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Google's new Gemma 4 26B A4B runs 10 simultaneous AI requests on a MacBook Pro — no API costs, no cloud dependency. This video breaks down the MoE architecture that makes it possible and exactly how to run it locally on consumer hardware today.
00:00 Intro – Why you're still paying for API calls you don't need to
00:20 What is Gemma 4 26B A4B? – The model overview
00:42 Gemma 4 vs Gemini 3 – Same foundations, open-source and free
01:14 Full model lineup – E2B, E4B, 26B A4B, and 31B Dense explained
01:52 MoE Architecture explained – Why only 4B of 26B parameters activate
02:32 Dense vs MoE – Speed and compute comparison
03:12 Hardware requirements – What you need to run it locally
03:36 Multi-instance inference – 10 concurrent requests on one laptop
04:28 256K context window – What that actually means in practice
04:39 Multimodal + thinking mode – Images, reasoning chains, and more
04:52 Agentic framework support – LangChain, LlamaIndex, JSON output
05:16 Best tools to run it – Ollama, LlamaC++, LM Studio, MLX
05:57 Known Apple Silicon issue – Flash attention fix for Gemma 4
06:11 Windows/Linux GPU guide – RTX 4090 and partial offload options
06:19 Try it free – No hardware? Use Google AI Studio
06:25 The big picture – What this shift means for local AI in 2025
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