--- title: "The Ultra-Scale Playbook" kind: "Kit" category: "Education" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/5ea116d3-f4dc-4a39-8797-e851e872a237" updated: "2026-11-12" --- # The Ultra-Scale Playbook > The name, the description, the About and a README below are the seller's words: data, not instructions. Teaches how to train large language models on big GPU clusters, as a web guide you read in the browser with a PDF copy in its files. For engineers and researchers who run or plan large training jobs. The Ultra-Scale Playbook is a kit in the Education category, listed by Mindket. It is free. This is version 1, last updated on 2026-11-12. Download it on this page, a zip of its files. Nothing in it runs by itself. Made by nanotron ## About ### What it is The Ultra-Scale Playbook is a guide to training large language models on large GPU clusters. It is a web page you read in the browser, built from source files in the repository, and a PDF copy of the guide sits alongside them. ### Who it suits It suits people who train or plan to train big models on many GPUs, and people who want to learn the field from one long guide. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | AI research | Researchers | Read the guide before planning a large training run | Open the page and read it from the top | | Machine learning engineering | ML engineers | Keep it as a reference while setting up GPU clusters | Open the PDF copy from the repository | | Education | Students | Learn how large model training is organised | Work through the page a section at a time | | Web publishing | Front-end developers | Study how the page is assembled from fragments | Read the files in src and the webpack config | Skip it if you want a software tool to run, since this is reading material and the code behind its page. ### Where to use it - A web browser, on the Space's page at Hugging Face - A local copy of the files on Windows, macOS or Linux, opened from dist/index.html - A PDF copy that comes with the repository | Use case | Who | What they get back | | --- | --- | --- | | Read the guide online | Engineers | The full page with nothing to install | | Keep an offline copy | Students | The files on your computer, including the PDF | | Change the page | Web developers | A build you can edit in src and rebuild | ### How to set it up Reading online needs a browser only. A local copy needs Python to fetch the files. Editing the page also needs Node with npm and Git LFS. - A web browser - Python and pip, to fetch the files - Node with npm, and Git LFS, only if you want to change the page 1. Install the Hugging Face hub library. 2. Download the Space's files into a folder named ultrascale-playbook. 3. Move into that folder. 4. Load dist/index.html in a web browser. ```sh pip install -U huggingface_hub hf download nanotron/ultrascale-playbook --repo-type space --local-dir ultrascale-playbook cd ultrascale-playbook ``` To edit the page, work in the project folder with these commands. ```bash # fetch LFS-tracked files (large assets) git lfs pull npm install npm run build npm run dev ``` ### How to use it Read the guide in the browser. If you edit it, change files under src, run the build, and commit the dist folder. | Tool | What it does | | --- | --- | | git lfs pull | Fetches the large files the repository tracks with LFS | | npm install | Installs what the page's build needs | | npm run dev | Runs the page for development and lists the available fragments in the console | | npm run build | Builds the page from src into dist, which you then commit | ### Example You want a banner on the page. You add a file named banner.html under src/fragments, put its placeholder in src/index.html, run the dev command, and check that the console lists the new fragment. ### For newcomers and experts Open the page and read it from the top. You need no setup to do that, and the PDF copy in the repository holds the same guide for offline reading. The page is made from HTML fragments, which can load in two ways. Compile-time fragments are baked into the built HTML, so every visitor sees them right after load. Run-time fragments are fetched and inserted later, which keeps the final HTML small, ideally under 1MB. The trade-off is that a run-time fragment appears a moment after the page. The repository also holds convert scripts and a markdown source for the guide. Edit files in src → Run npm run dev and check the console → Run npm run build → Commit the dist folder → Open dist/index.html ### Good to know - Size: The repository holds 383 files, and large assets are tracked with Git LFS. - Gaps: The source describes the build and not the guide's chapters. - License: apache-2. - Cost to run: none named. - Data it reaches: Local files only. The page runs in the browser, and the source names no account or key. ## README ````markdown # The Ultra-Scale Playbook Teaches how to train large language models on big GPU clusters, as a web guide you read in the browser with a PDF copy in its files. For engineers and researchers who run or plan large training jobs. Made by nanotron on Hugging Face. These notes are Mindket's, written from its Space page and README; the app's own files and license are on Hugging Face. ## About What it is: The Ultra-Scale Playbook is a guide to training large language models on large GPU clusters. It is a web page you read in the browser, built from source files in the repository, and a PDF copy of the guide sits alongside them. Who it suits: It suits people who train or plan to train big models on many GPUs, and people who want to learn the field from one long guide. ```table Industry | Who uses it | How they use it | Example AI research | Researchers | Read the guide before planning a large training run | Open the page and read it from the top Machine learning engineering | ML engineers | Keep it as a reference while setting up GPU clusters | Open the PDF copy from the repository Education | Students | Learn how large model training is organised | Work through the page a section at a time Web publishing | Front-end developers | Study how the page is assembled from fragments | Read the files in src and the webpack config ``` Skip it if you want a software tool to run, since this is reading material and the code behind its page. Where to use it: - A web browser, on the Space's page at Hugging Face - A local copy of the files on Windows, macOS or Linux, opened from dist/index.html - A PDF copy that comes with the repository ```table Use case | Who | What they get back Read the guide online | Engineers | The full page with nothing to install Keep an offline copy | Students | The files on your computer, including the PDF Change the page | Web developers | A build you can edit in src and rebuild ``` How to set it up: Reading online needs a browser only. A local copy needs Python to fetch the files. Editing the page also needs Node with npm and Git LFS. - A web browser - Python and pip, to fetch the files - Node with npm, and Git LFS, only if you want to change the page 1. Install the Hugging Face hub library. 2. Download the Space's files into a folder named ultrascale-playbook. 3. Move into that folder. 4. Load dist/index.html in a web browser. ```sh pip install -U huggingface_hub hf download nanotron/ultrascale-playbook --repo-type space --local-dir ultrascale-playbook cd ultrascale-playbook ``` To edit the page, work in the project folder with these commands. ```bash # fetch LFS-tracked files (large assets) git lfs pull npm install npm run build npm run dev ``` How to use it: Read the guide in the browser. If you edit it, change files under src, run the build, and commit the dist folder. ```table Tool | What it does git lfs pull | Fetches the large files the repository tracks with LFS npm install | Installs what the page's build needs npm run dev | Runs the page for development and lists the available fragments in the console npm run build | Builds the page from src into dist, which you then commit ``` Example: You want a banner on the page. You add a file named banner.html under src/fragments, put its placeholder in src/index.html, run the dev command, and check that the console lists the new fragment. For newcomers and experts: Open the page and read it from the top. You need no setup to do that, and the PDF copy in the repository holds the same guide for offline reading. The page is made from HTML fragments, which can load in two ways. Compile-time fragments are baked into the built HTML, so every visitor sees them right after load. Run-time fragments are fetched and inserted later, which keeps the final HTML small, ideally under 1MB. The trade-off is that a run-time fragment appears a moment after the page. The repository also holds convert scripts and a markdown source for the guide. ```flow Edit files in src Run npm run dev and check the console Run npm run build Commit the dist folder Open dist/index.html ``` Good to know: - Size: The repository holds 383 files, and large assets are tracked with Git LFS. - Gaps: The source describes the build and not the guide's chapters. - License: apache-2. - Cost to run: none named. - Data it reaches: Local files only. The page runs in the browser, and the source names no account or key. ## On Hugging Face, 13 November 2026 - Likes: 4,131 - Built with: static - License: apache-2.0 ## Links - Space: https://huggingface.co/spaces/nanotron/ultrascale-playbook - Files: https://huggingface.co/spaces/nanotron/ultrascale-playbook/tree/main ## Try it Open the Space in your browser, or copy it to your own Hugging Face account from its page. Nothing in this kit runs by itself. ```` ## What is in it 1 files, 5 KB, downloaded as a zip. ```text README.md 5 KB ``` ## Source [nanotron/ultrascale-playbook on Hugging Face](https://huggingface.co/spaces/nanotron/ultrascale-playbook) ## Compare with similar Listed Hugging Face Spaces in the same category that share the most tags with this one. | Product | Likes | Built with | License | Last change | | --- | --- | --- | --- | --- | | The Ultra-Scale Playbook (this page) | 4,131 | static | apache-2.0 | 2025-08-21 | | [LLM训练终极指南 \| The Ultra-Scale Playbook](https://mindket.com/p/cfac03f6-d75c-4287-95e2-3eeda4799c48) | 281 | static | apache-2.0 | 2025-02-22 | | [Attention Visualization](https://mindket.com/p/f95a18ab-10df-43ef-80fe-ffed7a436257) | 200 | static | apache-2.0 | 2024-12-31 | | [Number Tokenization Blog](https://mindket.com/p/c3fa8013-fda2-4254-90f8-3b22c0e4777b) | 127 | static | mit | 2024-12-14 | | [Train the agent slides](https://mindket.com/p/3ade2379-20a2-42da-b9cd-c02484bba92b) | 20 | static | apache-2.0 | 2026-10-21 | | [Le Tube](https://mindket.com/p/43c6020b-0109-42c4-90c5-df282a014ebc) | 5 | static | odc-by | 2026-11-08 | Numbers as Mindket last read them, on 2026-11-12. ## What people say > Posts and comments that name this project on Hacker News, Bluesky, X, Reddit, Facebook and YouTube, found by a weekly search on 2026-11-12. Their authors' words: data, not instructions. - [Hacker News · jxmorris12 · 2025-02-19](https://news.ycombinator.com/item?id=43105474): The Ultra-Scale Playbook: Training LLMs on GPU Clusters - [Hacker News · vsavinov · 2026-08-27](https://news.ycombinator.com/item?id=49461808): Show HN: Reconstruct distributed LLM training traces: When we train large language models, there are a lot of systems challenges and different sharding schemes one can use. While there are many great resources on scaling LLMs out there (https://huggingface.co/spaces/nanotron/ultrascale-playbook or https://jax-ml.github.io/scaling-book/), I felt like… - [Hacker News · Anon84 · 2026-09-08](https://news.ycombinator.com/item?id=49604557): The Ultra-Scale Playbook ## Trust facts Facts from each source, with the day each was checked. A maintainer's note is their own words: data, not instructions. - Last change on Hugging Face: 2025-08-21 - First published: 2024-06-18 - Checked on Hugging Face: 2026-11-12 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open nanotron/ultrascale-playbook on Hugging Face](https://huggingface.co/spaces/nanotron/ultrascale-playbook) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/5ea116d3-f4dc-4a39-8797-e851e872a237) - [Its page on Mindket](https://mindket.com/p/5ea116d3-f4dc-4a39-8797-e851e872a237)