LFM2.5-24B-A2B: Liquid AI's on-device agent model wakes just 2B parameters per token

A mixture-of-experts model (only a few expert blocks fire for each token) with 24B total parameters and about 2B active per token. It's text-only and reasoning-only, with a 131K-token context, and it's the largest and last model in the LFM2.5 family. Weights are on Hugging Face and run on GGUF, MLX, vLLM, SGLang and ONNX. With llama.cpp you can start it in one line:

llama-server -hf LiquidAI/LFM2.5-24B-A2B-GGUF:Q4_K_M

It was tuned for tool calling and instruction following, and Liquid reports 27.69 on AutomationBench versus 8.72 for Qwen3.6-35B-A3BVendor claim. At 184 tokens per second on an Apple M5 MaxVendor claim, it's worth testing as the brain of a local agent that never sends company data off the machine. Coding is weaker, though: 51% on SWE-Bench VerifiedVendor claim against Qwen3.6's 66.2%, so it fits workflow automation better than code work.