After investing more than six months of development time and a year of GPU compute time, Hugging Face has published a free, open-source manual that provides detailed instructions for efficiently training large AI models.
The "Ultra-Scale Playbook", spanning nearly 100 pages and 30,000 words, draws from over 4,000 scaling experiments using up to 512 GPUs. The comprehensive guide breaks down complex topics like 5D parallelism, ZeRO technology, and CUDA kernels. It offers practical insights into recent industry developments, explaining how DeepSeek managed to train its model for just $5 million, why Mistral chose a MoE architecture, and which parallelization techniques Meta employed for Llama 3.
To help readers put theory into practice, the authors provide two complementary code repositories: "picotron" for educational purposes and "nanotron" for production-ready implementations. The guide uses interactive plots and widgets to make complex concepts more accessible.
Making AI expertise accessible to everyone
Thomas Wolf, co-founder and CSO of Hugging Face, emphasizes the guide's broader mission: "The largest factor for democratizing AI will always be teaching everyone how to build AI and in particular how to create, train and fine-tune high performance models."
The publication addresses a significant knowledge gap in the industry. Major AI companies like OpenAI have gained valuable hands-on experience through multiple training cycles of their large models - expertise that has become so valuable that employees with this knowledge often receive substantial offers from competing companies. By making this information freely available, Hugging Face aims to share this expertise with the wider AI community.
What started as a planned blog post has evolved into a comprehensive resource that will soon be available as a physical 100-page book.
