MoE (Mixture of Experts)
MoE is a model architecture that divides an AI into many smaller, specialized 'expert' sub-networks. Instead of activating the entire massive brain for every question, it only activates the specific experts needed.
Why it Matters
it allows models like DeepSeek V3 and GPT-4 to be incredibly smart while running much faster and cheaper than traditional dense models.
Top AI Tools Using MoE (Mixture of Experts)
Discover the best tools that leverage this technology
How It Works
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MoE models use a 'gating network' or router to determine which experts (usually top-2) process each token.
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This results in 'sparse activation,' where a model might have 671B parameters total but only uses 37B per token active parameters during inference, drastically reducing computational costs.
Real-World Example
DeepSeek V3 and Mixtral 8x7B are famous MoE models. When you ask them a coding question, the router activates the 'coding experts' while keeping the 'creative writing experts' dormant, saving energy and token costs.