explaingit

infrasys-ai/aisystem

Analysis updated 2026-06-24

16,744Jupyter NotebookAudience · researcherComplexity · 2/5Setup · easy

TLDR

An open educational course in Chinese on the full AI systems stack, covering chips, compilers, inference, and frameworks through slides, notebooks, and lectures.

Mindmap

mindmap
  root((AISystem))
    Inputs
      Reader study time
    Outputs
      Slides
      Jupyter notebooks
      Video lectures
    Topics
      AI chips
      AI compilers
      Inference systems
      Framework internals
    Use Cases
      Learn AI infrastructure
      Prepare for systems roles
      Teach a graduate course
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Code map

Detail Auto

An interactive map of this repo's files and how they connect — its source is parsed live in your browser. Click Visualize to build it.

filefunction / class

What do people build with it?

USE CASE 1

Self-study how AI hardware, compilers, and frameworks fit together

USE CASE 2

Use the slide decks as the backbone for teaching a graduate AI systems course

USE CASE 3

Prepare for interviews at AI infrastructure or chip teams

What is it built with?

JupyterPythonMarkdown

How does it compare?

infrasys-ai/aisystemgooglecloudplatform/generative-aimicrosoft/iot-for-beginners
Stars16,74416,83616,905
LanguageJupyter NotebookJupyter NotebookJupyter Notebook
Setup difficultyeasymoderatemoderate
Complexity2/52/52/5
Audienceresearcherdevelopergeneral

Figures from each repo's GitHub metadata at analysis time.

How do you get it running?

Difficulty · easy Time to first run · 5min

Course materials are mostly in Chinese, so non-Chinese readers will need translation help.

In plain English

Infrasys-AI/AISystem is an open-source educational course focused on the full software and hardware stack that powers AI systems. The course is primarily in Chinese and delivered through slides, Jupyter notebooks, and video lectures. It targets senior undergraduates, graduate students, and AI infrastructure practitioners who want a deep understanding of how AI systems are built. The course covers five main areas. The first is an overview of the complete AI system stack, algorithms, frameworks, and hardware architecture. The second dives into AI chip design, covering CPUs, GPUs, and specialized AI processors, and how chip design must account for AI algorithms and frameworks. The third covers AI compilers, tools that translate high-level model descriptions into efficient machine code. The fourth covers AI inference systems (running a trained model to produce predictions), including model compression techniques like quantization and pruning that make models smaller and faster. The fifth covers AI framework technologies such as automatic differentiation (software that computes the math gradients needed for training), computational graphs, and distributed training. Someone would use this resource when learning how AI works not just at the algorithm level, but at the systems level, understanding how hardware, compilers, frameworks, and inference engines connect. Slides are on GitHub, video lectures are on external video platforms.

Copy-paste prompts

Prompt 1
Summarize the AISystem course module on AI compilers in English with a diagram of the lowering pipeline
Prompt 2
Translate the AISystem chapter on quantization and pruning into English notes
Prompt 3
Build a 12-week syllabus from the AISystem repo for a graduate AI systems class
Prompt 4
Compare what AISystem teaches about inference engines to TensorRT and vLLM in practice

Frequently asked questions

What is aisystem?

An open educational course in Chinese on the full AI systems stack, covering chips, compilers, inference, and frameworks through slides, notebooks, and lectures.

What language is aisystem written in?

Mainly Jupyter Notebook. The stack also includes Jupyter, Python, Markdown.

How hard is aisystem to set up?

Setup difficulty is rated easy, with roughly 5min to a first successful run.

Who is aisystem for?

Mainly researcher.

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