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LFM2-24B-A2B-GGUF

LFM2 is a family of hybrid models designed for on-device deployment. LFM2-24B-A2B is the largest model in the family, scaling the architecture to 24 billion parameters while keeping inference efficient.

• Best-in-class efficiency: A 24B MoE model with only 2B active parameters per token, fitting in 32 GB of RAM for deployment on consumer laptops and desktops.
• Fast edge inference: 112 tok/s decode on AMD CPU, 293 tok/s on H100. Fits in 32B GB of RAM with day-one support llama.cpp, vLLM, and SGLang.
• Predictable scaling: Quality improves log-linearly from 350M to 24B total parameters, confirming the LFM2 hybrid architecture scales reliably across nearly two orders of magnitude.

Find more information about LFM2-24B-A2B in our blog post.

How to run LFM2

Example usage with llama.cpp:

llama-cli -hf LiquidAI/LFM2-24B-A2B-GGUF --conversation \
    --temp 0.1 --top-k 50 --repeat-penalty 1.05

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