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i14's Laplace 3B linear attention model trained and served on AWS Trainium2

By PulseAugur Editorial · [1 sources] ·

i14 has developed Laplace 3B, a linear attention model that maintains a fixed-size recurrent state, unlike traditional transformer models whose key-value cache grows with context length. This innovation allows for more efficient handling of long conversations or documents, benefiting applications like AI agents. i14 collaborated with AWS to adapt Laplace 3B for AWS Trainium2, utilizing native PyTorch support via TorchNeuron Native for its Gated Linear Attention (GLA) design and Muon optimizer. The adapted model achieved significant performance metrics, training at approximately 120,000 tokens per second and serving 11,544 output tokens per second across 1,280 concurrent sequences on a single instance. AI

IMPACT Enables more efficient handling of long contexts for AI agents and applications requiring persistent memory.

RANK_REASON The item details the training and serving of a novel AI model architecture (Laplace 3B) with specific performance metrics, fitting the research category. [lever_c_demoted from research: ic=1 ai=1.0]

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AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

i14's Laplace 3B linear attention model trained and served on AWS Trainium2

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The item details the training and serving of a novel AI model architecture (Laplace 3B) with specific performance metrics, fitting the research category. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. AWS Machine Learning Blog TIER_1 English(EN) · Kevin Shabahang ·

    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 memor…

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