Large Model Systems & AI Chips Learning Roadmap: A Full-Stack Guide from Hugging Face to Heterogeneous Computing

This large model system learning roadmap for Chinese developers covers 20 topics including the Hugging Face ecosystem, training & fine-tuning, inference optimization, CUDA/CANN heterogeneous computing, etc., aiming to help engineers build a complete cognitive system from models to chips. The tutorial connects technical links from an engineering perspective, different from traditional reviews or introductory tutorials, providing problem-oriented practical guidance.

Most AI tutorials on the market lean towards researchers' paper reviews or quick starts for beginners, but the uniqueness of this tutorial lies in answering three engineering questions: 1. The positioning of the tech stack in real engineering; 2. The boundary between problems that technology solves and does not solve; 3. The first step of hands-on practice (projects/code). This problem-oriented approach makes it more like an engineer's practical notes rather than a list of knowledge.

The tutorial's knowledge architecture is divided into three layers:

Basic Layer

• Hardware Architecture: Differentiate between CUDA (NVIDIA native), ZLUDA (CUDA compatibility layer), and CANN (Huawei Ascend native ecosystem);
• Model Formats: PyTorch, ONNX (suitable for deployment but not training), safetensors, OM (Ascend-specific).

Core Layer

• Hugging Face Ecosystem: Transformers (model loading), PEFT/TRL (LoRA and other fine-tuning methods), vLLM/TGI (inference optimization);
• LoRA Principle: Keep pre-trained weights unchanged, adapt to tasks through a small number of parameters, reducing the experiment threshold.

Advanced Layer

• Distributed Training Strategies: Data Parallelism (DDP), Model Parallelism, Pipeline Parallelism, ZeRO/FSDP (optimizer state sharding).

The tutorial provides 12 core questions to verify learning effectiveness, such as:

• Why doesn't LoRA retrain the entire model?
• Why can vLLM improve concurrent throughput?
• What is the relationship between CUDA kernel, Ascend C custom operators, and PyTorch ops?
• Which parts do quantization techniques like INT4 and QLoRA target respectively? This problem-driven approach better tests the true level of understanding.

Learning suggestions for readers with different backgrounds:

• Want to distinguish between CUDA/ZLUDA/CANN: Focus on Chapters 1-2;
• Working on Hugging Face projects: Read Chapters 3-5 (workflow, fine-tuning, inference) + Chapters 13-14 (evaluation/data engineering) carefully;
• Moving towards chip system direction: Chapters 6-7 (chip roadmap/hands-on practice) + Chapters17-18 (advanced inference/distributed training);
• Building production applications: Chapters15-16 (RAG/Agent, quantization) + Chapter19 (security and operation).

Large model technology evolves rapidly; the value of the tutorial does not lie in standard answers, but in helping to establish a thinking framework for technological evolution: clarifying the position of technical points in the stack, the problems they solve, and their limitations. For Chinese developers, this is a systematic engineer's guide that requires reading while practicing, thinking while verifying.