Run fast, private agents on-device with LFM2.5-24B-A2B
Key points
- 24B total parameters with 2B active per token, built as a reasoning model that thinks before answering.
- Scores 48.09 on LiveClaw versus 38.19 for Qwen3.6-35B-A3B, a model with roughly 50% more parameters.
- Decodes 184 tokens/s on an Apple M5 Max and 114 tokens/s on a Ryzen AI Max+ 395, per the source.
- Open-weight and available on Hugging Face now; supports llama.cpp, MLX, vLLM, SGLang, and ONNX from day one.
Why it matters for marketers: Content teams building tool-using assistants can run a local agent model, but should pair it with retrieval or search for knowledge-heavy tasks.