# LFM2.5-2.6B — Liquid AI's 2.6B on-device agent competes with 4x-larger models

> A 2.6B open-weight agent that plans, calls tools, and runs entirely on phones, laptops, and robots.

- Org: Liquid AI
- Date: 2026-08-04
- Category: model
- Source: https://www.liquid.ai/blog/lfm2-5-2-6b
- Canonical page: https://ai-tldr.dev/releases/liquidai-lfm2-5-2-6b/

## In short

Liquid AI released LFM2.5-2.6B, an open-weight 2.6B agent model trained on ~34T tokens with a 128K context. It runs 30 tok/s on a phone and 220 tok/s on Apple M5 Max, and leads Gemma-4 and Qwen3.5 on tool use and instruction following.

## What it is

LFM2.5-2.6B is Liquid AI's new 2.6B-parameter open-weight language model, built for agentic workflows that run entirely on-device. It ships with base and instruction-tuned checkpoints on Hugging Face, a 128K context window, and support for 16 languages.

## How it works

The 30-layer hybrid stacks 22 double-gated convolution blocks with 8 grouped-query attention layers, pre-trained on ~34T tokens. Post-training turns the base into an agent through supervised fine-tuning, per-domain teacher distillation, multi-domain on-policy distillation, and a final agentic reinforcement-learning stage.

## Why it matters

LFM2.5-2.6B lets developers deploy planning, tool calling, and multi-step reasoning without a cloud round-trip — 30 tok/s on a phone and 220 tok/s on an M5 Max CPU — while beating Gemma-4 and Qwen3.5 4.7B on tool use and instruction following. Data stays on the device and the marginal cost per run is essentially zero.

## Who it's for

developers shipping local agents on phones, laptops, or robots

## Try it

huggingface.co/LiquidAI/LFM2.5-2.6B

## Quick facts

- Maker: Liquid AI
- Parameters: 2.69B (dense, 30 layers)
- Architecture: 22 double-gated conv + 8 GQA layers
- Context window: 131,072 tokens (128K)
- Training data: ~34T tokens, 16 languages
- License: LFM Open License 1.0 (Apache-2.0 based, revenue cap $10M)
- Availability: Open weights on Hugging Face; llama.cpp, MLX, vLLM, SGLang, ONNX

## FAQ

### How much does LFM2.5-2.6B cost to use?

LFM2.5-2.6B is free to download and run under Liquid AI's LFM Open License 1.0, which is based on Apache 2.0. Companies with annual revenue under $10M USD may use it commercially at no cost; larger companies must contact Liquid AI for a commercial license. There is no hosted API tier.

### How does LFM2.5-2.6B compare to Gemma-4 and Qwen3.5?

LFM2.5-2.6B leads every instruction-following benchmark Liquid AI tested and beats Gemma-4 5.1B and Qwen3.5 4.7B on tool use, trailing only Qwen3.5-9B on one BFCLv4 subtest. It lags Qwen3.5-9B on math (AIME25 51.87 vs 56.07) and code (LiveCodeBench 59.41 vs 69.86) — Qwen3.5-9B is 3.6x larger.

### What hardware can run LFM2.5-2.6B?

LFM2.5-2.6B fits in under 2.5 GB and runs at 30 tok/s on modern phones, 113 tok/s on an AMD Ryzen AI Max+, 220 tok/s on an Apple M5 Max CPU, and ~15K tok/s at high concurrency on an H100 GPU. Data never leaves the device.

### Which inference frameworks support it?

LFM2.5-2.6B ships with day-zero support for llama.cpp (GGUF), Apple MLX, vLLM, SGLang, and ONNX. Base and instruction-tuned checkpoints are on Hugging Face under LiquidAI/LFM2.5-2.6B, and a WebGPU browser demo runs entirely client-side.

## Links

- [Announcement](https://www.liquid.ai/blog/lfm2-5-2-6b)
- [HF Blog](https://huggingface.co/blog/LiquidAI/lfm2-5-2-6b)
- [Model (base + instruct)](https://huggingface.co/LiquidAI/LFM2.5-2.6B)
- [License](https://www.liquid.ai/lfm-license)