Liquid AI Releases LFM2.5-VL-3B: A 3B Vision-Language Model That Reads Screens, Grounds Objects, and Calls Tools On-Device

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Yesterday, Liquid AI released LFM2.5-VL-3B. It is a 3.1B-parameter vision-language model built for on-device deployment. The model reads digital screens across mobile, web, and desktop. It grounds objects to coordinates, parses documents and charts, and calls tools from text or image input. Liquid AI reports an average of 69.4 across 28 vision benchmarks. That matches InternVL-3.5-4B and sits 0.7 points behind Qwen3.5-4B, both 4.7B models. The model is non-reasoning, so it answers directly and keeps latency low. It fits in roughly 3 GB of memory and decodes 228 tokens/s on an Apple M5 Max.

Is it deployable?

Yes, the checkpoint ships in four formats: native, GGUF, ONNX, and MLX. Day-one runtimes include llama.cpp, MLX, vLLM, SGLang, and ONNX. It fits in roughly 3 GB of memory.

Which company levels: The LFM Open License v1.0 is Apache-2.0-based with one change: free commercial use ends once a company’s annual revenue reaches $10M USD. So indie developers, startups, and SMBs under that line can ship commercially at no cost. Enterprises above it must negotiate a commercial license with Liquid AI. Research, education, and non-profit use carry no revenue limit.

Industries: Consumer electronics, automotive, industrial and robotics, financial services, healthcare, and e-commerce. Also QA and RPA vendors that automate GUIs.

Applications: On-device screen agents, GUI test automation, PDF-to-structured-text with layout labels, invoice and receipt OCR, near-real-time object detection in vehicles, offline translation of menus and road signs, and multi-image comparison.

So, What …

Vollständiger Originalartikel ↗

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• Originalquelle / Hersteller-Advisory lesen: https://www.marktechpost.com/2026/08/13/liquid-ai-lfm2-5-vl-3b-on-device-vision-language-model/
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