Beyond the KV cache: Training and serving i14’s Laplace 3B linear attention model on AWS Trainium2 with native PyTorch
Learn how i14 trained and served Laplace 3B, a linear attention model, on AWS Trainium2 using native PyTorch. Its recurrent state stays fixed at 29.5 MB per sequence regardless of context length, avoiding the growing key-value cache of transformer models and keeping serving memory predictable.
Se muestra el contenido original; la traduccion localizada aun no esta disponible.
Qué ocurrió
Learn how i14 trained and served Laplace 3B, a linear attention model, on AWS Trainium2 using native PyTorch. Its recurrent state stays fixed at 29.5 MB per sequence regardless of context length, avoiding the growing key-value cache of transformer models and keeping serving memory predictable.
Por que importa
The development may change operating conditions or market expectations around Amazon. Further confirmation and measurable outcomes matter.
Entidades afectadas
Ver evidencia
1 articulos · 1 informe original · 1 independientes
- AWS Machine Learning BlogFuente primaria · Respalda · EN · 100%Beyond the KV cache: Training and serving i14’s Laplace 3B linear attention model on AWS Trainium2 with native PyTorch ↗
Afirmaciones
- Beyond the KV cache: Training and serving i14’s Laplace 3B linear attention model on AWS Trainium2 with native PyTorch Observado
Conflictos
No se detectaron conflictos importantes en la evidencia disponible.
Cronología
- Primera publicación
Movimiento del mercado posterior al evento
La reacción del mercado aún no está disponible para este activo y periodo.