Liquid AI Releases LFM2.5-24B-A2B: An Open Model for AI Agents That Runs on a Laptop

⚡ The gist in 5 seconds - Liquid AI released LFM2.5-24B-A2B — an open reasoning model aimed at AI agent tasks. - The model is claimed to run locally on a laptop and outperform competitors three times its size. - Full benchmarks, hardware requirements (VRAM, quantization), and license terms are not disclosed in the post. ### 🔍 What was found Liquid AI has introduced the LFM2.5-24B-A2B model. According to the description, it is an open reasoning model for AI agents: it is designed to run locally on a laptop without relying on server-side inference. The key claim is superiority over competitors three times larger on specialized tasks. The exact list of these tasks is cut off in the source, and no full benchmark list or figures are provided. Implementation details are absent from the post: there is no data on the number of active parameters, supported formats and quantizations, or the minimum amount of video memory required. The "24B-A2B" index in the name suggests a large model, but Liquid AI did not disclose the parameter breakdown in this announcement. It is also unclear where the weights are hosted, under what license they are released, and which runtime (llama.cpp, vLLM, or a proprietary one) is used to run the model locally. ### 💡 Why it matters The bet on local inference is the main trend in open models: if Liquid AI's claim is confirmed by independent measurements, it will raise the bar for models in the "three times larger" class and give agentic use cases an option that doesn't require cloud compute. For now, this is an announcement without published testing methodologies, so the comparison with "competitors three times larger" should be treated as a vendor claim until third-party runs appear.