> ## Documentation Index
> Fetch the complete documentation index at: https://se7en.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# 博客

> AI Infra 领域的推荐博客

### vLLM

* [Inside vLLM: Anatomy of a High-Throughput LLM Inference System](https://blog.vllm.ai/2025/09/05/anatomy-of-vllm.html)
* [vLLM源码解析](https://zhuanlan.zhihu.com/p/691038809)

### 推测解码

* [How Speculative Decoding Boosts vLLM Performance by up to 2.8x](https://blog.vllm.ai/2024/10/17/spec-decode.html)

## GPU

* [Building a Tiny GPU to Understand AI Hardware Engineering](https://levelup.gitconnected.com/building-a-tiny-gpu-to-understand-ai-hardware-engineering-62bb08f2d1d1)
* [A history of NVidia Stream Multiprocessor](https://fabiensanglard.net/cuda/index.html)

## Harness Engineering

Harness Engineering 指的是一种开发方式：工程师不直接写大量代码，而是设计环境、规则和测试反馈系统，让 AI Agent 自动生成并改进代码。

* [Effective harnesses for long-running agents](https://www.anthropic.com/engineering/effective-harnesses-for-long-running-agents)
* [Harness engineering: leveraging Codex in an agent-first world](https://openai.com/index/harness-engineering/)
* [Minions: Stripe’s one-shot, end-to-end coding agents](https://stripe.dev/blog/minions-stripes-one-shot-end-to-end-coding-agents)
* [Minions: Stripe’s one-shot, end-to-end coding agents—Part 2](https://stripe.dev/blog/minions-stripes-one-shot-end-to-end-coding-agents-part-2)
* [Vibe Coding AReaL：零手打代码开发分布式 RL 训练框架](https://zhuanlan.zhihu.com/p/2003269671630165191)

## 量化

* [PyTorch 官方资料](https://docs.pytorch.org/docs/main/quantization.html#introduction-to-quantization)
* [PyTorch 的量化实战项目](https://github.com/Laicheng0830/Pytorch_Model_Quantization/blob/main/pose_estimation.py)

## CUTLASS

* [Learn CUTLASS the hard way!](https://www.kapilsharma.dev/posts/learn-cutlass-the-hard-way/) ([Video](https://www.youtube.com/watch?v=_ZOK0WmGm7o))
* [Learn CUTLASS the hard way - part 2!](https://www.kapilsharma.dev/posts/learn-cutlass-the-hard-way-2/)

## Triton

* [Deep Dive into Triton Internals (Part 1)](https://www.kapilsharma.dev/posts/deep-dive-into-triton-internals/)
* [Deep Dive into Triton Internals (Part 2)](https://www.kapilsharma.dev/posts/deep-dive-into-triton-internals-2/)
* [Deep Dive into Triton Internals (Part 3)](https://www.kapilsharma.dev/posts/deep-dive-into-triton-internals-3/)

## FlashAttention

* [FlashAttention from First Principles](https://ai.gopubby.com/flashattention-from-first-principles-part-1-5a9f2407d739#bypass)
* [FlashAttention — Visually and Exhaustively Explained](https://ai.gopubby.com/flashattention-visually-and-exhaustively-explained-d6124670f7fb#bypass)
* [Flash Attention 2.0 with Tri Dao (author)!](https://www.youtube.com/watch?v=IoMSGuiwV3g\&t=2240s)
* [Flash Attention学习过程【详】解](https://www.bilibili.com/video/BV1FM9XYoEQ5)
* [ELI5: FlashAttention](https://gordicaleksa.medium.com/eli5-flash-attention-5c44017022ad)
* [Designing Hardware-Aware Algorithms: FlashAttention](https://www.digitalocean.com/community/tutorials/flashattention)
* [FlashAttention: Fast and Memory-Efficient Exact Attention With IO-Awareness](https://www.nvidia.com/en-us/on-demand/session/gtc24-s62546/)

## CUDA

* [How to Optimize a CUDA Matmul Kernel for cuBLAS-like Performance: a Worklog](https://siboehm.com/articles/22/CUDA-MMM)