# LiquidAI releases LFM2.5-24B-A2B reasoning model for on-device agents

> **Key takeaway:** LFM2.5-24B-A2B is an open-weight Mixture-of-Experts model that uses only 2B active parameters to provide fast, private reasoning on local hardware.

**Published:** 2026-10-13T17:05:48+00:00 
**Source:** Hugging Face Blog & Announcements 
**Category:** ai-open-models 
**Canonical URL:** https://fosswire.org/news/liquidai-releases-lfm25-24b-a2b-reasoning-model-for-on-devic.html 

## Summary
LiquidAI has introduced LFM2.5-24B-A2B, a 24B-parameter reasoning model designed for efficient on-device agentic tasks. Despite its 24B total parameters, it only activates 2B parameters per token, allowing for high-speed inference on consumer hardware like Apple M5 Max and NVIDIA GPUs. The model features an expanded 131K context window and is optimized for tool use and instruction following.

## Technical specifications of LFM2.5-24B-A2B
- **Architecture**: 40 layers with 64 experts per MoE block using top-4 routing.
- **Efficiency**: Only 2B active parameters per token, enabling speeds up to 184 tok/s on Apple M5 Max.
- **Context & Vocabulary**: Context window increased to 131K tokens; vocabulary expanded to 128K tokens.
- **Training**: Pre-trained on ~33T tokens with specialized mid-training for reasoning, math, and code.
- **Compatibility**: Supports llama.cpp, MLX, vLLM, SGLang, and ONNX.

## Why it matters
The model provides a high-performance, open-weight alternative for developers building private, local AI agents that require tool-calling capabilities without massive hardware requirements.