Two models from Liquid AI's new LFM3 family can now run entirely on a phone, with no internet connection and nothing sent to an external server. The credit goes to Private Mind, an open source app for iOS and Android that executes language models directly on the device.
The first model is LFM3-1.7B, built for text. The second is LFM3-VL-2B, which adds reading images. In the demo video, the model reads a Japanese ramen menu: asked what to order without spice, it skips the bowl marked as extra spicy and suggests the miso ramen; asked whether it takes card payments, it spots the note that only cash is accepted under the prices.
The figures claimed on an iPhone 16 Pro
Image: LiquidAI — https://huggingface.co/LiquidAI/LFM3-VL-2B
According to the author of the Reddit post, on an iPhone 16 Pro the text model LFM3-1.7B, quantized to 4 bits with MLX and run on the GPU, produces about 40 tokens per second and returns the first token in under half a second. For LFM3-VL-2B, with a photo as input, the first token arrives in about 9 seconds and generation runs at about 20 tokens per second. A follow-up text question in the same chat starts answering in about 5 seconds. These numbers have not been independently verified.
Why the conversion was not trivial
The post also argues that LFM3 is not simply a larger LFM2. That is why the existing LFM2 support in ExecuTorch, the PyTorch runtime for mobile devices, does not cover it. According to the description, the architecture alternates 24 layers of different kinds: gated short convolutions with width 7, full attention and sliding-window attention. Exporting it required new components, which the author says are part of the released code.
Where the family fits
LFM3 includes six models of about 2 billion parameters, all derived from the same base, LFM3-1.7B-Base. Four cover one modality each (text, vision, audio and omni), while two are decision models derived from the vision and omni ones. The weights are available on Hugging Face. This edge work connects to LFM2.5-VL-3B, already covered by AlexTech, and to the on-device runs described in this article.
A demo in the browser
Another demonstration of the same family shows real-time video description with LFM3-VL-2B running in the browser through WebGPU, with a demo published on Hugging Face. The project drew 139 votes and 21 comments on the r/LocalLLaMA community.
According to the official documentation, the VL model processes text, images and video at native resolution, pairing the LFM3 language backbone with a new in-house hybrid convolution and attention vision encoder. The model cards position it for documents, charts, app screens and questions about video.
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