← 🧠 Foundations of LLMs & GenAINEXT IN FOUNDATIONS OF LLMS & GENAIScaling Laws→
Core
Mixture-of-Experts
A Mixture-of-Experts model swaps the dense feed-forward layer for many expert networks plus a router that sends each token to only a few of them. This separates total parameters (capacity) from per-token compute: the model can be huge while each token activates only a slice. The trade-offs are routing complexity, memory (all experts must be loaded), and load balancing. AI, ML, and GenAI interviews probe it because most frontier models are MoE, and it explains how models grow more capable without proportionally more inference cost.
a free account unlocks the core curriculum tier · no card
RELATED CONCEPTS
PRACTICE THIS IN REAL QUESTIONS
LLM & GenAI FundamentalsExplain Mixture of Experts (MoE): how it works and the training and inference tradeoffs.→ML Infrastructure & GPUsWhat makes Mixture-of-Experts models hard to train, and how do you handle routing, load balance, and all-to-all?→System Design for AI in ProductionDesign a text-to-SQL feature: let users ask questions in natural language over a real database.→System Design for AI in ProductionDesign an LLM gateway in front of multiple model providers (routing, caching, fallback, rate limits, observability).→LLM & GenAI FundamentalsWhat do the feed-forward (MLP) layers in a transformer do, and why are they most of the parameters?→RAG & Agent System DesignHow do you optimize a RAG or agent system for cost and latency in production?→
COMPANIES THAT ASSUME THIS