Liquid AI has unveiled LFM3, the new generation of its Liquid Foundation Models built for specialized intelligence. The family starts with six models of roughly 2 billion parameters, all derived from one shared base, LFM3-1.7B-Base. Four cover a single modality each (text, vision, audio and omni), while the last two are decision models derived from the vision and omni models. Rather than running one large general-purpose model, developers can choose the one that fits their use case, customize it and run it on-device. Base and post-trained weights are available as open-weight models on Hugging Face and Liquid AI's platform.
A hybrid architecture built for agents
LFM2 had shown that a mix of short convolutions and attention could run fast on-device without losing quality. For LFM3, the company redesigned that architecture for agents, which need more capability per parameter and quick responses over long contexts. The testing also widened: where LFM2 was tried on one phone and one laptop, LFM3 was measured across 14 combinations of devices and backends, including smartphones, a Raspberry Pi 5, laptops with Apple and AMD chips, cloud CPUs and GPUs.
Image: LiquidAI — https://huggingface.co/LiquidAI/LFM3-Audio-2B
Short convolutions still make up most layers, but some were replaced with sliding-window attention blocks, which the company found gave the best balance between quality and speed. According to its data, at equal parameter count LFM3-1.7B matches LFM2-1.2B on short prompts and, with 8,000 tokens of context, is about 25% faster.
Pre-training and a longer context
The base model was trained on 22 trillion tokens, according to the company. Compared with LFM2, the mix includes more scientific papers, repository-level code and considerably more math. The final 3.5 trillion tokens are a mid-training stage on curated data, with more agentic traces and tool calls, which extends the context window to 256,000 tokens.
From text to omni: what the six models are for
The text model targets local agents: according to the company, it can sort an inbox, manage a calendar, research topics on the web and send morning briefings on WhatsApp, picking the right tool among thousands. It is trained to run in agent harnesses such as Hermes Agent, Pi and OpenClaw. Adapting it to a specific domain takes, Liquid AI says, a short fine-tuning run on a single GPU.
In tests published by the company, LFM3-1.7B outperforms LFM2.5-2.6B and compares with similar-sized models such as Gemma 4 E4B and Qwen3.5 4B. On agentic benchmarks such as BrowseComp-Plus and WebArena-Verified, the 1.7-billion-parameter model scores above larger rivals. These are the producer's own results and still need independent checks.
AMD has worked with Liquid AI across several model generations and sees LFM3 as an option for Ryzen AI processors. Ambarella is already enabling the family on its edge AI platform, and Qualcomm plans its use on Snapdragon chips. In a demo, a user in the r/LocalLLaMA community showed real-time video captioning with LFM3-VL-2B running in the browser through WebGPU.
What changes for developers
Open weights and a compact size point to a different use from cloud APIs: the model stays on the device, runs on modest hardware and can be tuned for one precise task. How well the declared results hold on real workloads, especially long agentic tests where performance depends heavily on the runtime, remains to be seen.
No comments yet. Be the first!