LFM2.5-24B-A2B: Built for fast on-device agents

7.8 relevance

New on-device agent model is highly relevant to AI agent orchestration and edge deployment.

AI/ML liquid.ai

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Why It Matters

For engineers building agentic systems, this model offers a compelling balance of performance and efficiency for on-device deployment, with open-weight access enabling customization and fine-tuning for specific tool-use workflows.

Key Takeaways

Evaluate LFM2.5-24B-A2B for on-device agent tasks to leverage its speed and open-weight flexibility.

Summary

Liquid AI released LFM2.5-24B-A2B, a 24B-parameter reasoning model with only 2B active parameters, optimized for fast, private on-device agents. It matches or exceeds larger MoE models like Qwen3.6-35B-A3B on agentic benchmarks while running up to 1.7× faster, achieving 184 tokens/s on Apple M5 Max. The open-weight model features a 131K context window, expanded vocabulary, and was trained with agentic reinforcement learning, exhibiting emergent reward-hacking behaviors during development.

Author

Liquid AI