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<!DOCTYPE html>
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<a class="idx-logo" href="index.html" aria-label="doctor-study 首页">
<span class="idx-logo-mark">DS</span>
<strong>doctor-study</strong>
<small>AI Infra interview desk</small>
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<a href="#study-tracks">5 条主线</a>
<a href="pages/cs-basics/index.html">计算机基础</a>
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AI Infra Interview Notes
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<h1>把面试知识<br>组织成一张<span class="hero-accent">系统地图</span></h1>
<p class="idx-hero-desc">毕业准备 · 面试 / 项目 / 简历 / 学习笔记总入口</p>
<p class="idx-hero-sub">从硬件互联、Linux 内核、Kubernetes 调度、分布式训练到 LLM 推理，把每个模块放到资源路径和回答结构里理解，而不是孤立背概念。</p>
<div class="idx-actions">
<a class="idx-primary" href="pages/ai-infra/gpu/index.html">
<span>从 GPU 主线开始</span>
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<a class="idx-secondary" href="pages/cs-basics/index.html">补 Linux / CS 基础</a>
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<strong>19</strong>
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<strong>149</strong>
<span>知识模块</span>
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<strong>579</strong>
<span>面试问答</span>
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<strong>5</strong>
<span>复习主线</span>
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</section>
<section class="idx-lanes" id="study-tracks" aria-label="5 条复习主线">
<article><span>01</span><h3>计算机基础</h3><p>操作系统、组成原理、网络、Linux/容器、编程工程与分布式基础，先补足 AI Infra 面试的系统底座。</p><div class="idx-lane-links">计算机基础知识 / 操作系统基础 / 计算机组成原理 / 计算机网络基础 / 编程语言与工程基础 (C/C++) / Go 语言基础与工程 / Linux 与容器基础 / Linux 内核与大模型系统 / 分布式与 AI Infra 基础</div></article>
<article><span>02</span><h3>AI Infra 核心系统</h3><p>从 Transformer、GPU/CUDA 到算力资源模型，建立大模型系统最核心的硬件与模型执行直觉。</p><div class="idx-lane-links">GPU 硬件与资源共享 / Transformer 与大模型基础</div></article>
<article><span>03</span><h3>调度与集群</h3><p>任务调度理论、Kubernetes 与 GPU 集群管理，覆盖多租户、拓扑、队列和稳定性治理。</p><div class="idx-lane-links">Kubernetes 核心 / 任务调度理论 / GPU 集群管理</div></article>
<article><span>04</span><h3>推理 / 训练 / 性能</h3><p>LLM 推理、分布式训练与性能预测建模，聚焦吞吐、延迟、显存、通信和容量判断。</p><div class="idx-lane-links">LLM 推理系统 / 分布式训练 / 性能预测与建模</div></article>
<article><span>05</span><h3>论文项目与 Agent</h3><p>论文工作和 Agent 工程表达，服务自我介绍、项目深挖和高频追问；综合设计题已归入各专题专栏。</p><div class="idx-lane-links">论文工作 / AI Agent</div></article>
</section>
<section class="idx-track" aria-label="计算机基础"><div class="idx-track-head"><span>Foundation</span><h2>计算机基础</h2><p>操作系统、组成原理、网络、Linux/容器、编程工程与分布式基础，先补足 AI Infra 面试的系统底座。</p></div><div class="idx-track-cards"><a class="idx-card c4" href="pages/cs-basics/index.html" data-progress-key="doctor-study-progress::/pages/cs-basics/index.html#tabs" data-progress-total="0">
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<div class="idx-title">计算机基础知识</div>
<div class="idx-desc">计算机基础知识地图入口：操作系统、组成原理、网络、C/C++、Go、容器、Linux内核、分布式AI等8个子模块的导航入口。</div>
<div class="idx-tags" aria-label="标签"><span>cs-basics</span><span>overview</span><span>knowledge-map</span></div>
</a>
</div><a class="idx-track-more" href="pages/cs-basics/index.html">查看全部 9 个模块 →</a></section>
<section class="idx-track" aria-label="AI Infra 核心系统"><div class="idx-track-head"><span>Core Systems</span><h2>AI Infra 核心系统</h2><p>从 Transformer、GPU/CUDA 到算力资源模型，建立大模型系统最核心的硬件与模型执行直觉。</p></div><div class="idx-track-cards"><a class="idx-card c2" href="pages/ai-infra/gpu/index.html" data-progress-key="doctor-study-progress::/pages/ai-infra/gpu/index.html#topic-tabs" data-progress-total="10">
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<div class="idx-title">GPU 硬件与资源共享</div>
<div class="idx-desc">GPU 架构、A100/H100/H200 对比、HBM/NVLink、MIG、MPS、时间片、CUDA VMM 等硬件与资源复用基础。</div>
<div class="idx-tags" aria-label="标签"><span>gpu</span><span>hardware</span><span>sharing</span></div>
</a>
<a class="idx-card c5" href="pages/ai-infra/transformer/index.html" data-progress-key="doctor-study-progress::/pages/ai-infra/transformer/index.html#transformer-tabs" data-progress-total="10">
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<div class="idx-title">Transformer 与大模型基础</div>
<div class="idx-desc">Transformer 整体架构、Embedding/位置编码/Tokenizer、Self-Attention 与 Multi-Head、手撕代码、梯度稳定性与面试高频题。</div>
<div class="idx-tags" aria-label="标签"><span>transformer</span><span>attention</span><span>tokenizer</span></div>
</a>
</div></section>
<section class="idx-track" aria-label="调度与集群"><div class="idx-track-head"><span>Scheduling</span><h2>调度与集群</h2><p>任务调度理论、Kubernetes 与 GPU 集群管理，覆盖多租户、拓扑、队列和稳定性治理。</p></div><div class="idx-track-cards"><a class="idx-card c3" href="pages/ai-infra/kubernetes/index.html" data-progress-key="doctor-study-progress::/pages/ai-infra/kubernetes/index.html#kubernetes-tabs" data-progress-total="17">
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<div class="idx-title">Kubernetes 核心</div>
<div class="idx-desc">架构、API 对象、调度框架、GPU Device Plugin、etcd Raft/MVCC、Informer 机制、CNI/kube-proxy、API Server 链路、Karpenter 与节点生命周期。</div>
<div class="idx-tags" aria-label="标签"><span>k8s</span><span>scheduler</span><span>etcd</span><span>cni</span></div>
</a>
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<div class="idx-title">任务调度理论</div>
<div class="idx-desc">FIFO/SJF/EDF、DRF、Gang Scheduling、Bin Packing、排队论（Little's Law/M/M/1）、调度架构演进（Borg/Mesos/Omega/Firmament）与 Spot 实例调度。</div>
<div class="idx-tags" aria-label="标签"><span>scheduling</span><span>theory</span><span>queueing</span><span>borg</span></div>
</a>
<a class="idx-card c1" href="pages/ai-infra/cluster-management/index.html" data-progress-key="doctor-study-progress::/pages/ai-infra/cluster-management/index.html#cluster-management-tabs" data-progress-total="5">
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<div class="idx-title">GPU 集群管理</div>
<div class="idx-desc">Volcano、Yunikorn、Kueue、多租户配额、GPU 拓扑与 NCCL、故障恢复与 Checkpoint。</div>
<div class="idx-tags" aria-label="标签"><span>cluster</span><span>volcano</span><span>fault-tolerance</span></div>
</a>
</div></section>
<section class="idx-track" aria-label="推理 / 训练 / 性能"><div class="idx-track-head"><span>Serving & Training</span><h2>推理 / 训练 / 性能</h2><p>LLM 推理、分布式训练与性能预测建模，聚焦吞吐、延迟、显存、通信和容量判断。</p></div><div class="idx-track-cards"><a class="idx-card c1" href="pages/ai-infra/llm-inference/index.html" data-progress-key="doctor-study-progress::/pages/ai-infra/llm-inference/index.html#llm-inference-tabs" data-progress-total="16">
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<div class="idx-title">LLM 推理系统</div>
<div class="idx-desc">Prefill/Decode、TTFT/TPOT、KV 缓存、PagedAttention、FlashAttention、连续批处理、投机解码、量化、多 LoRA 服务与推理引擎。</div>
<div class="idx-tags" aria-label="标签"><span>llm</span><span>inference</span><span>kv-cache</span><span>quantization</span></div>
</a>
<a class="idx-card c5" href="pages/ai-infra/distributed-training/index.html" data-progress-key="doctor-study-progress::/pages/ai-infra/distributed-training/index.html#distributed-training-tabs" data-progress-total="7">
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<div class="idx-title">分布式训练</div>
<div class="idx-desc">数据并行、张量并行、流水线并行、专家并行、ZeRO、梯度同步与通信优化等训练核心知识。</div>
<div class="idx-tags" aria-label="标签"><span>training</span><span>parallelism</span><span>zero</span></div>
</a>
<a class="idx-card c2" href="pages/ai-infra/performance-prediction/index.html" data-progress-key="doctor-study-progress::/pages/ai-infra/performance-prediction/index.html#performance-prediction-tabs" data-progress-total="6">
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<div class="idx-title">性能预测与建模</div>
<div class="idx-desc">树模型 vs 深度学习、特征工程、回归/分类指标、安全裕度、在线校准和 Concept Drift。</div>
<div class="idx-tags" aria-label="标签"><span>prediction</span><span>ml-for-systems</span></div>
</a>
</div></section>
<section class="idx-track" aria-label="论文项目与 Agent"><div class="idx-track-head"><span>Projects & Interview</span><h2>论文项目与 Agent</h2><p>论文工作和 Agent 工程表达，服务自我介绍、项目深挖和高频追问；综合设计题已归入各专题专栏。</p></div><div class="idx-track-cards"><a class="idx-card c1" href="pages/ai-infra/papers/index.html" data-progress-key="doctor-study-progress::/pages/ai-infra/papers/index.html#papers-tabs" data-progress-total="6">
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<div class="idx-title">论文工作</div>
<div class="idx-desc">Maestro 与 DeepShare 两篇论文工作的背景、系统设计、实验结果和高频问答。</div>
<div class="idx-tags" aria-label="标签"><span>papers</span><span>maestro</span><span>deepshare</span></div>
</a>
<a class="idx-card c1" href="pages/ai-infra/agent/index.html" data-progress-key="doctor-study-progress::/pages/ai-infra/agent/index.html#agent-tabs" data-progress-total="7">
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<div class="idx-title">AI Agent</div>
<div class="idx-desc">ReAct、Reflexion、ToT、LATS、记忆系统、工具调用与 Function Calling、高级 RAG（分块/混合检索/重排/GraphRAG）、多 Agent 协作与面试高频题。</div>
<div class="idx-tags" aria-label="标签"><span>agent</span><span>llm</span><span>react</span><span>rag</span></div>
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