Mixture of Experts Explained from Scratch | Transformers Made Simple

Modern AI models with hundreds of billions of parameters don't actually use all of them for every token. In this video, you'll learn exactly how Mixture of Experts (MoE) works—from tokenization and transformers to routers and expert networks—in the simplest way possible. Mixture of Experts (MoE) is one of the biggest architectural innovations behind today's large language models. Instead of sending every token through one gigantic neural network, MoE uses a small router to choose only the most relevant expert networks for each token. This allows models to become much larger while keeping inference efficient. In this video, we build the concept from first principles. We start with tokenization, embeddings, attention, and feed-forward networks (FFNs), then show exactly how MoE replaces a traditional FFN with multiple expert networks and a router. By following a single token through the transformer, you'll understand what actually happens inside modern LLMs without needing a machine learning background. In this video you'll learn: What Mixture of Experts (MoE) is Why 600B+ parameter models don't use every parameter How transformers process tokens What attention actually does What Feed Forward Networks (FFNs) are How routers select experts Why experts naturally specialize during training Why MoE makes modern LLMs faster and more scalable Whether you're learning AI, machine learning, transformers, LLMs, or retrieval systems like RAG, this video builds an intuitive foundation without unnecessary math. If you enjoy simple, visual explanations of AI concepts, consider subscribing. New videos every week covering LLMs, RAG, AI agents, embeddings, vector databases, transformers, and production AI systems. #AI #MachineLearning #LLM #MoE #MixtureOfExperts#Transformers #GenerativeAI #DeepLearning #ArtificialIntelligence #NeuralNetworks #GPT #AIExplained #DataScience #RAG #TechEducation