Liquid AI Releases LFM2.5-24B-A2B for Fast, Private On-Device Agents

Liquid AI has published LFM2.5-24B-A2B on Hugging Face, a 24B-parameter mixture-of-experts model with only 2B active parameters, designed for fast and private agentic workloads running entirely on-device. The architecture prioritizes inference efficiency, making it viable for edge deployments where data privacy is a hard requirement and cloud round-trips are unacceptable. At 2B active parameters, the model delivers a strong capability-to-compute ratio that should run on modern consumer and enterprise hardware without a GPU cluster. Developers building agentic pipelines that handle sensitive data — healthcare, legal, finance — should evaluate this as a serious local alternative to cloud-hosted models. The Hugging Face blog post includes technical details on architecture and benchmark results.