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Home/Models & Labs
Models & Labs

Liquid AI releases efficient on-device agent model

Hugging Face Blog·October 13, 2026·high confidence

Why it matters

  • →Smaller MoE models can now compete with larger dense or MoE models in complex agentic benchmarks.
  • →High token throughput on Apple Silicon makes real-time local agent interaction feasible for consumers.
  • →Open weights and broad framework support lower the barrier to deploying private, reasoning-capable agents.
Liquid AI releases efficient on-device agent model
©Hugging Face Blog

Liquid AI has released LFM2.5-24B-A2B, an open-weight mixture-of-experts model optimized for on-device agentic workflows. The model features a 131K context window and 2B active parameters per token, allowing it to outperform larger models like Qwen3.6-35B in tool-use benchmarks while maintaining high inference speeds on consumer hardware such as the Apple M5 Max. Available via Hugging Face with day-one support for major inference engines including vLLM and llama.cpp, the release targets developers seeking private, efficient local AI agents.

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The story around this

Earlier coverage that leads up to this article, and what followed. Lines connect each piece to the closest one after it, converging here.

MiniCPM5: A 1B Model for On-Device Projects — Sam Witteveen1Scaling AI Agents with Redis Iris — Cole Medin2Intel Explores Agentic AI for Enterprise Workflows — MIT Technology Review AI3Together AI Enhances Model Inference Configuration — Together AI Blog4Together AI Introduces Autoscaling for LLM Inference — Together AI Blog5LFM2.5-2.6B: Efficient AI Agents for Local Deployment — Hugging Face Blog6OpenAI buys Macs for private AI agents — Wes Roth7Jev: A Decision-Only AI Model for Agents — Cole Medin8Liquid AI releases efficient on-device agent modelNvidia releases open Nemotron models for agents and PCs — TechCrunch AI9Liquid AI releases LFM3 multimodal models — Hugging Face Blog10llama.cpp adds LiquidAI LFM3 model support — llama.cpp Releases11Jul 5You are hereOct 28

How we got here

  1. 1
    MiniCPM5: A 1B Model for On-Device Projects

    Sam Witteveen · July 5, 2026 · Related

  2. 2
    Scaling AI Agents with Redis Iris

    Cole Medin · July 9, 2026 · Related

  3. 3
    Intel Explores Agentic AI for Enterprise Workflows

    MIT Technology Review AI · July 27, 2026 · Related

  4. 4
    Together AI Enhances Model Inference Configuration

    Together AI Blog · July 29, 2026 · Related

  5. 5
    Together AI Introduces Autoscaling for LLM Inference

    Together AI Blog · July 31, 2026 · Related

  6. 6
    LFM2.5-2.6B: Efficient AI Agents for Local Deployment

    Hugging Face Blog · August 4, 2026 · Same story

  7. 7
    OpenAI buys Macs for private AI agents

    Wes Roth · September 1, 2026 · Related

  8. 8
    Jev: A Decision-Only AI Model for Agents

    Cole Medin · September 21, 2026 · Related

What happened next

  1. 9
    Nvidia releases open Nemotron models for agents and PCs

    TechCrunch AI · October 19, 2026 · Related

  2. 10
    Liquid AI releases LFM3 multimodal models

    Hugging Face Blog · October 27, 2026 · Same story

  3. 11
    llama.cpp adds LiquidAI LFM3 model support

    llama.cpp Releases · October 28, 2026 · Related

Follow this story

Open the full story →

Liquid AI opens d1 decision models for edge inference

2 developments

  1. Oct 7 · Hugging Face Blog
    Liquid AI opens d1 decision models for edge inference
  2. Oct 13 · Hugging Face Blog
    Liquid AI releases efficient on-device agent model (This article)↳ Liquid AI releases LFM2.5-24B-A2B, an efficient on-device agent model with 131K context that decodes at 184 tok/s on Apple M5 Max.

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llama.cpp b12029 adds CUDA 13 and Snapdragon support

This release quietly cements llama.cpp as the universal inference runtime by finally supporting NVIDIA's latest CUDA 13 stack alongside AMD's ROCm 10.0. The inclusion of native builds for Linux arm64 with Snapdragon chips, covering CPU, Adreno GPU, and Hexagon NPU, signals a serious push into mobile edge deployment. While Apple Silicon KleidiAI is disabled in this specific build, the broader hardware coverage means developers no longer need to compile from source for most modern accelerators. It’s less about new features and more about removing friction for anyone running local models on bleeding-edge or diverse hardware.

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