LFM2.5-24B-A2B is presented for private on-device agents
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LFM2.5-24B-A2B is presented as a reasoning model for agents that run on the device, call tools and handle multi-step tasks. Although it has 24 billion parameters, only 2 billion are active per token, reducing the cost of each reasoning step. The material reports 184 tokens per second on an Apple M5 Max and up to 21K output tokens per second on a single NVIDIA B300 GPU.
In evaluations across 16 benchmarks, the model scored 48.09 on LiveClaw, versus 38.19 for Qwen3.6-35B-A3B, and led or matched the competition on five of eight tool-use and agentic tests. It also led all three instruction-following benchmarks, while larger models performed better on knowledge and coding tasks. The model retains a mixture-of-experts architecture, an expanded context window and a larger-vocabulary tokenizer intended to improve non-Latin text tokenization.
Why it matters · editorial interpretation
The model was presented for private agents running on the device, using tools and handling multi-step tasks. Its architecture activates only 2 billion of its 24 billion parameters per token, which could reduce the cost of each reasoning step, although evaluations point to stronger advantages in tool use and instruction following than in knowledge and coding.
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