LFM2-VL is a new series of open-weight vision-language models from Liquid AI. Designed for on-device deployment, they offer up to 2x faster inference on GPU and come in 450M and 1.6B parameter sizes.
Liquid AI has been consistently pushing hard on on-device models, and now they're adding multimodal capabilities to the LFM2 series.
LFM2-VL is their latest answer. It's a new family of vision-language models designed for speed, with up to 2x faster inference on GPU compared to existing models.
They've released two versions: a tiny 450M and a more capable 1.6B, which is great for developers building for different device constraints.
Really like how Liquid AI is thinking about real deployment. On-device speed matters way more than benchmarks sometimes. Excited to see the 450M size too, great balance for lightweight apps without losing capability.
This is impressive work. the fact that it's open-weight and designed specifically for devices makes it way more accessible. The smaller model size especially seems practical for edge AI without constant cloud dependence.
Speed is everything when you're running models outside the cloud. Love the Liquid AI focused on GPU inference gains. Two different size options make it flexible for developers with different needs and resources.
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Hi everyone!
Liquid AI has been consistently pushing hard on on-device models, and now they're adding multimodal capabilities to the LFM2 series.
LFM2-VL is their latest answer. It's a new family of vision-language models designed for speed, with up to 2x faster inference on GPU compared to existing models.
They've released two versions: a tiny 450M and a more capable 1.6B, which is great for developers building for different device constraints.
A new generation of hybrid models built for on-device edge AI. Faster, efficient, and optimized for real world application.
Really like how Liquid AI is thinking about real deployment. On-device speed matters way more than benchmarks sometimes. Excited to see the 450M size too, great balance for lightweight apps without losing capability.
This is impressive work. the fact that it's open-weight and designed specifically for devices makes it way more accessible. The smaller model size especially seems practical for edge AI without constant cloud dependence.
Speed is everything when you're running models outside the cloud. Love the Liquid AI focused on GPU inference gains. Two different size options make it flexible for developers with different needs and resources.