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.
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CPU Performance: LFM2.5-230M is considerably faster than similar-sized attention-based and hybrid models. On a @raspberry Pi 5 and a @Qualcomm Snapdragon Gen4 (@Samsung Galaxy S25 Ultra), it delivers the highest prefill and decode throughput in its class while keeping the smallest memory footprint. It is available today on all platforms:
> llama.cpp (GGUF) for edge
> MLX for Apple Silicon
> vLLM and SGLang for GPU serving
> ONNX for cross-platform
(2/n)
As an early look at ongoing work, we deployed LFM2.5-230M on a Unitree G1, running entirely on-device on its onboard @nvidia Jetson Orin.
The model acts as a skill-selection layer, taking in natural-language instructions and decomposing them into sequences of tool calls.
After a quick fine-tune, "Hold still for 2s, walk forward at 1 m/s for 3 m, hold a one-leg kneel for 5s, walk back at 0.5 m/s for 3 m" becomes a structured multi-step plan automatically.
(3/n)
GPU Performance: For production-grade enterprise deployments, we have also developed an internal GPU inference stack that delivers extremely low-latency serving. We benchmark it against other small models running on SGLang, and across all concurrency levels, LFM2.5-230M achieves considerably lower end-to-end latency.
(4/n)
context window — The maximum amount of text a model can consider at once — its working memory for the current conversation or task.
distillation — Training a small, cheap model to imitate a big one's outputs, keeping much of the capability at a fraction of the cost.
agent skill — A reusable instruction file that teaches an agent how to do one job well — the procedure, the tools, and what counts as done.
tool use — A model's ability to call external functions — run code, search the web, edit files — instead of only generating text.
Why it matters
Instruction following, extraction and tool useA model's ability to call external functions — run code, search the web, edit files — instead of only generating text.Full definition → at a size that fits phones, robots and network hardware, at 213 tok/s on a Galaxy S25 Ultra and 42 tok/s on a Raspberry Pi 5, with launch support across the main runtimes.
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