Model Types

Modern AI systems leverage different architectural approaches to balance performance, efficiency, and capability. This section explores two fundamental model paradigms:

Dense Models

Traditional, unified architectures where every parameter participates in every inference.

• Consistent computational requirements
• Simpler training and deployment

Learn more about Dense Models →

Mixture-of-Experts (MoE)

Sparse, conditional architectures that activate specialized sub-networks ("experts") based on input.

• Dynamic computational scaling
• Specialized knowledge routing

Learn more about Mixture-of-Experts →

---

💡 Key Trade-off: Dense models offer simplicity and predictability, while MoE architectures provide scalability and efficiency at the cost of implementation complexity.