The edge-model race usually means shrinking one big model to fit a phone. On October 27, Liquid AI offered a different answer: LFM3, a family of six open-weight models in the 2B class, all branched from a single LFM3-1.7B-Base backbone, covering text, vision, audio, omni, and two decision models. Weights went live on Hugging Face the same day.

One backbone, six models

LFM3-1.7B-Base was pre-trained on 22T tokens; the final 3.5T tokens form a mid-training stage on curated data that extends the context window to 256K. Each family member has a job: LFM3-1.7B (text) targets local agents; LFM3-VL-2B (vision) is the first LFM vision model to take video input, and it reasons step by step before answering; LFM3-Audio-2B supports voice chat and tool calling across 18 languages; LFM3-Omni-2B (2.2B) is the company's first omni model that can see, listen, and talk back. Two decision models, d1-2B and d1-Omni-2B, skip long answers and return calibrated judgments in a single forward pass; d1-2B replaces d1-3B with a third fewer parameters while scoring higher.

A tri-hybrid architecture, over half convolution

The backbone mixes three operators: of 24 layers, 13 are gated short convolutions (kernel size raised from 3 to 7), 5 are sliding-window attention with a 256-token window, and 6 are global attention. Hardware-in-the-loop architecture search expanded from one phone and one laptop for LFM2 to 14 device and backend combinations, including the Galaxy S26 Ultra, Raspberry Pi 5, Apple M4 Max, AMD Ryzen AI, and the Qualcomm Hexagon NPU.

What the numbers say

The agent benchmarks are the strongest part: LFM3-1.7B posts 45.60 on LiveClaw versus 14.70 for LFM2.5-2.6B and 35.77 for Qwen3.5-9B; its table average of 49.37 also beats Qwen3.5-9B's 47.24, with roughly one-sixth the parameters. On the vision side, LFM3-VL-2B averages 71.4 across 62 vision benchmarks, essentially matching Gemma 4 12B (71.7), a model more than six times larger. On-device: roughly 1 GB at 4-bit, decoding 1.8-2.8x faster than the next model across three devices under llama.cpp, and a 128K-token KV cache of just 1.6 GB (versus 4.3 GB for Qwen3.5-4B). On a single NVIDIA B300 with SGLang it can produce 3.6 billion tokens per day; the team even ran more than 300,000 parallel agents through the Enron email archive.

Two caveats

First, these scores come from the official launch post; independent replication will take time, and outside coverage so far mostly cites the announcement. Second, Liquid AI itself admits larger models still win on agentic coding and knowledge: GPQA Diamond 63.37 versus 80.40 for Qwen3.5-9B is a clear gap. So the LFM3 bet is not "small models win across the board" but specialized intelligence: a 2B-class model is small enough to fine-tune into a domain expert on a single A100 in hours, then run on-device indefinitely. MacPaw's test supports the thesis: across 65 real macOS agent tasks, it beat Qwen3.5-2B with 64% versus 48% task success while using 58% fewer tokens.

While cloud players chase trillion-parameter scale, Liquid AI moved the battlefield to a few small experts living in every device. The question for builders: does your next app need one generalist, or six specialists?

References: LFM3 launch post | AI Weekly coverage