According to DeepLearning.AI on X (formerly Twitter), Liquid AI released LFM2.5-1.2B-Thinking, a 1.17-billion-parameter reasoning model that runs in under 900 MB of RAM and operates about twice as fast as similar models, with full details reported in The Batch. As reported by DeepLearning.AI, the model targets small devices and performs competitively on reasoning benchmarks, enabling on-device agents to orchestrate tools, extract data, and execute local workflows without cloud compute. According to The Batch via DeepLearning.AI, this positions LFM2.5-1.2B-Thinking for edge AI use cases like offline copilots, privacy-preserving data extraction, and low-latency automation, opening cost-efficient deployment paths for enterprises that need reliable reasoning on constrained hardware.