---
title: "Expert Parallelism & Expert Placement"
description: "How an engine maps a router's expert choices onto GPUs, and how expert parallelism, where each device holds whole experts, differs from tensor parallelism."
canonical_url: "https://fanout.sh/inference-eng/curriculum/19-03"
md_url: "https://fanout.sh/inference-eng/curriculum/19-03.md"
access: "pro-preview"
---

# Expert Parallelism & Expert Placement

How an engine maps a router's expert choices onto GPUs, and how expert parallelism, where each device holds whole experts, differs from tensor parallelism.

- Access: Fanout Pro (public preview)

## Public preview

A router returns logical expert IDs. A GPU can execute those assignments only after the engine knows which weights it owns. With tensor parallelism each device holds a slice of every expert; with expert parallelism each device holds complete experts and receives the tokens assigned to them.

## In this lesson

- The fourth axis
- A per-device plan
- From global IDs to local slots
- Requested placement and actual placement
- Practice: revise the placement sheet

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