An open book on AI infrastructure architecture has been published

Developers presented AI-infra-book — a comprehensive guide to designing and operating systems for training and running neural networks. The project covers the full stack of technologies needed to scale modern models, from choosing hardware to optimizing inference. The material is structured by key engineering disciplines. The sections detail the principles of model architectures, methods of distributed training on GPU clusters, and strategies for minimizing latency when executing queries. Particular attention is paid to hardware optimization, including setting up interconnects and managing memory in high-load environments. The book contains practical examples and experimental data that allow evaluating the effectiveness of various infrastructure configurations. The authors provided the opportunity to download the content in PDF format for offline study. All source code and testing methodologies are available in the repository under an open license. The resource is aimed at engineers involved in deploying AI systems in production. The publication systematizes fragmented knowledge about working with CUDA, task schedulers, and data storage systems, which simplifies the design of fault-tolerant clusters for working with large language models.