Understanding Mixture of Experts in Large Language Models
An interactive visualization of how Mixture of Experts (MoE) architecture works in modern language models, showing the flow from input through expert networks to final output.
Mixture of Experts (MoE) Processing Flow
Interactive MoE Demo
Experts Network
Language Expert
Grammar and language structure
Math Expert
Mathematical calculations and concepts
Science Expert
Scientific knowledge and reasoning
Logic Expert
Logical reasoning and problem-solving
Creative Expert
Creative writing and storytelling
About Mixture of Experts (MoE)
Mixture of Experts (MoE) is an architectural approach used in modern large language models that enables efficient scaling by selectively activating only parts of the network for each input. This allows models to effectively grow in capacity without proportional increases in computation.
Key Benefits
- Computational Efficiency: Only activates relevant parts of the network
- Specialized Processing: Different experts handle different types of inputs
- Increased Model Capacity: Can scale to larger sizes with better efficiency
- Improved Performance: Often results in better results on complex tasks
Applications
MoE architecture is used in models like Google's GLaM, Switch Transformers, and Anthropic's Claude models. It has become a key design pattern in the most advanced AI systems, enabling larger and more capable models.