---
title: "Liquid AI open-sources fast, CPU-friendly text encoders"
url: https://www.parallelquant.com/posts/liquid-ai-open-sources-fast-cpu-friendly-text-encoders-da2d12
source_name: "MarkTechPost"
source_url: https://www.marktechpost.com/2026/07/29/liquid-ai-releases-lfm2-5-encoder-230m-and-lfm2-5-encoder-350m-bidirectional-encoders-that-stay-fast-at-8k-context-on-cpu/
published: 2026-07-29T09:38:42.000Z
topics: ["open source", "llms"]
publisher: "Parallel Quant"
---

# Liquid AI open-sources fast, CPU-friendly text encoders

*2026-07-29 · Source: [MarkTechPost](https://www.marktechpost.com/2026/07/29/liquid-ai-releases-lfm2-5-encoder-230m-and-lfm2-5-encoder-350m-bidirectional-encoders-that-stay-fast-at-8k-context-on-cpu/)*

Liquid AI released two open-weight bidirectional encoder models, LFM2.5-Encoder-230M and LFM2.5-Encoder-350M, both with 8,192-token context built on the LFM2 hybrid backbone. The 350M model ranks fourth of 14 models on a 17-task GLUE/SuperGLUE/multilingual benchmark suite, and the 230M model completes an 8K-token forward pass on CPU in about 28 seconds.

**Why it matters:** Small, CPU-efficient encoders matter for edge and on-device use cases — retrieval, classification, and search often just need a good encoder rather than a full generative model. Competitive benchmark results at this size make it a practical, low-cost option for deployments that can't justify running an LLM.

**Topics:** open source, llms

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Read the original: https://www.marktechpost.com/2026/07/29/liquid-ai-releases-lfm2-5-encoder-230m-and-lfm2-5-encoder-350m-bidirectional-encoders-that-stay-fast-at-8k-context-on-cpu/
Canonical: https://www.parallelquant.com/posts/liquid-ai-open-sources-fast-cpu-friendly-text-encoders-da2d12
Published by Parallel Quant — https://www.parallelquant.com