The liquid ai lfm 3 release landed on 27 October, and it is one of the more practical open-model launches for small teams: a 1.7B-parameter text model that Liquid AI says fits in about 1 GB of memory at 4-bit precision. This research-based post covers what was released, where it is strong and weak according to Liquid AI's own numbers, and how to try it locally without paying for an API.

What Liquid AI released

According to the Liquid AI LFM3 announcement , the first LFM3 release is a family of six open-weight models in the 2B range, all built on one shared backbone:

LFM3-1.7B : a text model for local agents, with a 256K-token context window

LFM3-VL-2B : vision, now including video input

LFM3-Audio-2B : speech in and out, 18 languages

LFM3-Omni-2B : vision, audio and text together

d1-2B and d1-Omni-2B : "decision" models that answer a list of questions about a ticket, invoice, photo or voice note with probabilities, instead of writing free text

The weights are on Hugging Face under the LFM Open…