{"id":"03833914-f50e-4c53-b3d9-0e02b647265a","url":"https://api.tumn.com/news/03833914-f50e-4c53-b3d9-0e02b647265a","title":"Beyond the KV cache: Training and serving i14’s Laplace 3B linear attention model on AWS Trainium2 with native PyTorch","summary":"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 memor","sections":[],"faq":[],"sources":[{"url":"https://aws.amazon.com/blogs/machine-learning/beyond-the-kv-cache-training-and-serving-i14s-laplace-3b-linear-attention-model-on-aws-trainium2-with-native-pytorch/","title":"AWS Machine Learning Blog","publishedAt":"2026-10-20T21:14:10.000Z"}],"author":"TUMN 큐레이션","aiAssisted":false,"reviewStatus":"automated","reviewNote":"원문 제목과 공개된 발췌를 인용하고 원문으로 연결합니다. 내용을 재작성하거나 요약하지 않았습니다.","publishedAt":"2026-10-20T21:14:10.000Z","modifiedAt":"2026-10-21T01:25:36.000Z","checkedAt":"2026-10-21T01:25:36.883Z","language":"ko","kind":"link","sourceLabel":"AI 공식","profile":null,"via":null,"formats":{"json":"https://api.tumn.com/news/03833914-f50e-4c53-b3d9-0e02b647265a.json","markdown":"https://api.tumn.com/news/03833914-f50e-4c53-b3d9-0e02b647265a.md"}}