[{"data":1,"prerenderedAt":1023},["ShallowReactive",2],{"header-counts":3,"footer-counts":6,"compare-cherry-studio-vs-lm-studio":9,"compare-a-cherry-studio":10,"compare-b-lm-studio":539},{"tools":4,"reviews":5},78,26,{"tools":4,"reviews":5,"playbooks":7,"news":8},22,21,null,{"id":11,"title":12,"alternatives":13,"api_compatible":9,"body":18,"category":470,"chinese_friendly":471,"cover":472,"description":473,"domestic":474,"extension":475,"faq":476,"free":474,"github":9,"languages":489,"lastVerified":9,"meta":492,"models":9,"navigation":493,"notSuitable":9,"opensource":493,"path":494,"pillar":495,"platforms":496,"priceTable":501,"pricing":510,"published":511,"relatedPlaybooks":512,"relatedReviews":9,"score":515,"self_host":493,"seo":517,"seoTitle":9,"slug":518,"sources":519,"stem":527,"suitable":9,"tagline":528,"tags":529,"updated":522,"verdict":536,"website":537,"__hash__":538},"tools\u002Ftools\u002Fcoding\u002Flocal\u002Fcherry-studio.md","Cherry Studio",[14,15,16,17],"coding\u002Flocal\u002Flobe-chat","coding\u002Flocal\u002Flm-studio","coding\u002Flocal\u002Follama","coding\u002Flocal\u002Fopen-webui",{"type":19,"value":20,"toc":455},"minimark",[21,26,30,33,36,83,86,100,106,110,115,132,137,154,157,184,187,338,341,373,377,400,403,429,432],[22,23,25],"h2",{"id":24},"tldr","TL;DR",[27,28,29],"p",{},"Cherry Studio 是一款开源、跨平台（Windows \u002F macOS \u002F Linux \u002F Android）的桌面 AI 客户端，定位『全能 AI 工作台』：把 OpenAI \u002F Anthropic \u002F Google \u002F DeepSeek 等云端模型，以及 Ollama \u002F LM Studio 本地模型，全部聚合到同一个桌面应用里管理。内置 300+ 助手模板、本地 RAG 知识库、Markdown + Mermaid 渲染、MCP 协议支持，所有对话数据本地存储 + WebDAV 备份。AGPL-3.0 开源、GitHub 60k+ stars，企业版可联系商务做私有化部署。",[27,31,32],{},"适合：中文 AI 重度用户、想统一管理多家模型、需要本地知识库 RAG、关注数据本地存储的开发者 \u002F 研究者。不适合：要 Web 端访问 \u002F Docker 自托管 \u002F 团队多人共享 \u002F iOS 端使用。",[22,34,35],{"id":35},"核心能力",[37,38,39,47,53,59,65,71,77],"ul",{},[40,41,42,46],"li",{},[43,44,45],"strong",{},"多模型聚合","：OpenAI \u002F Claude \u002F Gemini \u002F DeepSeek \u002F Qwen \u002F Kimi \u002F Moonshot 等云端 + Ollama \u002F LM Studio 本地",[40,48,49,52],{},[43,50,51],{},"本地 RAG 知识库","：拖拽 PDF \u002F Word \u002F Excel \u002F PPT \u002F 网址 \u002F sitemap → 自动向量化 → 检索增强问答 + 来源追溯",[40,54,55,58],{},[43,56,57],{},"300+ 助手模板","：编程 \u002F 写作 \u002F 翻译 \u002F 学习 \u002F 角色扮演开箱即用，可自定义 System Prompt",[40,60,61,64],{},[43,62,63],{},"MCP 协议","：扩展工具调用 \u002F 联网搜索 \u002F 文件操作",[40,66,67,70],{},[43,68,69],{},"数据本地优先","：对话历史本地存储，WebDAV 同步，不上传第三方",[40,72,73,76],{},[43,74,75],{},"多模态","：图片识别 \u002F PDF 阅读 \u002F Markdown + Mermaid + 代码高亮",[40,78,79,82],{},[43,80,81],{},"AI 绘画 + 翻译","：内置主流 SD \u002F DALL·E \u002F 翻译 API 集成",[22,84,85],{"id":85},"价格",[37,87,88,94],{},[40,89,90,93],{},[43,91,92],{},"开源版","：完全免费，AGPL-3.0",[40,95,96,99],{},[43,97,98],{},"Enterprise","：私有化部署 + 团队协作 + 资源管控，联系销售",[101,102,103],"blockquote",{},[27,104,105],{},"模型 API 费用按你自己绑定的供应商计费；本地 Ollama \u002F LM Studio 零成本。",[22,107,109],{"id":108},"实测mac-m2-中型知识库","实测（Mac M2 + 中型知识库）",[27,111,112],{},[43,113,114],{},"亮点：",[37,116,117,120,123,126,129],{},[40,118,119],{},"中文 UI \u002F 文档 \u002F 社区都顶级，零门槛上手",[40,121,122],{},"本地 RAG 拖入 30+ PDF 后向量化 \u003C 2 分钟（用 bge-m3）",[40,124,125],{},"多模型并排回答：让 Claude \u002F GPT \u002F DeepSeek 同回一个问题做比较",[40,127,128],{},"MCP 接 Brave Search + 自定义工具流畅",[40,130,131],{},"WebDAV 同步坚果云 \u002F 阿里云盘，桌面 + 移动设备数据互通",[27,133,134],{},[43,135,136],{},"踩坑：",[37,138,139,142,145,148,151],{},[40,140,141],{},"没有 Web 端 \u002F Docker 自托管（要这个用 LobeChat）",[40,143,144],{},"iOS 版尚未发布（roadmap 中）",[40,146,147],{},"大型 PDF（>100 MB）向量化偶有失败，要切小",[40,149,150],{},"助手市场质量参差，要自筛",[40,152,153],{},"模型 API 调用全靠你自己付费，新手要先理解 API Key 概念",[22,155,156],{"id":156},"上手",[158,159,160,163,166,175,178,181],"ol",{},[40,161,162],{},"cherry-ai.com 下载客户端（或 GitHub releases）",[40,164,165],{},"设置 → 模型服务 → 填 OpenAI \u002F Claude \u002F DeepSeek API Key",[40,167,168,169],{},"（可选）本地：装 Ollama → Cherry Studio 自动识别 endpoint ",[170,171,172],"a",{"href":172,"rel":173},"http:\u002F\u002Flocalhost:11434",[174],"nofollow",[40,176,177],{},"新建知识库 → 拖文件 \u002F 加网址 → 等向量化",[40,179,180],{},"新对话 → 选模型 → 勾知识库 → 提问",[40,182,183],{},"进阶：自定义助手（System Prompt）+ MCP 扩展工具",[22,185,186],{"id":186},"对比",[188,189,190,211],"table",{},[191,192,193],"thead",{},[194,195,196,200,202,205,208],"tr",{},[197,198,199],"th",{},"维度",[197,201,12],{},[197,203,204],{},"LobeChat",[197,206,207],{},"LM Studio",[197,209,210],{},"Open WebUI",[212,213,214,231,245,261,274,290,305,321],"tbody",{},[194,215,216,220,223,226,228],{},[217,218,219],"td",{},"形态",[217,221,222],{},"桌面",[217,224,225],{},"Web + 桌面",[217,227,222],{},[217,229,230],{},"Docker \u002F 桌面",[194,232,233,235,238,240,243],{},[217,234,45],{},[217,236,237],{},"✅ 云 + 本地",[217,239,237],{},[217,241,242],{},"本地为主",[217,244,237],{},[194,246,247,250,253,255,258],{},[217,248,249],{},"知识库 RAG",[217,251,252],{},"✅ 强",[217,254,252],{},[217,256,257],{},"弱",[217,259,260],{},"✅",[194,262,263,266,268,270,272],{},[217,264,265],{},"MCP",[217,267,260],{},[217,269,260],{},[217,271,257],{},[217,273,260],{},[194,275,276,279,282,285,288],{},[217,277,278],{},"自托管 \u002F Web",[217,280,281],{},"无 Web",[217,283,284],{},"✅ Docker",[217,286,287],{},"无",[217,289,284],{},[194,291,292,295,298,300,303],{},[217,293,294],{},"中文",[217,296,297],{},"5\u002F5",[217,299,297],{},[217,301,302],{},"4\u002F5",[217,304,302],{},[194,306,307,310,313,316,319],{},[217,308,309],{},"开源协议",[217,311,312],{},"AGPL-3.0",[217,314,315],{},"MIT",[217,317,318],{},"闭源（免费）",[217,320,315],{},[194,322,323,326,329,332,335],{},[217,324,325],{},"GitHub Stars",[217,327,328],{},"60k+",[217,330,331],{},"72k+",[217,333,334],{},"–",[217,336,337],{},"126k+",[22,339,340],{"id":340},"避坑",[37,342,343,349,355,361,367],{},[40,344,345,348],{},[43,346,347],{},"API Key 别明文外泄","：客户端配置文件以明文存 Key，机器借出前先清；团队共享用企业版 \u002F 自建中转",[40,350,351,354],{},[43,352,353],{},"知识库别一次塞太多","：单库 1000+ 文档检索质量明显下降，按主题切分多个知识库",[40,356,357,360],{},[43,358,359],{},"嵌入模型选择","：免费 bge-m3 够用；专业用付费 Pro\u002FBAAI\u002Fbge-m3 或 OpenAI text-embedding-3",[40,362,363,366],{},[43,364,365],{},"WebDAV 同步先小范围测","：知识库向量数据较大，先备份对话再开同步",[40,368,369,372],{},[43,370,371],{},"MCP 工具来源要可控","：MCP 是给 AI 真实工具能力，第三方插件审一遍代码",[22,374,376],{"id":375},"适合-不适合","适合 \u002F 不适合",[37,378,379,382,385,388,391,394,397],{},[40,380,381],{},"✅ 中文用户、AI 重度使用 \u002F 多模型管理",[40,383,384],{},"✅ 需要本地 RAG 知识库",[40,386,387],{},"✅ 关注数据隐私 \u002F 本地存储",[40,389,390],{},"✅ 想用 Ollama \u002F LM Studio 本地模型",[40,392,393],{},"❌ 需要 Web 端 \u002F Docker 自托管",[40,395,396],{},"❌ 团队多人共享 \u002F SSO",[40,398,399],{},"❌ iOS 主力用户",[22,401,402],{"id":402},"相关阅读",[37,404,405,411,417,423],{},[40,406,407],{},[170,408,410],{"href":409},"\u002Ftools\u002Fcoding\u002Flocal\u002Flobe-chat","LobeChat 评测",[40,412,413],{},[170,414,416],{"href":415},"\u002Ftools\u002Fcoding\u002Flocal\u002Flm-studio","LM Studio 评测",[40,418,419],{},[170,420,422],{"href":421},"\u002Ftools\u002Fcoding\u002Flocal\u002Follama","Ollama 评测",[40,424,425],{},[170,426,428],{"href":427},"\u002Fplaybook\u002Fonboarding\u002Frag-pipeline-build","RAG Pipeline 搭建 Playbook",[22,430,431],{"id":431},"来源",[158,433,434,441,448],{},[40,435,436,437],{},"Cherry Studio 官网（功能 + 下载）",[170,438,439],{"href":439,"rel":440},"https:\u002F\u002Fwww.cherry-ai.com\u002F",[174],[40,442,443,444],{},"MBLUO Studio — Cherry Studio 评测 2026 ",[170,445,446],{"href":446,"rel":447},"https:\u002F\u002Fmbluostudio.com\u002Ftools\u002Fcherry-studio",[174],[40,449,450,451],{},"Cursor IDE 博客 — Cherry Studio 完全指南（2025-03）",[170,452,453],{"href":453,"rel":454},"https:\u002F\u002Fwww.cursor-ide.com\u002Fblog\u002Fcherry-studio-guide",[174],{"title":456,"searchDepth":457,"depth":457,"links":458},"",3,[459,461,462,463,464,465,466,467,468,469],{"id":24,"depth":460,"text":25},2,{"id":35,"depth":460,"text":35},{"id":85,"depth":460,"text":85},{"id":108,"depth":460,"text":109},{"id":156,"depth":460,"text":156},{"id":186,"depth":460,"text":186},{"id":340,"depth":460,"text":340},{"id":375,"depth":460,"text":376},{"id":402,"depth":460,"text":402},{"id":431,"depth":460,"text":431},"local",5,"\u002Fimg\u002Ftools\u002Fcherry-studio.webp","Cherry Studio 真实评测：开源跨平台桌面 AI 客户端，集成 OpenAI \u002F Claude \u002F Gemini \u002F DeepSeek + Ollama \u002F LM Studio 本地模型，内置 300+ 助手模板 + 本地 RAG 知识库。AGPL-3.0 开源、GitHub 60k+ stars，企业版另询。",false,"md",[477,480,483,486],{"q":478,"a":479},"Cherry Studio 真的免费吗？","是。客户端完全免费、AGPL-3.0 开源，模型调用走你自己的 API Key（OpenAI \u002F Claude \u002F Gemini \u002F DeepSeek 等付费）或本地 Ollama \u002F LM Studio（零成本）。",{"q":481,"a":482},"本地知识库怎么用？","在『知识库』面板新建，拖文件 \u002F 加网址 \u002F 填 sitemap，系统自动向量化（默认 BAAI\u002Fbge-m3 或硅基流动的 Pro 版）；提问时勾选要检索的知识库，AI 会基于检索片段答题并标出来源。",{"q":484,"a":485},"和 LobeChat 怎么选？","都开源、多模型、有 RAG。LobeChat 是 Web + 桌面双形态，可自托管 Docker，72k stars；Cherry Studio 是纯桌面（Win\u002FMac\u002FLinux\u002FAndroid），不支持 Web 部署但桌面体验更精细，60k+ stars。要 Web 访问 \u002F 公司多人共享选 LobeChat；个人重度选 Cherry Studio。",{"q":487,"a":488},"支持 MCP \u002F 插件吗？","支持 MCP（Model Context Protocol）扩展，配合自定义助手（System Prompt）可扩展工具调用、联网搜索等能力。",[490,491],"zh","en",{},true,"\u002Ftools\u002Fcoding\u002Flocal\u002Fcherry-studio","coding",[497,498,499,500],"windows","macos","linux","android",[502,506],{"plan":92,"price":503,"features":504,"notes":505},"免费","300+ 助手模板 \u002F 云端 + 本地模型 \u002F 知识库 \u002F MCP \u002F WebDAV 备份","AGPL-3.0 开源",{"plan":98,"price":507,"features":508,"notes":509},"联系销售","私有化部署 \u002F 团队协作 \u002F AI 资源管控 \u002F 知识库管理","面向企业团队","开源免费 \u002F 企业版联系销售","2026-06-19",[513,514],"onboarding\u002Frag-pipeline-build","onboarding\u002Fcursor-mcp-deep-integration",{"power":516,"ux":471,"price":471,"cn_support":471,"stability":516},4,{"title":12,"description":473},"coding\u002Flocal\u002Fcherry-studio",[520,523,525],{"name":521,"url":439,"accessed":522},"Cherry Studio 官网","2026-06-24",{"name":524,"url":446,"accessed":522},"MBLUO Studio — Cherry Studio 评测",{"name":526,"url":453,"accessed":522},"Cursor IDE 博客 — Cherry Studio 指南","tools\u002Fcoding\u002Flocal\u002Fcherry-studio","全能 AI 客户端：多模型聚合 + 本地知识库 + 300+ 助手模板，跨平台桌面应用",[470,530,531,532,533,534,535],"desktop","multi-model","knowledge-base","rag","open-source","china","国产 AI 桌面客户端第一梯队，多模型聚合 + 本地 RAG + 中文体验顶级。需要 Web 部署 \u002F 自托管选 LobeChat；只要桌面体验完整选 Cherry Studio。","https:\u002F\u002Fcherry-ai.com","LAkkQWZMoqlr7XC7II0hPyN5bwRJkF6rbuSjkfZz2eA",{"id":540,"title":207,"alternatives":541,"api_compatible":9,"body":542,"category":470,"chinese_friendly":457,"cover":973,"description":974,"domestic":474,"extension":475,"faq":975,"free":474,"github":9,"languages":988,"lastVerified":9,"meta":989,"models":9,"navigation":493,"notSuitable":9,"opensource":474,"path":415,"pillar":495,"platforms":990,"priceTable":991,"pricing":999,"published":511,"relatedPlaybooks":1000,"relatedReviews":9,"score":1002,"self_host":493,"seo":1003,"seoTitle":9,"slug":15,"sources":1004,"stem":1011,"suitable":9,"tagline":1012,"tags":1013,"updated":522,"verdict":1020,"website":1021,"__hash__":1022},"tools\u002Ftools\u002Fcoding\u002Flocal\u002Flm-studio.md",[16,17,518,14],{"type":19,"value":543,"toc":961},[544,546,554,557,559,616,618,632,637,641,645,662,666,683,685,711,713,852,854,886,888,911,913,936,938],[22,545,25],{"id":24},[27,547,548,549,553],{},"LM Studio 是 Windows \u002F macOS \u002F Linux 桌面应用，让你像浏览 App Store 一样发现、下载、运行本地大模型（GGUF \u002F MLX 格式）。底层基于 llama.cpp + MLX，Mac M 系列原生优化。0.3+ 起新增 Headless 模式 + ",[550,551,552],"code",{},"lms"," CLI，可在服务器跑 OpenAI 兼容 API（默认 :1234）。个人 \u002F 评估完全免费，商用咨询。",[27,555,556],{},"适合：本地 LLM 入门 \u002F 评估、Mac 用户、需要 GUI 调参 \u002F 模型比较、想给 IDE \u002F 应用接本地 OpenAI 兼容 endpoint 的开发者。不适合：多用户并发生产服务（用 vLLM）、嵌入式 \u002F 边缘部署（用 llama.cpp）、纯 CLI 工作流（用 Ollama）。",[22,558,35],{"id":35},[37,560,561,567,573,579,589,598,604,610],{},[40,562,563,566],{},[43,564,565],{},"模型浏览器","：内置 Hugging Face 检索，按 GGUF \u002F MLX \u002F 大小筛选、一键下载",[40,568,569,572],{},[43,570,571],{},"聊天界面","：System Prompt \u002F temperature \u002F top-p \u002F context size 可视化调参",[40,574,575,578],{},[43,576,577],{},"多模型并存 \u002F 切换","：同时加载多模型在不同会话中比较",[40,580,581,584,585,588],{},[43,582,583],{},"OpenAI 兼容 Local Server","：",[550,586,587],{},"http:\u002F\u002Flocalhost:1234\u002Fv1","，任何 SDK 即接即用",[40,590,591,584,594,597],{},[43,592,593],{},"Headless \u002F CLI",[550,595,596],{},"lms server start --port 1234","，无 GUI 可跑",[40,599,600,603],{},[43,601,602],{},"PDF \u002F 文档对话","：内置基础 RAG，丢文件就能聊",[40,605,606,609],{},[43,607,608],{},"MLX 原生支持（Mac）","：M1+ 上比 GGUF + Metal 快 30–50%",[40,611,612,615],{},[43,613,614],{},"持续批处理","：Codersera 2026 测得 50–90 tok\u002Fs（消费级 GPU + 中等模型）",[22,617,85],{"id":85},[37,619,620,626],{},[40,621,622,625],{},[43,623,624],{},"个人 \u002F 评估","：免费，全功能可用",[40,627,628,631],{},[43,629,630],{},"商用","：邮件 \u002F 官网联系 LM Studio 团队",[101,633,634],{},[27,635,636],{},"模型本身免费（开源权重），LM Studio 不抽水任何 token 费用。",[22,638,640],{"id":639},"实测mac-m2-pro-qwen3-coder-7b-gguf-q4_k_m","实测（Mac M2 Pro + Qwen3-Coder-7B GGUF Q4_K_M）",[27,642,643],{},[43,644,114],{},[37,646,647,650,653,656,659],{},[40,648,649],{},"模型浏览器极舒服：搜「qwen3-coder」直接列出 GGUF + MLX 各 quant，标硬件兼容度",[40,651,652],{},"加载 7B Q4 模型 \u003C 3 秒，生成 ~75 tok\u002Fs",[40,654,655],{},"Local Server 开了 Cursor 直接接 baseURL → 本地代码补全零成本",[40,657,658],{},"MLX 版同模型 ~110 tok\u002Fs，差距显著",[40,660,661],{},"多窗口加载 2 个模型并排测，调 prompt 直观",[27,663,664],{},[43,665,136],{},[37,667,668,671,674,677,680],{},[40,669,670],{},"模型库依赖 Hugging Face，国内访问要镜像 \u002F 代理",[40,672,673],{},"GPU 显存吃满后会自动 offload 到 CPU，无提示就慢下来",[40,675,676],{},"Headless 模式相对 Ollama 偏新，文档稍少",[40,678,679],{},"闭源应用（虽免费），不适合企业合规挂钩",[40,681,682],{},"中文 UI 可用但部分菜单仍英文",[22,684,156],{"id":156},[158,686,687,690,693,696,699,706],{},[40,688,689],{},"lmstudio.ai 下载（Mac \u002F Windows \u002F Linux）",[40,691,692],{},"打开 → Discover 标签 → 搜模型（如 qwen3-coder、deepseek-v3 GGUF\u002FMLX）→ Download",[40,694,695],{},"Chat 标签 → 选模型 → 调参聊天",[40,697,698],{},"Local Server 标签 → Start Server → 默认端口 1234",[40,700,701,702,705],{},"在你的应用里：",[550,703,704],{},"baseURL = \"http:\u002F\u002Flocalhost:1234\u002Fv1\"","，API Key 任意",[40,707,708,709],{},"Headless：",[550,710,596],{},[22,712,186],{"id":186},[188,714,715,731],{},[191,716,717],{},[194,718,719,721,723,726,728],{},[197,720,199],{},[197,722,207],{},[197,724,725],{},"Ollama",[197,727,210],{},[197,729,730],{},"llama.cpp",[212,732,733,749,765,780,795,809,823,836],{},[194,734,735,737,740,743,746],{},[217,736,219],{},[217,738,739],{},"GUI + CLI",[217,741,742],{},"CLI Daemon",[217,744,745],{},"Docker UI",[217,747,748],{},"二进制",[194,750,751,754,757,760,762],{},[217,752,753],{},"模型浏览",[217,755,756],{},"✅ 内置",[217,758,759],{},"CLI pull",[217,761,287],{},[217,763,764],{},"手动",[194,766,767,770,772,775,778],{},[217,768,769],{},"参数调优 GUI",[217,771,260],{},[217,773,774],{},"❌",[217,776,777],{},"部分",[217,779,774],{},[194,781,782,785,788,791,793],{},[217,783,784],{},"OpenAI 兼容 API",[217,786,787],{},"✅ :1234",[217,789,790],{},"✅ :11434",[217,792,260],{},[217,794,260],{},[194,796,797,800,802,805,807],{},[217,798,799],{},"MLX (Mac)",[217,801,260],{},[217,803,804],{},"✅ 0.19+",[217,806,334],{},[217,808,334],{},[194,810,811,814,816,818,820],{},[217,812,813],{},"多用户并发",[217,815,257],{},[217,817,257],{},[217,819,260],{},[217,821,822],{},"中",[194,824,825,828,830,832,834],{},[217,826,827],{},"开源",[217,829,318],{},[217,831,315],{},[217,833,315],{},[217,835,315],{},[194,837,838,841,844,847,849],{},[217,839,840],{},"上手难度",[217,842,843],{},"极低",[217,845,846],{},"低",[217,848,822],{},[217,850,851],{},"高",[22,853,340],{"id":340},[37,855,856,862,868,874,880],{},[40,857,858,861],{},[43,859,860],{},"国内下模型走镜像","：HF 直连慢 \u002F 卡，配 HF_ENDPOINT=hf-mirror.com",[40,863,864,867],{},[43,865,866],{},"显存爆 ≠ 报错","：GPU 装不下会无声 offload 到 CPU，关注生成速度，必要时降 quant 或换小模型",[40,869,870,873],{},[43,871,872],{},"MLX 优先（Mac M 系列）","：能下 MLX 版就别下 GGUF，速度差距明显",[40,875,876,879],{},[43,877,878],{},"Local Server 暴露要谨慎","：默认 0.0.0.0 + 无鉴权，对外开放前加反代 + Bearer",[40,881,882,885],{},[43,883,884],{},"闭源合规要核","：企业内部使用前查 license；商用必须联系官方",[22,887,376],{"id":375},[37,889,890,893,896,899,902,905,908],{},[40,891,892],{},"✅ 本地 LLM 入门 \u002F 评估",[40,894,895],{},"✅ Mac M 系列用户",[40,897,898],{},"✅ 想给 Cursor \u002F Cline 接本地 OpenAI 兼容 endpoint",[40,900,901],{},"✅ 需要 GUI 调参 \u002F 模型比较",[40,903,904],{},"❌ 多用户并发生产服务",[40,906,907],{},"❌ 嵌入式 \u002F 边缘设备",[40,909,910],{},"❌ 强合规 \u002F 必须开源审计",[22,912,402],{"id":402},[37,914,915,919,925,930],{},[40,916,917],{},[170,918,422],{"href":421},[40,920,921],{},[170,922,924],{"href":923},"\u002Ftools\u002Fcoding\u002Flocal\u002Fopen-webui","Open WebUI 评测",[40,926,927],{},[170,928,929],{"href":494},"Cherry Studio 评测",[40,931,932],{},[170,933,935],{"href":934},"\u002Fplaybook\u002Fonboarding\u002Fclaude-code-getting-started","Claude Code 上手 Playbook",[22,937,431],{"id":431},[158,939,940,947,954],{},[40,941,942,943],{},"LM Studio 官网 ",[170,944,945],{"href":945,"rel":946},"https:\u002F\u002Flmstudio.ai\u002F",[174],[40,948,949,950],{},"Codersera — LM Studio Complete Guide 2026 ",[170,951,952],{"href":952,"rel":953},"https:\u002F\u002Fcodersera.com\u002Fblog\u002Flm-studio-complete-guide-2026\u002F",[174],[40,955,956,957],{},"Codersera — Ollama vs LM Studio vs vLLM vs llama.cpp vs MLX 2026 ",[170,958,959],{"href":959,"rel":960},"https:\u002F\u002Fcodersera.com\u002Fblog\u002Follama-vs-lm-studio-vs-vllm-vs-llama-cpp-vs-mlx-2026\u002F",[174],{"title":456,"searchDepth":457,"depth":457,"links":962},[963,964,965,966,967,968,969,970,971,972],{"id":24,"depth":460,"text":25},{"id":35,"depth":460,"text":35},{"id":85,"depth":460,"text":85},{"id":639,"depth":460,"text":640},{"id":156,"depth":460,"text":156},{"id":186,"depth":460,"text":186},{"id":340,"depth":460,"text":340},{"id":375,"depth":460,"text":376},{"id":402,"depth":460,"text":402},{"id":431,"depth":460,"text":431},"\u002Fimg\u002Ftools\u002Flm-studio.webp","LM Studio 真实评测：跨平台桌面应用，运行本地 GGUF \u002F MLX 大模型。50–90 tok\u002Fs 持续批处理、OpenAI 兼容本地 API（默认端口 1234）、Headless 模式、Mac \u002F Win 双端。对个人开发者免费，企业咨询。",[976,979,982,985],{"q":977,"a":978},"和 Ollama 怎么选？","LM Studio 是 GUI 优先（模型浏览器 + 参数面板 + 聊天界面），适合个人 \u002F 评估 \u002F 上手。Ollama 是 CLI \u002F Daemon 优先（后台跑 + REST API），适合应用嵌入 \u002F 脚本调用。两者都基于 llama.cpp，在 Mac M 系列上都已用 MLX。",{"q":980,"a":981},"支持 MLX 吗？","支持。Mac M1+ 上可加载 MLX 格式模型，速度比 GGUF + Metal 快 30–50%。模型搜索时筛选 MLX 即可。",{"q":983,"a":984},"OpenAI 兼容 API 怎么用？","开 Local Server → 默认端口 1234 → `http:\u002F\u002Flocalhost:1234\u002Fv1`。任何 OpenAI SDK 把 baseURL 改这个就能跑本地模型，零代码改动。",{"q":986,"a":987},"Headless 模式？","0.3+ 起支持 `lms server start` CLI 启动后台服务，无 GUI 即可跑 OpenAI 兼容 API，适合服务器 \u002F SSH 场景。",[491,490],{},[497,498,499],[992,995],{"plan":624,"price":503,"features":993,"notes":994},"全功能 GUI + Headless API + GGUF\u002FMLX","供个人 \u002F 评估使用",{"plan":630,"price":996,"features":997,"notes":998},"联系咨询","团队部署 \u002F 商用 license","邮件 \u002F 官网联系","免费（个人 \u002F 评估） \u002F 企业 \u002F 商用咨询",[513,1001],"onboarding\u002Fclaude-code-getting-started",{"power":516,"ux":471,"price":471,"cn_support":457,"stability":516},{"title":207,"description":974},[1005,1007,1009],{"name":1006,"url":945,"accessed":522},"LM Studio 官网",{"name":1008,"url":952,"accessed":522},"Codersera — LM Studio Complete Guide 2026",{"name":1010,"url":959,"accessed":522},"Codersera — Ollama vs LM Studio vs vLLM 2026","tools\u002Fcoding\u002Flocal\u002Flm-studio","本地 LLM 的 GUI 首选——模型浏览器 + GGUF\u002FMLX 推理 + OpenAI 兼容 API + Mac 原生优化",[470,1014,1015,1016,1017,1018,1019],"gui","gguf","mlx","llama-cpp","mac","openai-compatible","Mac \u002F Windows 桌面本地 LLM 的 GUI 首选——上手最快、模型浏览最舒服、自带 OpenAI 兼容 API。批量服务 \u002F 多用户场景用 vLLM；纯 CLI \u002F 嵌入应用走 Ollama。","https:\u002F\u002Flmstudio.ai","aNdpYFeU-2Jf8ElPrgRWVGVb70JZJ6MzrXXg5Tc6t3s",1784565442325]