Mixture of Experts

Imagine having a team of specialists instead of one generalist trying to handle everything. That's essentially what Mixture of Experts (MoE) architecture does for AI models. 

Instead of routing every query through one massive network, the system activates specific "experts,” or smaller networks trained for particular tasks or topics.

Here are a few examples: 

When you ask about medical information, the medical expert kicks in. Need help with coding? The programming expert takes over. Ask for financial advice, and the economics expert activates. Request creative writing assistance, and the language arts expert responds. Mathematical problems trigger the quantitative reasoning expert, while historical questions engage the humanities specialist. Travel planning might activate both geography and logistics experts simultaneously.

This approach can significantly reduce computational costs since only relevant experts activate for each query, rather than engaging the entire model.

For users, this often translates to faster responses and potentially more accurate answers, since specialized networks can develop deeper expertise in their domains. Models like DeepSeek use this architecture to balance performance with efficiency. 

The Mixture of Experts approach represents a significant shift toward more sustainable AI development that enables powerful capabilities without the massive energy consumption typically associated with large-scale language models.