Beyond Transformers: How Liquid AI Is Rethinking LLM Architecture | Maxime Labonne

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Previously titled “Beyond Transformers: Maxime Labonne on Post-Training, Edge AI, and the Liquid Foundation Model Breakthrough” — renamed by the publisher on Aug 4, 2026

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Recorded12 Nov 2025

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The transformer architecture has dominated AI since 2017, but it’s not the only approach to building LLMs - and new architectures are bringing LLMs to edge devicesMaxime Labonne, Head of Post-Training at Liquid AI and creator of the 67,000+ star LLM Course, joins Conor Bronsdon to challenge the AI architecture status quo. Liquid AI’s hybrid architecture, combining transformers with convolutional layers, delivers faster inference, lower latency, and dramatically smaller footprints without sacrificing capability. This alternative architectural philosophy creates models that run effectively on phones and laptops without compromise.But reimagined architecture is only half the story. Maxime unpacks the post-training reality most teams struggle with: challenges and opportunities of synthetic data, how to balance helpfulness against safety, Liquid AI’s approach to evals, RAG architectural approaches, how he sees AI on edge devices evolving, hard won lessons from shipping LFM1 through 2, and much more. If you're tired of surface-level AI takes and want to understand the architectural and engineering decisions behind production LLMs from someone building them in the trenches, this is your episode.Connect with ⁨Maxime Labonne⁩ :LinkedIn – https://www.linkedin.com/in/maxime-labonne/ X (Twitter) – @maximelabonneAbout Maxime – https://mlabonne.github.io/blog/about.html HuggingFace – https://huggingface.co/mlabonne The LLM Course – https://github.com/mlabonne/llm-course Liquid AI – https://liquid.ai Connect with ⁨Conor Bronsdon⁩ :X (twitter) – @conorbronsdonSubstack – https://conorbronsdon.substack.com/ LinkedIn – https://www.linkedin.com/in/conorbronsdon/00:00 Intro — Welcome to Chain of Thought 00:27 Guest Intro — Maxime Labonne of Liquid AI 02:21 The Hybrid LLM Architecture Explained 06:30 Why Bigger Models Aren’t Always Better 11:10 Convolution + Transformers: A New Approach to Efficiency 18:00 Running LLMs on Laptops and Wearables 22:20 Post-Training as the Real Moat 25:45 Synthetic Data and Reliability in Model Refinement 32:30 Evaluating AI in the Real World 38:11 Benchmarks vs Functional Evals 43:05 The Future of Edge-Native Intelligence 48:10 Closing Thoughts & Where to Find Maxime Online

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