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Liquid AI announced LFM2.5-Embedding-350M and LFM2.5-ColBERT-350M — two…

2026-06-19 · 03:29 UTC ·@ivanfioravanti ·0 min read

Brief

Liquid AI released two 350M-parameter retrieval models, LFM2.5-Embedding-350M and LFM2.5-ColBERT-350M, claiming end-to-end retrieval latency down to 1.5 ms and 'best-in-class' multilingual/cross-lingual performance across 11 languages. @ivanfioravanti responded with interest in testing the models specifically for a local search project.

Why it matters

Liquid AI announced LFM2.5-Embedding-350M and LFM2.5-ColBERT-350M — two multilingual retrieval models (350M) aimed at fast, accurate search.

Key details

  • They claim end-to-end retrieval latency as low as 1.5 ms on their enterprise stack and 'best-in-class' multilingual/cross-lingual performance across 11 languages: Arabic, German, English, Spanish, French, Italian, Japanese, Korean, Norwegian, Portuguese, and Swedish.
  • Author @ivanfioravanti reacted: 'Best in class! I need to try this for local search in a project…', signalling interest in applying the models for local search use cases.
Source evidence

Best in class! I need to try this for local search in a project…

Liquid AI (@liquidai)

Introducing LFM2.5-Embedding-350M and LFM2.5-ColBERT-350M: two multilingual retrieval models built for ultra-fast and accurate search across 11 languages.

> End-to-end retrieval latency as low as 1.5ms with our enterprise stack! 🚀

> Consistently best-in-class multilingual and cross-lingual performance across Arabic, German, English, Spanish, French, Italian, Japanese, Korean, Norwegian, Portuguese, and Swedish.

🧵

— https://nitter.net/liquidai/status/2067610173024219225#m