Mixture of Experts is a neural network architecture that splits computation across multiple specialized sub-networks called experts.
A gating mechanism selects only a small subset of experts for each input, so the model can store far more knowledge in its parameters without proportionally increasing the compute needed for every prediction. This sparse activation pattern enables trillion-parameter models that remain practical to run. Also known as: MoE
What this topic covers
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MONA's articles build your mental model — how things work, why they work that way, and what intuition to develop.
Concepts covered
MoE models promise scale at fractional compute cost. Understand routing collapse, memory tradeoffs, and communication overhead — the hard engineering limits.
Mixture of experts activates only selected sub-networks per token. Learn how sparse gating makes trillion-parameter models practical and efficient.
Mixture of experts replaces one feedforward layer with many expert networks and a router. Learn how MoE gating and routing enable trillion-parameter models.
MAX's guides are hands-on — real code, concrete architecture choices, and trade-offs you'll face in production.
Tools & techniques
Deploy and fine-tune open-weight MoE models like DeepSeek-V3, Mixtral 8x22B, and Llama 4. Hardware mapping, expert parallelism setup, and LoRA strategies that work.
DAN tracks how this domain is evolving — which models, techniques, and benchmarks are reshaping 2026.
Models & benchmarks
Updated April 2026
Mixture of experts is now the default frontier architecture. Why every major lab chose MoE over dense models, and what it means for inference costs.
ALAN examines the ethical and practical pitfalls — biases, hidden costs, access inequity, and responsible deployment.
Risks & metrics
Trillion-parameter MoE models promise efficiency through sparse activation. But training costs keep rising, and the ability to shape AI concentrates fast.