Mixture of Experts (MoE) 2.0 is an advanced neural architecture that dynamically routes computational tasks to specialized expert networks based on input characteristics, dramatically improving parameter efficiency while maintaining or exceeding performance compared to traditional dense models. This evolution addresses the fundamental limitations of first-generation MoE systems through sophisticated routing mechanisms, hierarchical expert organization, and adaptive load balancing techniques. The architecture builds upon conditional computation principles where only a subset of model parameters activate for each input token or sequence. MoE 2.0 architectures consist of four essential components: a gating network, expert networks, load balancing mechanisms, and modern implementations maintain stable training dynamics even at very large scales. Real-world applications include Large Language Models, Computer Vision Systems, and Multimodal AI Applications, where computational efficiency matters most. The technical architecture of MoE 2.0 represents a fundamental evolution in conditional computation, addressing core limitations through dynamic expert selection algorithms, hierarchical routing strategies, adaptive load balancing techniques, and reinforcement learning-based optimization. Implementing MoE 2.0 architectures requires careful consideration of system architecture, training optimization, and monitoring strategies, including distributed architecture patterns, optimized training procedures, sparse gradient handling, and comprehensive monitoring infrastructure. Successful implementation with Galileo provides advanced architecture evaluation tools, real-time expert monitoring capabilities, production-scale performance optimization recommendations, comprehensive testing frameworks, automated failure detection, and a platform for exploration and optimization.