Liquid AI announced the release of LFM2-VL, a vision-language foundation series built for rapid, on-device inference. Two configurations are offered: a 450-million-parameter edition for resource-constrained environments and a 1.6-billion-parameter version that balances higher capacity with a compact form factor for smartphones, laptops, wearables, or embedded setups. This approach addresses growing demand for local processing, critical for privacy and minimal network delays.
Rigorous tests show LFM2-VL can complete inference tasks on a GPU at up to twice the speed of current models while matching benchmark scores on image captioning, visual question answering, and mixed reasoning challenges.
Its design splits into a modular stack combining an LFM2 language core (available in either 1.2B or 350M parameters), a SigLIP2 NaFlex visual encoder (400M or 86M parameters), and a multimodal projection layer that uses pixel rearrangement to shrink image token counts and speed up processing.
Images up to 512×512 pixels feed through at native resolution. Larger visuals get divided into nonoverlapping 512×512 tiles to conserve proportions. The 1.6B setup includes a reduced-size overview of the full image so global context is captured.
Developers can specify image token caps and tile limits at inference time to trade off detail against speed based on hardware capability.
Both variants started with pretraining on the LFM2 language backbone, then moved through a joint mid-training phase that blended text and image data in shifting ratios, and finished with fine-tuning on about 100 billion multimodal tokens for improved visual understanding.
On public tests such as RealWorldQA, MM-IFEval, and OCRBench, LFM2-VL holds pace with larger systems like InternVL3 and SmolVLM2 while cutting memory use and inference time—making it a fit for edge and mobile projects.
Both model sizes are available as open-weight releases on Hugging Face under an Apache-2.0-style license, free for research or commercial integrations. Companies needing enterprise terms can discuss options with Liquid AI. The models plug into the Hugging Face Transformers library and support quantization to shrink size or boost speed on specialized hardware.
The on-device focus lets teams build features such as live image caption generation, context-aware visual search, and interactive chat clients that handle pictures, all without constant server calls. Use cases range from robotics and IoT sensors to smart camera feeds and next-level mobile assistants.
Download links appear now in Liquid AI’s Hugging Face collection, and sample scripts show how to run inference with platforms like llama.cpp using various quantization tiers.
For further customization and deployment across different targets, the models integrate with Liquid AI’s LEAP platform, letting teams push updates to multiple on-device environments. By combining LEAP’s orchestration tools with LFM2-VL’s lightweight footprint, teams can deploy updates to hundreds of devices in the field with manual intervention kept to a minimum.
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