Liquid AI released LFM2.5-230M

Brief

LFM2.5-230M is Liquid AI's new 230M-parameter LFM2-family model, pre-trained on 19T tokens with a 32K context and distilled from LFM2.5-350M. Designed for extremely low-latency agentic workloads, it hits 213 tok/s on a Galaxy S25 Ultra CPU and 42 tok/s on a Raspberry Pi 5, and Liquid AI says it often outperforms models more than twice its size; demo shown as a skill-selection layer on a Unitree G1 robot.

Key details

Measured decode speeds: 213 tok/s on a Galaxy S25 Ultra (CPU) and 42 tok/s on a Raspberry Pi 5 (CPU); designed to run on CPUs, NPUs, and GPUs for extremely low-latency agentic tasks on phones, robots, and edge devices.

Company claims LFM2.5-230M competes with and often beats models more than twice its size on instruction following, data extraction, and tool use; demoed as a skill-selection layer converting prompts into tool calls on a Unitree G1 robot and pitched for large-scale extraction pipelines or on-device agentic workloads.

We are releasing the smallest member of the LFM2 family yet: LFM2.5-230M.

It is meant for specialized, extremely low latency tasks.

Check out the demo of it being used as a skill-selection layer, turning prompts into tool calls on a Unitree G1:

Video

Liquid AI (@liquidai)

Introducing LFM2.5-230M: our smallest model yet, built to run fast anywhere (CPUs, NPUs, and GPUs) to enable agentic tasks on phones, robots, home and network automation devices.

> 230M parameters, built on the LFM2 architecture
> Pre-trained on 19T tokens, with a 32K context extension
> Post-trained with distillation from LFM2.5-350M
> 213 tok/s decode speed on Galaxy S25 Ultra (CPU)
> 42 tok/s on a Raspberry Pi 5 (CPU)
> Competes with and often beats models more than twice its size on instruction following, data extraction, and tool use.
> use it for large-scale data extraction pipelines or lightweight on-device agentic workloads.

🧵

— https://nitter.net/liquidai/status/2070148140368318884#m

Brief

Why it matters

Key details