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Liquid AI 推出 LFM2-24B-A2B MoE 模型

Liquid AI 推出 LFM2-24B-A2B MoE 模型
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🦙閱讀原文： Reddit r/LocalLLaMA
#moe#sparse-model#efficient-inference#open-weightslfm2-24b-a2bliquid-ailfm2-24b-a2bhuggingfacellama.cpp

💡LiquidAI's 24B MoE (2B active) runs on laptops – beats scaling plateaus, HF open now

⚡ 30-Second TL;DR

有什麼變化

總參數 24B，每前向傳遞活躍 2.3B

為什麼重要

展示無高運算的邊緣部署高效擴展。将高品質 MoE 帶至消費級硬體，推進本地 AI。

下一步行動

Download LFM2-24B-A2B GGUF from Hugging Face and test on llama.cpp with 32GB RAM setup.

誰應關注：Developers & AI Engineers

關鍵要點

  • •總參數 24B，每前向傳遞活躍 2.3B
  • •MoE 架構：40 層、64 專家、top-4 路由
  • •32GB RAM 執行；llama.cpp 日零支援
  • •基準測試從 350M 至 24B 呈對數線性擴展
  • •指令模型開放權重於 Hugging Face

🧠 深度解析

背景與延伸：來自公開資料，非原文內容。引用 8 個來源。

🔑 增強重點摘要

  • •Liquid AI originated as an MIT CSAIL spinoff founded by Ramin Hasani, Mathias Lechner, Alexander Amini, and Daniela Rus, focusing on liquid neural networks inspired by dynamical systems for improved causality and interpretability.[1][2][3]
  • •LFM models represent Liquid Foundation Models (LFMs), a non-Transformer architecture using hardware-aware co-design for edge, enterprise, and multimodal applications like video, audio, and time series.[4][5][7]
  • •By 2026, Liquid AI achieved unicorn status as a Cambridge-based lab, emphasizing lowest latency across GPUs, CPUs, and NPUs through first-principles design.[6]

🔮 前景展望AI analysis grounded in cited sources

LFM2-24B-A2B advances Liquid AI's open-weight strategy
This release aligns with their open-science commitment via technical reports and model weights, building on prior non-open-sourced LFMs to foster community adoption.[5][7]
MoE in LFMs enables log-linear scaling to larger sparse models
The model's scaling from 350M to 24B parameters demonstrates efficient compute use, rooted in dynamical systems theory for sequential data processing.[7]

⏳ 時間線

2016
Foundational liquid neural network research begins at MIT CSAIL and Vienna University of Technology.[3][6]
2023-12
Liquid AI emerges from stealth as MIT spinoff, announces LFMs beyond Transformers.[1][2]
2024
Releases first series of Liquid Foundation Models (LFMs) in multiple sizes for generative AI.[7]
2025
Launches Liquid Labs, commits to open science with model weights and reports.[5]
2026-02
Unicorn status confirmed; focuses on hardware-in-the-loop LFMs for enterprise.[6]
2026-02
Releases LFM2-24B-A2B, largest sparse MoE model with open weights.[article]
🦙閱讀原文： Reddit r/LocalLLaMA
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