Liquid AI upgrades its 24B-parameter local model: only 2B parameters are activated, and it outperforms the larger Qwen model in agent tests.

Beating AI Express: Leading edge-side AI R&D firm Liquid AI has launched LFM2.5-24B-A2B, a model focused on enhancing local agent inference and tool calling capabilities. The previous LFM2-24B-A2B generation prioritized operational efficiency; the new version retains a total of 24 billion parameters with only around 2 billion activated per run, while its pre-training data has grown from 17 trillion to 33 trillion tokens, and its context window expanded from 32K to 131K. The new model performs in

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