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Hydar Muhammad Bello

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Monday 07 September 2026 14:47:38 GMT

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MoE: The Routing Trick Behind Every Model You Actually Run Kimi K3 has 896 experts inside it, and 16 of them fire for any word it writes. That sounds like the giant model you could finally run at home, and it isn't. Mixture of experts is the architecture behind nearly every model you used this year, and the thing most developers get wrong about it shows up on your hardware bill. A model that computes with 2% of itself still has to keep 100% of itself in memory. This video walks through what actually happens when your prompt hits the router, why the word

MoE: The Routing Trick Behind Every Model You Actually Run Kimi K3 has 896 experts inside it, and 16 of them fire for any word it writes. That sounds like the giant model you could finally run at home, and it isn't. Mixture of experts is the architecture behind nearly every model you used this year, and the thing most developers get wrong about it shows up on your hardware bill. A model that computes with 2% of itself still has to keep 100% of itself in memory. This video walks through what actually happens when your prompt hits the router, why the word "expert" is misleading, and how to read the two numbers on a model card before you buy hardware. Get the hotter takes in your inbox, every Tuesday: https://devsplainers.c... Chapters: 00:00 The 896 experts nobody uses 00:40 What an "expert" actually is 01:57 Inside the router: 256 slices, 8 picked 02:52 Every word, every stage 04:18 Nobody assigns the specialties 05:09 What sparsity actually buys you 05:35 Why the RAM bill is for the whole model 06:33 MoE models break your rules more often 07:20 Total vs active on a model card 07:41 Why a 400B model undercuts a 27B one What is mixture of experts? A mixture of experts (MoE) model splits the big block inside each stage of a language model into many narrower copies, called experts, and adds a small router that picks a handful of them for every single word. Qwen's 35B-A3B keeps 256 experts per stage and fires 8, so 3 billion parameters do the work of 35 billion. That cuts the math per token, which is why MoE LLMs generate fast and cost less per token on an API. It does not cut memory. The router can send the next word to any expert at any stage, so every parameter has to stay loaded. Total parameters tell you what hardware to buy. Active parameters tell you how fast it runs. In this video: • How a MoE router scores and picks experts, per token and per layer • Why an "expert" is not a Python expert or a French expert • The 20.7% number that kills naive expert prefetching • Dense vs MoE models, and where the savings actually come from • Load balancing, dead experts, and DeepSeek's fix • Why streaming experts off an SSD costs 13 seconds per word • Reading total vs active parameters (35B-A3B) before sizing a machine • Why MoE models follow your tool-use rules less reliably than dense ones

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