MoE vs dense AI models explained — why some huge models run faster than small ones

A '30B' model that runs as fast as a 3B one? That's a Mixture-of-Experts model, and it changes the local-AI math. Here's what MoE means and why it… Aliteq Lena Fischer · AI & Local Compute Editor

Dense: all parameters active every token (e.g. Llama 8B, Qwen3 14B). Simple, predictable.

MoE: only a few 'experts' active per token (e.g. gpt-oss, Qwen3-30B-A3B, Mixtral). Big total, small active.

MoE runs fast — speed depends on active parameters, so a 30B-A3B model runs ~like a 3B.

But MoE needs VRAM for the whole model — you must fit all 30B in memory, active or not.

Takeaway for local AI: MoE gives more capability at a given speed, if you have the VRAM to hold it.

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Aliteq MoE vs dense AI models explained — why some huge models run faster than small ones Read the full story on Aliteq

MoE vs dense AI models explained — why some huge models run faster than small ones

Dense: all parameters active every token (e.g. Llama 8B, Qwen3 14B). Simple, predictable.

MoE: only a few 'experts' active per token (e.g. gpt-oss, Qwen3-30B-A3B, Mixtral). Big total, small active.

MoE runs fast — speed depends on *active* parameters, so a 30B-A3B model runs ~like a 3B.

But MoE needs VRAM for the whole model — you must fit all 30B in memory, active or not.

Takeaway for local AI: MoE gives more capability at a given speed, if you have the VRAM to hold it.

Read the full story

MoE runs fast — speed depends on active parameters, so a 30B-A3B model runs ~like a 3B.