AI Infra Learning: A 28-Month Systematic Learning Roadmap for LLM Inference Engineers

This open-source course offers a complete 28-month learning path for engineers who want to transition into or deepen their knowledge in the AI infrastructure field. It covers a full-stack knowledge system from GPU architecture to distributed inference optimization, aiming to fill the gaps of unclear learning paths and fragmented knowledge points in the interdisciplinary AI Infra domain.

As large language models (LLMs) move from laboratories to production environments, the demand for AI infrastructure (AI Infra) engineers is growing explosively. Such engineers need to master deep learning principles, high-performance computing, distributed systems, and software engineering simultaneously, making them one of the most scarce talent types in the AI industry. However, traditional computer education systems and existing online courses rarely cover this interdisciplinary field. Many engineers aspiring to enter the AI Infra domain face challenges such as unclear learning paths, fragmented knowledge points, and a lack of practical projects. The AI Infra Learning project was born to fill this gap.

The course adopts a 'depth-first, breadth-progressive' design philosophy, with a 28-month learning cycle divided into four progressive stages:

Stage 1: Foundation Building (Months 1-6)

Establish underlying cognition, covering GPU architecture and CUDA programming, linear algebra and numerical computing, and deep learning basics. It includes supporting theoretical explanations, code implementation, and performance analysis assignments.

Stage 2: Inference Engine (Months 7-14)

Focus on full-stack optimization of LLM inference, including model compilation and graph optimization, operator optimization and kernel development, memory management and KV Cache optimization, quantization and compression. Students need to hands-on implement a simplified inference engine.

Stage 3: Distributed Systems (Months 15-22)

Covers data/model parallelism, service orchestration and scheduling, and inference serviceization. The assignment is to build a multi-card parallel inference service cluster and conduct stress testing.

Stage 4: Production Practice (Months 23-28)

Integrate full-stack performance tuning, observability and debugging, cost optimization and energy efficiency. The graduation project requires contributing to an open-source inference framework or implementing innovative optimization features.

The course provides rich supporting resources:

• Recommended books: The CUDA C Programming Guide, Designing Machine Learning Systems, etc., with marked required chapters;
• Paper reading list: 50+ core papers (including Transformer, Megatron-LM, DeepSpeed, etc.);
• Code practice repositories: GitHub template repositories for each stage;
• Discussion community: Discord channel and GitHub Discussions, with regular online book clubs.

Suitable for:

1. Traditional backend engineers (transitioning to the AI field);
2. Algorithm engineers (complementing engineering capabilities);
3. College students (AI systems direction). Prerequisites: Proficient in Python/C++, basic machine learning concepts, Linux operation experience, and 10-15 hours of weekly investment. Comparison with existing resources: Unique value lies in systematicness, practice orientation, and continuous updates; a 6-month fast track is provided (skipping some underlying principles).

AI infrastructure is a professional field that requires long-term accumulation, with no shortcuts. AI Infra Learning is not just a course syllabus, but a demonstration of a learning paradigm: starting from first principles, verifying through hands-on practice, and forming transferable problem-solving abilities. For students who aspire to become LLM inference engineers, it is a roadmap worth saving and following.