MoLE (Mixture of Latent Experts) introduces a novel architecture that addresses memory and communication challenges in traditional Mixture of Experts models while maintaining performance.

• Reduces memory usage and communication overhead by operating in a latent space
• Maintains competitive performance with significantly fewer resources
• Enables more efficient training and inference for large language models
• Offers practical solutions to scaling challenges in LLM development

This innovation matters for engineering teams by providing a more resource-efficient approach to building and deploying large language models, potentially reducing infrastructure costs while preserving model capabilities.

Beyond Standard MoE: Mixture of Latent Experts for Resource-Efficient Language Models