AIInfraGuide

This open-source project addresses the critical need for systematic, practical, and Chinese-language learning resources in AI infrastructure (AI Infra). It provides a comprehensive, continuously updated knowledge base covering the entire AI Infra stack, from GPU hardware and CUDA programming to distributed training and inference optimization. The guide is designed for engineers seeking to build a robust AI infrastructure knowledge system and prepare for AI Infra roles, offering significant benefits in career development and technical mastery.

How It Works

The knowledge base is meticulously structured into four core learning modules: Prerequisites, CUDA Programming & Operator Optimization, Distributed Training, and Inference Optimization. These are complemented by auxiliary sections on Performance Analysis and a detailed Interview Guide. Each article employs a "plain language first, then technical terms" approach, ensuring concepts are accessible before diving into rigorous technical details. This methodology covers foundational concepts, advanced techniques like 3D parallelism and PagedAttention, and practical tools for performance analysis.

Quick Start & Requirements

Access the continuously updated guide online at https://caomaolufei.github.io/AIInfraGuide/. No direct installation is required for the learning material itself. The content assumes foundational knowledge in programming (Python, C/C++), mathematics, and Linux. Practical application of certain modules, such as CUDA programming or distributed training, may necessitate appropriate hardware (e.g., GPUs).

Highlighted Details

• Full-Stack Coverage: Systematically maps the entire AI Infra technology stack, from low-level GPU architecture and CUDA programming to high-level distributed training strategies (DDP, FSDP, 3D parallelism) and cutting-edge inference optimization techniques (PagedAttention, quantization, speculative decoding).
• Interview Preparation: Features an extensive "Interview Guide" with over 180 real interview questions from more than 40 companies, categorized by company tier and interview focus, offering targeted preparation for AI Infra roles.
• Pedagogical Approach: Employs a dual-layer explanation strategy, first introducing concepts in simple terms before delving into precise technical details, enhancing comprehension for a broad audience.

Maintenance & Community

The project is actively maintained, with content continuously updated. Contributions are welcomed via GitHub Issues for suggestions and bug reports, and Pull Requests for sharing practical experiences and technical details. Community engagement is facilitated through WeChat, a WeChat Official Account ("AI炼金炉"), and Zhihu ("草帽路飞").

Licensing & Compatibility

Licensed under the permissive MIT License, allowing for broad compatibility with commercial use and integration into closed-source projects without significant restrictions.

Limitations & Caveats

The "Performance Analysis" section is currently marked as "Under Construction." While comprehensive, the depth of coverage for every specific sub-topic may vary, and practical implementation may require consulting additional resources. The primary language of the guide is Chinese.