Phase 1: Transformer Architecture · ~45 minutes · Python
Mixture of Experts (MoE)
A dense 70B transformer activates every parameter for every token. A 671B MoE activates only 37B per token and beats it on every benchmark. Sparsity is the most important scaling idea of the decade.
What you will learn
- Implement mixture of experts (moe) from scratch
- Understand the math and intuition behind the algorithm
- Use production libraries for the same task
- Ship a reusable artifact
Introduction
- Type: Build
- Languages: Python
- Prerequisites: The Full Transformer — Encoder + Decoder, GPT — Causal Language Modeling
- Time: ~45 minutes
The Problem
A dense transformer's FLOPs at inference equal its parameter count (times 2 for forward pass). Scale up a dense model and every token pays the full bill. By 2024 the frontier was hitting a compute wall: to be meaningfully smarter, you needed exponentially more FLOPs per token.
Mixture of Experts breaks this link. Replace each FFN with E independent experts + a router that picks k experts per token. Total parameters = E × FFN_size. Active parameters per token = k × FFN_size. Typical 2026 configuration: E=256, k=8. Storage scales with E, compute scales with k.
The 2026 frontier is almost entirely MoE: DeepSeek-V3 (671B total / 37B active), Mixtral 8×22B, Qwen2.5-MoE, Llama 4, Kimi K2, gpt-oss. On Artificial Analysis's independent leaderboard, the top 10 open-source models are all MoE.
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