Mixture of Experts (MoE) architecture offers a smarter approach to scaling text embedding models without the typical computational costs.

• Addresses key deployment challenges in inference latency and memory usage
• Enables more efficient Retrieval-Augmented Generation (RAG) applications
• Achieves comparable performance to dense models while using fewer resources
• Significantly reduces computational bottlenecks that limit adoption

This engineering innovation matters because it makes powerful language models more accessible for real-world applications, allowing for larger context windows and faster processing in production environments.

Original Paper: Training Sparse Mixture Of Experts Text Embedding Models