Liquid AI released LFM2.5-2.6B, an agentic model that runs entirely on-device…

Brief

Liquid AI announced LFM2.5-2.6B, a 2.5–2.6B-parameter agentic model intended to run fully on-device (phones, laptops, PCs, robots) without GPUs. Pretrained on ~34T tokens with an LFM2.5 hybrid architecture, 128K context and vocab, it’s customizable on one GPU, released under an open-weight license, and reported higher benchmark scores than several larger models.

Key details

Model specs: pre-trained on ~34T tokens using the LFM2.5 flagship hybrid architecture, context length 128K, vocab size 128K; the model is customizable on a single GPU and distributed under the LFM2 open-weight license.

Benchmark claims: Liquid AI reports ToolSandbox 77.83 (vs Qwen3.5-9B 76.44), Multi-IF 80.07 (vs Gemma-4-E4B-it 77.35), and IFStruct 85.49 (vs Qwen3.5-9B 78.50), asserting comparable or better scores versus models up to ~4x its size.

An AI model powerful than Qwen, Gemma and sits inside your phone and pc. No GPU

Liquid AI (@liquidai)

Today we release LFM2.5-2.6B, an agentic model that runs entirely on-device. It plans, calls tools, and works through multi-step tasks on phones, laptops, PCs, and robots. Data never leaves the device, and the marginal cost of each run is essentially zero.

> Pre-trained on ~34T tokens
> LFM2.5 flagship hybrid architecture
> Context length: 128K
> Vocab size: 128K
> balanced intelligence per watt
> customizable on a single GPU for any specialized task
> LFM2 open-weight license

Comparable or better scores compared to models up to nearly 4x its size:
> ToolSandbox 77.83, ahead of Qwen3.5-9B at 76.44
> Multi-IF 80.07, ahead of Gemma-4-E4B-it at 77.35
> IFStruct 85.49, ahead of Qwen3.5-9B at 78.50

🧵

— https://nitter.net/liquidai/status/2084640701669613906#m

Brief

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