---
title: "Training Compute-Optimal Large Language Models"
description: "Chinchilla is the scaling paper that made data budget feel as important as parameter count. It is a good daily pick for learning why a smaller model trained on more tokens can beat a larger undertrained one."
canonical_url: "https://fanout.sh/daily/2026-07-16-chinchilla"
md_url: "https://fanout.sh/daily/2026-07-16-chinchilla.md"
last_updated: "2026-07-16"
access: "public"
---

# Training Compute-Optimal Large Language Models

Chinchilla is the scaling paper that made data budget feel as important as parameter count. It is a good daily pick for learning why a smaller model trained on more tokens can beat a larger undertrained one.

## Paper details

- Authors: Hoffmann et al.

- Venue: arXiv 2022

- Track: AI Research

- Difficulty: Medium

- Reading time: 45 min

- Original paper: https://arxiv.org/abs/2203.15556

## What you will learn

- Why compute-optimal training balances model size and token count.

- How scaling laws become practical training-budget decisions.

- Why undertrained large models can waste compute despite looking impressive.

## Continue exploring

[Explore LLM Training](https://fanout.sh/knowledge-graph?node=ai-research%3Amodule%3Allm-from-scratch): Use Fanout's LLM path to connect compute-optimal training with language-model architecture and training practice.

---
This representation contains public Fanout content only. Protected Pro lessons, account data, billing, checkout, and pricing are not included.

Browse the public content map: https://fanout.sh/sitemap.md