CalvinXKY/InfraTech resource

Health v2 · maintenance only

Flags: no_releases no_license

Adoption not part of the score

3683 stars · 350 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

A collection of Jupyter notebook tutorials and exercises covering AI infrastructure topics including PyTorch, vLLM/SGLang inference frameworks, performance acceleration, and LLM fundamentals. Content is presented as runnable Python notebooks with accompanying explanatory articles, primarily in Chinese.

Use cases

• learn how LLM inference engines like vLLM and SGLang work
• understand RoPE and MLA attention computation with worked examples
• study distributed inference parallelism strategies like DP, TP, PP, EP
• practice chunked prefill and flash decoding implementations
• learn speculative decoding and LLM sampling techniques
• understand collective communication for distributed training and inference
• calculate MFU and attention FLOPs with prefix caching

When to choose

• you want hands-on notebook exercises for AI infra and LLM inference internals
• you are an engineer preparing to work on inference optimization or GPU performance
• you prefer Chinese-language explanations paired with runnable code
• you need to understand attention variants like MLA and RoPE at a low level

When to avoid

• you need a production inference framework rather than learning material
• you want English-only documentation
• you are a complete beginner to deep learning and Python
• you need a formally licensed, citable library for commercial reuse

Facets

learning-resource · maturity active

machine-learning llm-inference llm-training gpu-computing developer-tools artificial-intelligence large-language-models deep-learning machine-learning gpu-computing tutorials python cross-platform jupyter-notebooks ai-infrastructure vllm sglang pytorch inference-optimization distributed-training attention-mechanisms speculative-decoding chinese-language gpu

Member repositories

For agents

markdown · JSON · MCP: product_card(name="CalvinXKY/InfraTech")