Twitter/X

LFM2.5-230M is a 230 million parameter model pre-trained on 19T tokens, with a…

2026-06-25 · 14:18 UTC ·@ramin_m_h ·1 min read

Brief

LFM2.5-230M is Liquid AI's 230M-parameter, highly distilled LFM2 model optimized for edge and on-device agentic tasks. Pretrained on 19T tokens with a 32K context and distilled from a 350M variant, it reports 213 tok/s on a Galaxy S25 Ultra CPU and 42 tok/s on a Raspberry Pi 5 while claiming to outcompete models >2× its size.

Why it matters

LFM2.5-230M is a 230 million parameter model pre-trained on 19T tokens, with a 32K context-extension and post-trained via distillation from LFM2.5-350M.

Key details

  • Measured decode speeds: 213 tokens/sec on a Galaxy S25 Ultra (CPU) and 42 tokens/sec on a Raspberry Pi 5 (CPU); claims to compete with or beat models more than twice its size on instruction following, data extraction, and tool use.
  • Designed for on-device and edge agentic workloads (phones, robots, home and network automation, NPUs/CPUs/GPUs) and large-scale data-extraction pipelines.
Source evidence

meet the tinniest LFM: LFM2.5-230m. fast, lightweight, easy to tune, sexy, ready to be used on robots, phones, home and network automation devices with CPUs! god-mode speed on GPUs too!

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