model, on AWS Trainium2 using native PyTorch. The model's recurrent state remains fixed at 29.5 MB per sequence, regardless of context length, avoiding the growing key-value cache of transformer models and ensuring predictable serving memory.
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