Liquid AI
Device-native foundation models.
327 followers
Device-native foundation models.
327 followers
Advanced intelligence for processors outside of data centers.
Built for the latency, privacy, and hardware constraints of the physical world.
This is the 6th launch from Liquid AI. View more
LFM3 by Liquid AI
Launched this week
LFM3 is Liquid AI’s third generation of Liquid Foundation Models, with open weights and a shared backbone across text, vision, audio, omni and decision models. The first release is six 2B-class models, including a 1.7B text model that fits in about 1 GB at 4-bit and has a 256K context. Liquid says they run on phones, laptops and edge devices and can be fine-tuned on one GPU. Weights are on Hugging Face.
Launch tags:Artificial Intelligence
Launch Team
Framer AI AgentsDesign and publish professional sites with AI
Promoted
The hardware-in-the-loop search is thoughtful engineering - testing across Pi, Qualcomm NPU, laptops, phones. That's how you find real tradeoffs. On-device inference at 1.8-2.8x speed-up matters for latency-critical tasks. The KV cache story (1.6GB for 128K) is the real constraint - that's tight enough for voice agents and real-time systems. How's the fine-tuning community adoption looking so far?