LLM Architecture

Inside Mixture of Experts: How Sparse Routing Scales LLMs

How Mixture of Experts scales LLMs without proportional inference cost. Covers routing networks, load balancing loss, expert capacity, and why MoE models behave differently from dense transformers.

Published May 12, 2026

14 min read

AI Tools Kit

AI Tools Kit provides free developer tools for working with AI language models. Built by developers, for developers.

Learn more about us →

LLM Architecture

Inside LLM Training: The Transformer Pipeline Explained

The full LLM pre-training pipeline: tokenization, attention computation, cross-entropy loss, backpropagation, AdamW optimizer, and the architectural choices behind billion-parameter scale.

Inside Mixture of Experts: How Sparse Routing Scales LLMs

Related Articles

Inside LLM Training: The Transformer Pipeline Explained