AINewsnow

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.

Read the full story at AWS Machine Learning Blog ↗

Timeline · 1 report

  1. 2026-10-20 21:14 · AWS Machine Learning Blog
    Beyond the KV cache: Training and serving i14’s Laplace 3B linear attention model on AWS Trainium2 with native PyTorch

More stories

  1. Build a semantic layer for agentic AI on AWS with AtScale and Amazon Bedrock AgentCore — AWS Machine Learning Blog
  2. Structured outputs for Anthropic Claude models on Amazon Bedrock — AWS Machine Learning Blog
  3. Work management context for AI agents with Smartsheet MCP on AWS — AWS Machine Learning Blog
  4. Strengthening AI safety evaluation through independent third-party review — Amazon Science
  5. From data to decisions: Conversational supply chain intelligence with Amazon Quick and Amazon Bedrock AgentCore — AWS Machine Learning Blog
  6. Fine-tuning GPT-OSS-20B for multilingual reasoning on Amazon SageMaker HyperPod — AWS Machine Learning Blog
  7. How Octave built a secure multi-tenant AI foundation on Amazon Bedrock — AWS Machine Learning Blog
  8. Discover tools at runtime with AWS Agent Registry — AWS Machine Learning Blog

Get the daily brief of stories like this at 6:30 every morning →