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LFM2.5-230M is a 230 million–parameter model from Liquid AI that achieves up to…

2026-06-25 · 17:14 UTC ·@BrianRoemmele ·2 min read

Brief

LFM2.5-230M, Liquid AI’s 230M-parameter edge model, combines high decode speeds (213 tok/s on Galaxy S25 Ultra, 42 tok/s on Raspberry Pi 5) with a 32K context window. Pre-trained on 19 trillion tokens and distilled from a 350M model, it’s positioned as a compact, high‑efficiency option for on‑device agents, robotics, data extraction, and privacy-preserving applications.

Why it matters

LFM2.5-230M is a 230 million–parameter model from Liquid AI that achieves up to 213 tokens/sec decode on a Galaxy S25 Ultra CPU and 42 tokens/sec on a Raspberry Pi 5, and offers a 32K context window.

Key details

  • The model was pre-trained on 19 trillion tokens using the LFM2 architecture and distilled from Liquid AI’s larger 350M model; Liquid AI claims it outperforms many models more than twice its size on instruction following, data extraction, and tool use.
  • Liquid AI demonstrated fully on‑device operation (Unitree G1 robot with NVIDIA Jetson Orin); the release includes instruct and base variants (LFM2.5-230M and LFM2.5-230M-Base) and out-of-the-box support for llama.cpp (GGUF), MLX (Apple Silicon), vLLM/SGLang (GPU), and ONNX.
Source evidence

Wow!

Liquid AI just released LFM2.5-230M, their smallest and most efficient model yet. This 230 million parameter powerhouse is built for speed and real-world use on phones, robots, Raspberry Pi devices, and other edge hardware.

We love how it pushes the boundaries of what tiny models can achieve.

The numbers speak for themselves. LFM2.5-230M delivers up to 213 tokens per second decode speed on a Galaxy S25 Ultra CPU. On a Raspberry Pi 5, it hits 42 tokens per second.

These speeds make it one of the fastest options in its class while using the smallest memory footprint.

It outperforms many models more than twice its size on key tasks like instruction following, data extraction, and tool use. This efficiency comes from its LFM2 architecture, pre-training on 19 trillion tokens, and distillation from the larger 350M model.

The result is a compact model with a 32K context window that feels much smarter than its size suggests.

We especially like this model for practical agentic applications. Liquid AI demonstrated it running entirely on-device on a Unitree G1 robot powered by an NVIDIA Jetson Orin.

The model takes natural language instructions and turns them into structured multi-step plans with tool calls. Imagine robots, home automation, or phone-based agents that work offline, privately, and instantly.

It shines in large-scale data extraction pipelines and lightweight agentic tasks. Whether you need fast summarization, structured output, or reliable tool calling,

LFM2.5-230M delivers without relying on the cloud.

The model supports a wide range of frameworks right out of the box:
•llama.cpp (GGUF) for edge devices
•MLX for Apple Silicon
•vLLM and SGLang for GPU serving
•ONNX for cross-platform compatibility

Both the instruct version (LFM2.5-230M) and base version (LFM2.5-230M-Base) are available now.

Liquid AI reminds us that intelligence density matters most. A 230M model that runs this fast and performs this well opens the door to truly ubiquitous AI.

No more cloud dependency for many everyday tasks. Faster, cheaper, and more private experiences become possible on hardware people already own.

It is a big win for developers building on-device applications, robotics teams, and anyone who values speed and privacy.

Hugging Face.liquid.ai/blog/lfm2-5-230m

Link

LFM2.5-230M: Built to Run Anywhere | Liquid AI

Meet LFM2.5-230M, Liquid AI’s smallest model yet: a fast, open-weight foundation model for fine-tuning, edge deployment, tool use, and data extraction.
liquid.ai