Liquid AI Releases LFM2.5-24B-A2B: A 2B-Active MoE for On-Device Agents

Liquid AI has released LFM2.5-24B-A2B, a 24-billion-parameter Mixture-of-Experts model with only 2 billion parameters active per forward pass, specifically designed for on-device agentic workloads. The MoE architecture allows the model to deliver performance closer to a full 24B model while keeping per-inference compute costs near that of a 2B model, making it viable for deployment on consumer hardware and edge devices. This is directly relevant for developers building autonomous agents that need to run locally — whether for privacy reasons, latency requirements, or offline use cases. Liquid AI's focus on liquid neural network foundations continues to differentiate their architecture from standard transformer-based small models. The release joins a growing class of efficient edge models but stands out for its explicit agentic positioning and the MoE efficiency trade-off.