Kimi K3’s 2.8 Trillion Parameter Trick — Only 16 Experts Wake Up

Описание: Kimi K3 contains almost 2.8 trillion parameters—but for each token, only 16 out of 896 experts are activated.

That apparent contradiction is the key to understanding its architecture.

In this video, we explore how Kimi K3 combines enormous model capacity with selective computation, efficient communication, long-context memory, and improved information flow.

You’ll learn how four important architectural ideas work together:

• Mixture of Experts — activating only a small group of experts for each token
• Stable Latent MoE — reducing communication between GPUs and stabilizing expert routing
• Kimi Delta Attention — processing extremely long contexts more efficiently
• Attention Residuals — allowing deeper layers to retrieve useful earlier representations

This is not simply a story about building a larger AI model.

It is a story about selective scale: giving a model access to enormous capacity while teaching it when not to use it.

CHAPTERS

00:00 — The 2.8-trillion-parameter paradox
01:00 — Why full activation is too expensive
01:16 — Mixture of Experts explained
02:17 — Why experts must be distributed across GPUs
02:52 — Stable Latent MoE
03:25 — Expert routing and quantile balancing
04:33 — Why attention becomes expensive
05:18 — Kimi Delta Attention
05:48 — A more efficient kind of memory
07:18 — Why deep models can lose information
07:58 — Attention Residuals
08:56 — The bigger picture
10:02 — Why selective scale matters

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What architecture should I explain next? Let me know in the comments.

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