# Beyond the KV Cache: Training and Serving I14's Laplace 3B Linear Attention Model on AWS Trainium2 with Native PyTorch

> **Platform:** [SPIDITS AI](https://spidits.com/) — Real-Time AI News & Market Intelligence  
> **Published:** 2026-10-20T21:14:10.000Z  
> **Category:** RESEARCH  
> **Impact Score:** 210/100  
> **Primary Source:** [AWS ML Blog](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)  
> **Canonical Citation:** [https://spidits.com/timeline/beyond-the-kv-cache-training-and-serving-i14-s-laplace-3b-linear-attention](https://spidits.com/timeline/beyond-the-kv-cache-training-and-serving-i14-s-laplace-3b-linear-attention)

## Executive Summary
Learn how i14 trained and served Laplace 3B, a linear attention model, on AWS Trainium2 using native PyTorch.

## Why It Matters (Strategic Analysis)
Replacing quadratic KV cache scaling with a fixed-size recurrent state removes context length bottlenecks for long-running agentic workloads and high-concurrency document processing applications. Utilizing native PyTorch on custom AWS Trainium2 hardware bypasses GPU-dependent Triton kernels to validate alternative non-transformer inference architectures at scale.

## Referenced Coverage & Sources
- **[AWS ML Blog](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)**: 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..._

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