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服务器；桌面版数据本地存、隐私好；Docker 自托管对团队 \u002F 公司部署最优，完全掌控数据。",{"q":544,"a":545},"支持哪些模型？","80+ 模型：OpenAI 全系列、Anthropic Claude、Google Gemini、DeepSeek、Qwen、Kimi、Moonshot、字节豆包、Groq、Together、OpenRouter、Ollama \u002F LM Studio 本地模型，以及任何 OpenAI 兼容 API。",{"q":547,"a":548},"多模型对比怎么用？","同一对话窗口里把消息广播给多个模型并排回答，选型 \u002F 评估特别有用——直接看 Claude 和 GPT 在同一 prompt 下的回答差异。",{"q":550,"a":551},"助手市场是什么？","LobeHub 维护的预设 AI 角色市场（代码审查 \u002F 翻译 \u002F 写作 \u002F 角色扮演等几百个），一键拉到本地用，省去自己写 System Prompt。",[553,554],"zh","en","2026-08-02",{},null,"\u002Ftools\u002Fcoding\u002Flocal\u002Flobe-chat","coding",[561,562,563,564,565],"web","windows","macos","linux","docker",[567,571],{"plan":132,"price":568,"features":569,"notes":570},"免费","全功能 \u002F 80+ 模型 \u002F 知识库 \u002F 插件 \u002F 助手市场","MIT 协议",{"plan":110,"price":572,"features":573,"notes":574},"订阅制","云端托管 \u002F 免部署 \u002F 团队协作 \u002F 同步","chat.lobehub.com 注册即用","完全免费（MIT 开源） \u002F LobeHub Cloud 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同样优秀。","https:\u002F\u002Flobehub.com","sAhQ5CHDobJa--z6NbtgysTQDwTBYsoGPXR0zat-5-w",[606,1049,1518,2003],{"id":607,"title":243,"alternatives":608,"api_compatible":557,"body":609,"category":533,"chinese_friendly":534,"cover":1000,"description":1001,"domestic":537,"extension":538,"faq":1002,"free":537,"github":1015,"languages":1016,"lastVerified":555,"meta":1017,"models":557,"navigation":537,"notSuitable":557,"opensource":537,"path":471,"pillar":559,"platforms":1018,"priceTable":1020,"pricing":1028,"published":576,"relatedPlaybooks":1029,"relatedReviews":557,"score":1031,"self_host":537,"seo":1032,"seoTitle":1033,"slug":12,"sources":1034,"stem":1041,"suitable":557,"tagline":1042,"tags":1043,"updated":588,"verdict":1046,"website":1047,"__hash__":1048},"tools\u002Ftools\u002Fcoding\u002Flocal\u002Fcherry-studio.md",[584,15,14,13],{"type":33,"value":610,"toc":988},[611,613,616,619,621,662,664,678,684,688,692,709,713,730,732,756,758,881,883,915,917,940,942,963,965],[36,612,39],{"id":38},[41,614,615],{},"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。",[41,617,618],{},"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。",[36,620,54],{"id":54},[56,622,623,628,633,639,644,650,656],{},[59,624,625,627],{},[45,626,63],{},"：OpenAI \u002F Claude \u002F Gemini \u002F DeepSeek \u002F Qwen \u002F Kimi \u002F Moonshot 等云端 + Ollama \u002F LM Studio 本地",[59,629,630,632],{},[45,631,75],{},"：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",[59,634,635,638],{},[45,636,637],{},"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",[59,640,641,643],{},[45,642,93],{},"：扩展工具调用 \u002F 联网搜索 \u002F 文件操作",[59,645,646,649],{},[45,647,648],{},"数据本地优先","：对话历史本地存储，WebDAV 同步，不上传第三方",[59,651,652,655],{},[45,653,654],{},"多模态","：图片识别 \u002F PDF 阅读 \u002F Markdown + Mermaid + 代码高亮",[59,657,658,661],{},[45,659,660],{},"AI 绘画 + 翻译","：内置主流 SD \u002F DALL·E \u002F 翻译 API 集成",[36,663,125],{"id":125},[56,665,666,672],{},[59,667,668,671],{},[45,669,670],{},"开源版","：完全免费，AGPL-3.0",[59,673,674,677],{},[45,675,676],{},"Enterprise","：私有化部署 + 团队协作 + 资源管控，联系销售",[679,680,681],"blockquote",{},[41,682,683],{},"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。",[36,685,687],{"id":686},"实测mac-m2-中型知识库","实测（Mac M2 + 中型知识库）",[41,689,690],{},[45,691,150],{},[56,693,694,697,700,703,706],{},[59,695,696],{},"中文 UI \u002F 文档 \u002F 社区都顶级，零门槛上手",[59,698,699],{},"本地 RAG 拖入 30+ PDF 后向量化 \u003C 2 分钟（用 bge-m3）",[59,701,702],{},"多模型并排回答：让 Claude \u002F GPT \u002F DeepSeek 同回一个问题做比较",[59,704,705],{},"MCP 接 Brave Search + 自定义工具流畅",[59,707,708],{},"WebDAV 同步坚果云 \u002F 阿里云盘，桌面 + 移动设备数据互通",[41,710,711],{},[45,712,175],{},[56,714,715,718,721,724,727],{},[59,716,717],{},"没有 Web 端 \u002F Docker 自托管（要这个用 LobeChat）",[59,719,720],{},"iOS 版尚未发布（roadmap 中）",[59,722,723],{},"大型 PDF（>100 MB）向量化偶有失败，要切小",[59,725,726],{},"助手市场质量参差，要自筛",[59,728,729],{},"模型 API 调用全靠你自己付费，新手要先理解 API Key 概念",[36,731,195],{"id":195},[197,733,734,737,740,747,750,753],{},[59,735,736],{},"cherry-ai.com 下载客户端（或 GitHub releases）",[59,738,739],{},"设置 → 模型服务 → 填 OpenAI \u002F Claude \u002F DeepSeek API Key",[59,741,742,743],{},"（可选）本地：装 Ollama → Cherry Studio 自动识别 endpoint ",[469,744,745],{"href":745,"rel":746},"http:\u002F\u002Flocalhost:11434",[503],[59,748,749],{},"新建知识库 → 拖文件 \u002F 加网址 → 等向量化",[59,751,752],{},"新对话 → 选模型 → 勾知识库 → 提问",[59,754,755],{},"进阶：自定义助手（System Prompt）+ MCP 扩展工具",[36,757,225],{"id":225},[227,759,760,774],{},[230,761,762],{},[233,763,764,766,768,770,772],{},[236,765,238],{},[236,767,243],{},[236,769,10],{},[236,771,249],{},[236,773,246],{},[251,775,776,789,802,815,827,842,857,869],{},[233,777,778,780,782,785,787],{},[256,779,258],{},[256,781,263],{},[256,783,784],{},"Web + 桌面",[256,786,263],{},[256,788,266],{},[233,790,791,793,796,798,800],{},[256,792,63],{},[256,794,795],{},"✅ 云 + 本地",[256,797,795],{},[256,799,283],{},[256,801,795],{},[233,803,804,806,809,811,813],{},[256,805,302],{},[256,807,808],{},"✅ 强",[256,810,808],{},[256,812,295],{},[256,814,278],{},[233,816,817,819,821,823,825],{},[256,818,332],{},[256,820,278],{},[256,822,278],{},[256,824,295],{},[256,826,278],{},[233,828,829,832,835,838,840],{},[256,830,831],{},"自托管 \u002F Web",[256,833,834],{},"无 Web",[256,836,837],{},"✅ Docker",[256,839,352],{},[256,841,837],{},[233,843,844,847,850,852,855],{},[256,845,846],{},"中文",[256,848,849],{},"5\u002F5",[256,851,849],{},[256,853,854],{},"4\u002F5",[256,856,854],{},[233,858,859,861,863,865,867],{},[256,860,378],{},[256,862,384],{},[256,864,381],{},[256,866,389],{},[256,868,381],{},[233,870,871,873,875,877,879],{},[256,872,361],{},[256,874,367],{},[256,876,364],{},[256,878,373],{},[256,880,370],{},[36,882,392],{"id":392},[56,884,885,891,897,903,909],{},[59,886,887,890],{},[45,888,889],{},"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",[59,892,893,896],{},[45,894,895],{},"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",[59,898,899,902],{},[45,900,901],{},"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",[59,904,905,908],{},[45,906,907],{},"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",[59,910,911,914],{},[45,912,913],{},"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",[36,916,434],{"id":433},[56,918,919,922,925,928,931,934,937],{},[59,920,921],{},"✅ 中文用户、AI 重度使用 \u002F 多模型管理",[59,923,924],{},"✅ 需要本地 RAG 知识库",[59,926,927],{},"✅ 关注数据隐私 \u002F 本地存储",[59,929,930],{},"✅ 想用 Ollama \u002F LM Studio 本地模型",[59,932,933],{},"❌ 需要 Web 端 \u002F Docker 自托管",[59,935,936],{},"❌ 团队多人共享 \u002F SSO",[59,938,939],{},"❌ iOS 主力用户",[36,941,463],{"id":463},[56,943,944,949,955,959],{},[59,945,946],{},[469,947,948],{"href":558},"LobeChat 评测",[59,950,951],{},[469,952,954],{"href":953},"\u002Ftools\u002Fcoding\u002Flocal\u002Flm-studio","LM Studio 评测",[59,956,957],{},[469,958,484],{"href":483},[59,960,961],{},[469,962,490],{"href":489},[36,964,493],{"id":493},[197,966,967,974,981],{},[59,968,969,970],{},"Cherry Studio 官网（功能 + 下载）",[469,971,972],{"href":972,"rel":973},"https:\u002F\u002Fwww.cherry-ai.com\u002F",[503],[59,975,976,977],{},"MBLUO Studio — Cherry Studio 评测 2026 ",[469,978,979],{"href":979,"rel":980},"https:\u002F\u002Fmbluostudio.com\u002Ftools\u002Fcherry-studio",[503],[59,982,983,984],{},"Cursor IDE 博客 — Cherry Studio 完全指南（2025-03）",[469,985,986],{"href":986,"rel":987},"https:\u002F\u002Fwww.cursor-ide.com\u002Fblog\u002Fcherry-studio-guide",[503],{"title":519,"searchDepth":520,"depth":520,"links":989},[990,991,992,993,994,995,996,997,998,999],{"id":38,"depth":523,"text":39},{"id":54,"depth":523,"text":54},{"id":125,"depth":523,"text":125},{"id":686,"depth":523,"text":687},{"id":195,"depth":523,"text":195},{"id":225,"depth":523,"text":225},{"id":392,"depth":523,"text":392},{"id":433,"depth":523,"text":434},{"id":463,"depth":523,"text":463},{"id":493,"depth":523,"text":493},"\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。",[1003,1006,1009,1012],{"q":1004,"a":1005},"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":1007,"a":1008},"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":1010,"a":1011},"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":1013,"a":1014},"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。","https:\u002F\u002Fgithub.com\u002FCherryHQ\u002Fcherry-studio",[553,554],{},[562,563,564,1019],"android",[1021,1024],{"plan":670,"price":568,"features":1022,"notes":1023},"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":676,"price":1025,"features":1026,"notes":1027},"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",[578,1030],"onboarding\u002Fcursor-mcp-deep-integration",{"power":581,"ux":534,"price":534,"cn_support":534,"stability":581},{"title":243,"description":1001},"Cherry Studio 评测 2026：AI 客户端工具，多模型桌面助手，开源免费",[1035,1037,1039],{"name":1036,"url":972,"accessed":588},"Cherry Studio 官网",{"name":1038,"url":979,"accessed":588},"MBLUO Studio — Cherry Studio 评测",{"name":1040,"url":986,"accessed":588},"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",[533,596,597,1044,598,601,1045],"knowledge-base","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","nhG3iaQ6G7dAWmMarVHUYX3EU47pSjb4yoUqlqZy18k",{"id":1050,"title":246,"alternatives":1051,"api_compatible":1052,"body":1053,"category":533,"chinese_friendly":581,"cover":1469,"description":1470,"domestic":1471,"extension":538,"faq":1472,"free":537,"github":1485,"languages":1486,"lastVerified":555,"meta":1487,"models":557,"navigation":537,"notSuitable":557,"opensource":537,"path":477,"pillar":559,"platforms":1488,"priceTable":1490,"pricing":1497,"published":576,"relatedPlaybooks":1498,"relatedReviews":557,"score":1499,"self_host":537,"seo":1500,"seoTitle":1501,"slug":13,"sources":1502,"stem":1509,"suitable":557,"tagline":1510,"tags":1511,"updated":588,"verdict":1515,"website":1516,"__hash__":1517},"tools\u002Ftools\u002Fcoding\u002Flocal\u002Fopen-webui.md",[584,12,14,15],[17,18,19,20,21,22,23,24,25,26,27,28,29,30,31],{"type":33,"value":1054,"toc":1457},[1055,1057,1060,1063,1065,1127,1129,1132,1136,1140,1168,1172,1197,1199,1233,1235,1342,1344,1387,1389,1412,1414,1432,1434],[36,1056,39],{"id":38},[41,1058,1059],{},"Open WebUI（原 Ollama WebUI）是 MIT 开源、自托管 AI 平台，最常见用法是 Docker 跑起来给 Ollama 套一个 ChatGPT 风格前端。GitHub 126k+ stars、282M+ Docker pulls，事实上的本地 AI 前端首选。支持任意 OpenAI 兼容后端 + RAG 知识库 + 多用户账号 + 工具调用 + MCP-OpenAPI 代理 + 联网搜索 + 语音 + 图像生成。",[41,1061,1062],{},"适合：团队 \u002F 家庭 \u002F 公司部署一份共享、要 Web 端访问、多用户分账号、SearXNG 联网搜索、Confluence \u002F S3 \u002F GitHub 数据源同步。不适合：单人桌面体验（用 Cherry Studio）、零运维 \u002F 不愿碰 Docker。",[36,1064,54],{"id":54},[56,1066,1067,1073,1079,1085,1091,1097,1103,1109,1115,1121],{},[59,1068,1069,1072],{},[45,1070,1071],{},"多模型后端","：Ollama \u002F OpenAI \u002F vLLM \u002F Anthropic \u002F Groq \u002F LocalAI \u002F 任意 OpenAI 兼容",[59,1074,1075,1078],{},[45,1076,1077],{},"多用户 + RBAC","：注册 \u002F 邀请 \u002F 角色权限 \u002F 工作区隔离",[59,1080,1081,1084],{},[45,1082,1083],{},"RAG 知识库","：上传文档 \u002F 网址 \u002F SearXNG 联网搜索 → 向量化 → 对话引用",[59,1086,1087,1090],{},[45,1088,1089],{},"Tools \u002F Functions","：Python 写函数即扩展（联网 \u002F 计算器 \u002F 自定义 API）",[59,1092,1093,1096],{},[45,1094,1095],{},"mcpo","：MCP-to-OpenAPI 代理，任意 MCP 服务器接进来",[59,1098,1099,1102],{},[45,1100,1101],{},"oikb","：知识库同步本地文件夹 \u002F GitHub \u002F S3 \u002F Confluence 等 40+ 源",[59,1104,1105,1108],{},[45,1106,1107],{},"open-terminal \u002F cptr","：给 AI 真实终端 + 文件 + 沙箱执行",[59,1110,1111,1114],{},[45,1112,1113],{},"图像生成","：Stable Diffusion \u002F DALL·E \u002F 自托管接入",[59,1116,1117,1120],{},[45,1118,1119],{},"语音输入 \u002F TTS","：内置",[59,1122,1123,1126],{},[45,1124,1125],{},"企业 LTS","：custom branding + SLA + 长期支持版本（联系销售）",[36,1128,125],{"id":125},[41,1130,1131],{},"完全免费、MIT 开源、商用免费。Enterprise 提供品牌定制 + SLA + LTS。",[36,1133,1135],{"id":1134},"实测ubuntu-2404-ollama-后端-5-人小团队","实测（Ubuntu 24.04 + Ollama 后端 + 5 人小团队）",[41,1137,1138],{},[45,1139,150],{},[56,1141,1142,1149,1152,1159,1162,1165],{},[59,1143,1144,1145,1148],{},"单条 ",[119,1146,1147],{},"docker run"," 五分钟上线",[59,1150,1151],{},"自带的多用户 + 角色权限省去重新搭 Auth",[59,1153,1154,1155,1158],{},"RAG 直传 30 个 PDF 后向量化顺利，对话中 ",[119,1156,1157],{},"#知识库"," 引用准确",[59,1160,1161],{},"mcpo 把 GitHub MCP 服务器接进来，团队对话里直接 issue \u002F PR 操作",[59,1163,1164],{},"模型切换流畅，OpenAI + Ollama 并存",[59,1166,1167],{},"SearXNG 联网搜索给模型实时信息，过时知识截止问题缓解",[41,1169,1170],{},[45,1171,175],{},[56,1173,1174,1177,1184,1191,1194],{},[59,1175,1176],{},"Docker 镜像 ~1.5GB，首次拉取偏慢",[59,1178,1179,1180,1183],{},"默认 ",[119,1181,1182],{},"0.0.0.0"," 公网暴露要加 HTTPS + 反代",[59,1185,1186,1187,1190],{},"嵌入模型 ",[119,1188,1189],{},"sentence-transformers"," 中文效果一般，建议换 bge-m3",[59,1192,1193],{},"多用户共享 Ollama 时并发吞吐瓶颈在 Ollama，不在 Open WebUI（生产用 vLLM 后端）",[59,1195,1196],{},"版本升级要看 changelog，部分 minor 含 breaking 改动",[36,1198,195],{"id":195},[197,1200,1201,1207,1214,1217,1220,1223,1226],{},[59,1202,1203,1204],{},"装 Docker → ",[119,1205,1206],{},"docker run -d -p 3000:8080 --add-host=host.docker.internal:host-gateway -v open-webui:\u002Fapp\u002Fbackend\u002Fdata --name open-webui --restart always ghcr.io\u002Fopen-webui\u002Fopen-webui:main",[59,1208,1209,1210,1213],{},"浏览器开 ",[119,1211,1212],{},"http:\u002F\u002Flocalhost:3000"," → 注册第一个账号（管理员）",[59,1215,1216],{},"设置 → Connections → 连接 Ollama \u002F 加 OpenAI Key",[59,1218,1219],{},"Models → Pull \u002F Discover 模型",[59,1221,1222],{},"Workspaces → 建知识库 → 上传文档",[59,1224,1225],{},"Tools → 启用 \u002F 写自定义函数",[59,1227,1228,1229,1232],{},"生产部署：Nginx 反代 + Let's Encrypt + 备份 ",[119,1230,1231],{},"\u002Fapp\u002Fbackend\u002Fdata"," volume",[36,1234,225],{"id":225},[227,1236,1237,1251],{},[230,1238,1239],{},[233,1240,1241,1243,1245,1247,1249],{},[236,1242,238],{},[236,1244,246],{},[236,1246,10],{},[236,1248,243],{},[236,1250,249],{},[251,1252,1253,1265,1277,1291,1304,1318,1330],{},[233,1254,1255,1257,1259,1261,1263],{},[256,1256,258],{},[256,1258,266],{},[256,1260,784],{},[256,1262,263],{},[256,1264,263],{},[233,1266,1267,1269,1271,1273,1275],{},[256,1268,346],{},[256,1270,290],{},[256,1272,278],{},[256,1274,352],{},[256,1276,352],{},[233,1278,1279,1282,1285,1287,1289],{},[256,1280,1281],{},"RAG",[256,1283,1284],{},"✅ 强 + oikb",[256,1286,278],{},[256,1288,278],{},[256,1290,295],{},[233,1292,1293,1296,1298,1300,1302],{},[256,1294,1295],{},"工具 \u002F MCP",[256,1297,339],{},[256,1299,278],{},[256,1301,278],{},[256,1303,295],{},[233,1305,1306,1309,1312,1314,1316],{},[256,1307,1308],{},"自托管",[256,1310,1311],{},"✅ Docker \u002F K8s",[256,1313,837],{},[256,1315,352],{},[256,1317,352],{},[233,1319,1320,1322,1324,1326,1328],{},[256,1321,361],{},[256,1323,370],{},[256,1325,364],{},[256,1327,367],{},[256,1329,373],{},[233,1331,1332,1334,1336,1338,1340],{},[256,1333,378],{},[256,1335,381],{},[256,1337,381],{},[256,1339,384],{},[256,1341,389],{},[36,1343,392],{"id":392},[56,1345,1346,1352,1360,1369,1375,1381],{},[59,1347,1348,1351],{},[45,1349,1350],{},"不要裸 0.0.0.0 + HTTP 暴露公网","：默认无 HTTPS，必上反代 + 强密码 + 速率限制",[59,1353,1354,1359],{},[45,1355,1356,1357,1232],{},"备份 ",[119,1358,1231],{},"：知识库 \u002F 用户 \u002F 对话全在里面",[59,1361,1362,1365,1366,1368],{},[45,1363,1364],{},"中文 RAG 换嵌入模型","：默认 ",[119,1367,1189],{}," 中文一般，配 bge-m3 或硅基流动嵌入 API",[59,1370,1371,1374],{},[45,1372,1373],{},"mcpo 工具范围谨慎","：MCP 给 AI 真实能力，第三方服务器审一遍",[59,1376,1377,1380],{},[45,1378,1379],{},"后端吞吐看 Ollama","：5+ 并发上 vLLM 后端，Ollama 单 worker 会排队",[59,1382,1383,1386],{},[45,1384,1385],{},"升级前看 changelog","：weekly 更新，偶有 breaking",[36,1388,434],{"id":433},[56,1390,1391,1394,1397,1400,1403,1406,1409],{},[59,1392,1393],{},"✅ 团队 \u002F 家庭 \u002F 公司多人共享 AI 平台",[59,1395,1396],{},"✅ 要 Web 端访问 \u002F 移动端兼容",[59,1398,1399],{},"✅ 自托管 \u002F 完全控制数据",[59,1401,1402],{},"✅ MCP \u002F 工具调用刚需",[59,1404,1405],{},"❌ 单人桌面体验（用 Cherry Studio）",[59,1407,1408],{},"❌ 零运维 \u002F 不愿碰 Docker",[59,1410,1411],{},"❌ iOS 原生 App 主力",[36,1413,463],{"id":463},[56,1415,1416,1420,1424,1428],{},[59,1417,1418],{},[469,1419,948],{"href":558},[59,1421,1422],{},[469,1423,472],{"href":471},[59,1425,1426],{},[469,1427,484],{"href":483},[59,1429,1430],{},[469,1431,490],{"href":489},[36,1433,493],{"id":493},[197,1435,1436,1443,1450],{},[59,1437,1438,1439],{},"Open WebUI 官方文档 ",[469,1440,1441],{"href":1441,"rel":1442},"https:\u002F\u002Fdocs.openwebui.com\u002F",[503],[59,1444,1445,1446],{},"Local AI Master — Open WebUI Setup Guide 2026 ",[469,1447,1448],{"href":1448,"rel":1449},"https:\u002F\u002Flocalaimaster.com\u002Fblog\u002Fopen-webui-setup-guide",[503],[59,1451,1452,1453],{},"AIToolDiscovery — Set Up Open-WebUI with Ollama 2026 ",[469,1454,1455],{"href":1455,"rel":1456},"https:\u002F\u002Fwww.aitooldiscovery.com\u002Fhow-to\u002Fsetup-open-webui-ollama",[503],{"title":519,"searchDepth":520,"depth":520,"links":1458},[1459,1460,1461,1462,1463,1464,1465,1466,1467,1468],{"id":38,"depth":523,"text":39},{"id":54,"depth":523,"text":54},{"id":125,"depth":523,"text":125},{"id":1134,"depth":523,"text":1135},{"id":195,"depth":523,"text":195},{"id":225,"depth":523,"text":225},{"id":392,"depth":523,"text":392},{"id":433,"depth":523,"text":434},{"id":463,"depth":523,"text":463},{"id":493,"depth":523,"text":493},"\u002Fimg\u002Ftools\u002Fopen-webui.webp","Open WebUI 2026 真实评测：MIT 开源、自托管 ChatGPT 替代和 Ollama Web 前端。支持 Docker 一行部署、Ollama\u002FOpenAI\u002FvLLM 多后端、RAG 知识库、多用户、联网搜索、工具调用和 MCP-to-OpenAPI，适合团队私有 AI 平台。",false,[1473,1476,1479,1482],{"q":1474,"a":1475},"Docker 一行命令真的够用吗？","够。`docker run -d -p 3000:8080 --add-host=host.docker.internal:host-gateway -v open-webui:\u002Fapp\u002Fbackend\u002Fdata --name open-webui --restart always ghcr.io\u002Fopen-webui\u002Fopen-webui:main`，5 分钟可上线、能多人注册、能接 Ollama \u002F OpenAI。生产再加反代 + HTTPS + 备份。",{"q":1477,"a":1478},"支持哪些模型后端？","Ollama（首选）+ 任何 OpenAI 兼容 endpoint：OpenAI 官方 \u002F Anthropic（OpenAI 兼容代理）\u002F vLLM \u002F Groq \u002F LocalAI \u002F 自建 baseURL。可同时配多个，对话中切换。",{"q":1480,"a":1481},"RAG \u002F 知识库怎么做？","内置：上传 PDF \u002F DOCX \u002F TXT、网址抓取、SearXNG 联网搜索 → 自动向量化 → 在对话中 `#` 引用知识库。配套 oikb 项目可同步本地文件夹 \u002F GitHub \u002F S3 \u002F Confluence 等 40+ 数据源。",{"q":1483,"a":1484},"MCP 怎么接？","通过 mcpo（官方的 MCP-to-OpenAPI 代理）把任意 MCP 服务器暴露成 OpenAPI 工具，再在 Open WebUI 注册即可。无需写 glue code。","https:\u002F\u002Fgithub.com\u002Fopen-webui\u002Fopen-webui",[554,553],{},[565,564,563,562,1489],"kubernetes",[1491,1493],{"plan":670,"price":568,"features":1492,"notes":570},"全功能 \u002F 多用户 \u002F RAG \u002F Tools \u002F 联网搜索 \u002F MCP-OpenAPI 代理 \u002F Docker \u002F K8s",{"plan":676,"price":1494,"features":1495,"notes":1496},"咨询","Custom branding \u002F SLA \u002F LTS 长期支持版本","邮件官方","完全免费（MIT 开源） \u002F Enterprise SLA 联系",[578,579],{"power":534,"ux":581,"price":534,"cn_support":581,"stability":534},{"title":246,"description":1470},"Open WebUI 评测 2026：自托管 ChatGPT 替代，Ollama 前端部署指南",[1503,1505,1507],{"name":1504,"url":1441,"accessed":588},"Open WebUI 官方文档",{"name":1506,"url":1448,"accessed":588},"Local AI Master — Open WebUI Setup Guide 2026",{"name":1508,"url":1455,"accessed":588},"AIToolDiscovery — Open-WebUI with Ollama 2026","tools\u002Fcoding\u002Flocal\u002Fopen-webui","自托管的 ChatGPT 替代：Ollama \u002F OpenAI 兼容、多用户、RAG、126k+ GitHub stars",[533,1512,565,598,1513,1514,601],"self-host","multi-user","ollama","自托管多用户 AI 前端的事实标准。团队 \u002F 家庭 \u002F 公司部署一份共享，多模型聚合 + RAG + 工具调用全有。单机 \u002F 桌面体验首选 Cherry Studio \u002F LobeChat。","https:\u002F\u002Fdocs.openwebui.com","5Y-zy_XMSVV_gsS0aa6V7zk268r2TvaIcssIp-HXM_U",{"id":1519,"title":30,"alternatives":1520,"api_compatible":1521,"body":1522,"category":533,"chinese_friendly":520,"cover":1955,"description":1956,"domestic":1471,"extension":538,"faq":1957,"free":537,"github":1970,"languages":1971,"lastVerified":555,"meta":1972,"models":557,"navigation":537,"notSuitable":557,"opensource":537,"path":483,"pillar":559,"platforms":1973,"priceTable":1974,"pricing":1978,"published":576,"relatedPlaybooks":1979,"relatedReviews":557,"score":1980,"self_host":537,"seo":1981,"seoTitle":1982,"slug":14,"sources":1983,"stem":1990,"suitable":557,"tagline":1991,"tags":1992,"updated":588,"verdict":2000,"website":2001,"__hash__":2002},"tools\u002Ftools\u002Fcoding\u002Flocal\u002Follama.md",[15,13,12,584],[17,18,19,20,21,22,23,24,25,26,27,28,29,30,31],{"type":33,"value":1523,"toc":1943},[1524,1526,1533,1536,1538,1608,1610,1613,1617,1621,1641,1645,1675,1677,1711,1713,1839,1841,1873,1875,1898,1900,1918,1920],[36,1525,39],{"id":38},[41,1527,1528,1529,1532],{},"Ollama 是本地 LLM 的 Daemon 事实标准——后台跑、暴露 REST API（11434）+ CLI、Modelfile 配置、GGUF 一站式。MIT 开源，跨 Win \u002F Mac \u002F Linux。0.19+ 起 Mac M 系列底层切 MLX 推理。模型库覆盖 Llama \u002F Qwen \u002F DeepSeek \u002F Gemma \u002F Mistral 等主流开源模型，",[119,1530,1531],{},"ollama pull"," 一键拉。",[41,1534,1535],{},"适合：给 Cursor \u002F Cline \u002F Continue \u002F Open WebUI 接本地 OpenAI 兼容 endpoint、个人 \u002F 评估 \u002F 原型、嵌入应用、自动化脚本。不适合：GUI 偏好用户（用 LM Studio）、多用户并发生产服务（用 vLLM）、模型浏览 \u002F 调参界面（用 LM Studio）。",[36,1537,54],{"id":54},[56,1539,1540,1546,1555,1561,1569,1584,1590,1596,1602],{},[59,1541,1542,1545],{},[45,1543,1544],{},"后台 Daemon","：开机自启，应用调用零延迟",[59,1547,1548,1551,1552],{},[45,1549,1550],{},"CLI","：",[119,1553,1554],{},"ollama pull \u002F run \u002F list \u002F show \u002F create \u002F serve",[59,1556,1557,1560],{},[45,1558,1559],{},"Modelfile","：类 Dockerfile 注册任意 GGUF，配 SYSTEM \u002F PARAMETER \u002F TEMPLATE",[59,1562,1563,1551,1566],{},[45,1564,1565],{},"OpenAI 兼容 API",[119,1567,1568],{},"http:\u002F\u002Flocalhost:11434\u002Fv1\u002Fchat\u002Fcompletions",[59,1570,1571,1551,1574,1577,1578,1577,1581],{},[45,1572,1573],{},"原生 API",[119,1575,1576],{},"\u002Fapi\u002Fchat","、",[119,1579,1580],{},"\u002Fapi\u002Fgenerate",[119,1582,1583],{},"\u002Fapi\u002Fembeddings",[59,1585,1586,1589],{},[45,1587,1588],{},"模型库","：官方注册表内置 Llama \u002F Qwen \u002F DeepSeek \u002F Gemma \u002F Mistral \u002F GPT-OSS 等",[59,1591,1592,1595],{},[45,1593,1594],{},"MLX 加速（Mac）","：0.19+ 起 M 系列自动用 MLX",[59,1597,1598,1601],{},[45,1599,1600],{},"量化","：默认 Q4_K_M、支持 Q5 \u002F Q8 \u002F FP16",[59,1603,1604,1607],{},[45,1605,1606],{},"跨平台","：Win \u002F Mac \u002F Linux 安装包，Docker 官方镜像",[36,1609,125],{"id":125},[41,1611,1612],{},"完全免费、MIT 开源、商用免费。",[36,1614,1616],{"id":1615},"实测m2-pro-qwen3-coder-7b-q4","实测（M2 Pro + Qwen3-Coder-7B Q4）",[41,1618,1619],{},[45,1620,150],{},[56,1622,1623,1629,1632,1635,1638],{},[59,1624,1625,1628],{},[119,1626,1627],{},"ollama run qwen3-coder:7b"," 一行起飞，3 秒进交互",[59,1630,1631],{},"REST API 配 Cursor \u002F Cline \u002F Continue 几乎全工具开箱即用",[59,1633,1634],{},"Modelfile 写自定义编码助手（low temperature + system prompt + 16K context）几分钟搞定",[59,1636,1637],{},"多模型并存，按需切换，内存占用合理",[59,1639,1640],{},"Mac M 系列 MLX 后比旧 GGUF 模式快显著",[41,1642,1643],{},[45,1644,175],{},[56,1646,1647,1656,1662,1669,1672],{},[59,1648,1179,1649,1652,1653],{},[119,1650,1651],{},"num_ctx"," 偏小（2048），跑长上下文要在 Modelfile 加 ",[119,1654,1655],{},"PARAMETER num_ctx 16384",[59,1657,1658,1659],{},"模型默认走 0.0.0.0:11434 ↔ Docker 容器互访要 ",[119,1660,1661],{},"--add-host=host.docker.internal:host-gateway",[59,1663,1664,1665,1668],{},"国内 ",[119,1666,1667],{},"ollama.com\u002Flibrary"," 下载偶有慢，可手动 HF 下 GGUF + Modelfile 自建",[59,1670,1671],{},"多用户并发吞吐显著低于 vLLM",[59,1673,1674],{},"没有 GUI，模型浏览 \u002F 参数面板要走 LM Studio \u002F Open WebUI 配合",[36,1676,195],{"id":195},[197,1678,1679,1685,1691,1696,1702,1708],{},[59,1680,1681,1684],{},[119,1682,1683],{},"curl -fsSL https:\u002F\u002Follama.ai\u002Finstall.sh | sh","（Mac \u002F Linux）；Windows winget",[59,1686,1687,1690],{},[119,1688,1689],{},"ollama pull qwen3-coder:7b","（按需换模型）",[59,1692,1693,1695],{},[119,1694,1627],{}," 直接聊",[59,1697,1698,1699],{},"应用接入：baseURL = ",[119,1700,1701],{},"http:\u002F\u002Flocalhost:11434\u002Fv1",[59,1703,1704,1705],{},"自定义：写 Modelfile → ",[119,1706,1707],{},"ollama create my-coder -f Modelfile",[59,1709,1710],{},"进阶：装 Open WebUI 做前端 \u002F 多人共享",[36,1712,225],{"id":225},[227,1714,1715,1731],{},[230,1716,1717],{},[233,1718,1719,1721,1723,1725,1728],{},[236,1720,238],{},[236,1722,30],{},[236,1724,249],{},[236,1726,1727],{},"vLLM",[236,1729,1730],{},"llama.cpp",[251,1732,1733,1749,1764,1778,1793,1809,1824],{},[233,1734,1735,1737,1740,1743,1746],{},[256,1736,258],{},[256,1738,1739],{},"CLI + Daemon",[256,1741,1742],{},"GUI + Headless",[256,1744,1745],{},"Python Server",[256,1747,1748],{},"C++ 二进制",[233,1750,1751,1753,1756,1758,1761],{},[256,1752,195],{},[256,1754,1755],{},"极低",[256,1757,1755],{},[256,1759,1760],{},"中",[256,1762,1763],{},"高",[233,1765,1766,1769,1771,1774,1776],{},[256,1767,1768],{},"模型浏览",[256,1770,1550],{},[256,1772,1773],{},"✅ GUI",[256,1775,352],{},[256,1777,352],{},[233,1779,1780,1783,1786,1789,1791],{},[256,1781,1782],{},"OpenAI 兼容",[256,1784,1785],{},"✅ :11434",[256,1787,1788],{},"✅ :1234",[256,1790,278],{},[256,1792,278],{},[233,1794,1795,1798,1801,1804,1807],{},[256,1796,1797],{},"多用户吞吐",[256,1799,1800],{},"弱（~40 tok\u002Fs）",[256,1802,1803],{},"中（50–90）",[256,1805,1806],{},"强（800–12500）",[256,1808,1760],{},[233,1810,1811,1814,1817,1819,1822],{},[256,1812,1813],{},"MLX (Mac)",[256,1815,1816],{},"✅ 0.19+",[256,1818,278],{},[256,1820,1821],{},"部分",[256,1823,373],{},[233,1825,1826,1829,1831,1834,1837],{},[256,1827,1828],{},"开源",[256,1830,381],{},[256,1832,1833],{},"闭源",[256,1835,1836],{},"Apache 2.0",[256,1838,381],{},[36,1840,392],{"id":392},[56,1842,1843,1849,1855,1861,1867],{},[59,1844,1845,1848],{},[45,1846,1847],{},"num_ctx 一定要设","：默认 2K 太小，跑代码 \u002F 长文档要 16K+",[59,1850,1851,1854],{},[45,1852,1853],{},"Modelfile 模板别漏 TEMPLATE","：错的 chat template 会让模型输出乱码 \u002F 不停",[59,1856,1857,1860],{},[45,1858,1859],{},"KV cache 爆表 = 速度悬崖","：32B 模型 32K 上下文，KV cache 可能 12+ GB，超显存自动 offload 慢 10×",[59,1862,1863,1866],{},[45,1864,1865],{},"不要 0.0.0.0 直接对公网","：默认无鉴权，对外暴露走反代 + Bearer \u002F mTLS",[59,1868,1869,1872],{},[45,1870,1871],{},"Mac 让它自动用 MLX","：升 0.19+；不要手动强制 GGUF + Metal",[36,1874,434],{"id":433},[56,1876,1877,1880,1883,1886,1889,1892,1895],{},[59,1878,1879],{},"✅ 应用 \u002F IDE 接本地模型（Cursor \u002F Cline \u002F Continue）",[59,1881,1882],{},"✅ 个人 \u002F 评估 \u002F 脚本自动化",[59,1884,1885],{},"✅ Modelfile 自定义系统 prompt + 参数",[59,1887,1888],{},"✅ Mac M 系列 MLX 用户",[59,1890,1891],{},"❌ 多用户并发生产服务（用 vLLM）",[59,1893,1894],{},"❌ GUI 调参 \u002F 模型浏览（配 LM Studio \u002F Open WebUI）",[59,1896,1897],{},"❌ 极致单卡吞吐研究（直接 llama.cpp \u002F vLLM）",[36,1899,463],{"id":463},[56,1901,1902,1906,1910,1914],{},[59,1903,1904],{},[469,1905,954],{"href":953},[59,1907,1908],{},[469,1909,478],{"href":477},[59,1911,1912],{},[469,1913,472],{"href":471},[59,1915,1916],{},[469,1917,490],{"href":489},[36,1919,493],{"id":493},[197,1921,1922,1929,1936],{},[59,1923,1924,1925],{},"Markaicode — Import GGUF Models into Ollama 2026（2026-05-15）",[469,1926,1927],{"href":1927,"rel":1928},"https:\u002F\u002Fmarkaicode.com\u002Fimport-gguf-models-ollama-guide",[503],[59,1930,1931,1932],{},"ComputingForGeeks — Ollama Models Cheat Sheet 2026 ",[469,1933,1934],{"href":1934,"rel":1935},"https:\u002F\u002Fcomputingforgeeks.com\u002Follama-models-cheat-sheet",[503],[59,1937,1938,1939],{},"Codersera — Ollama vs LM Studio vs vLLM vs llama.cpp vs MLX 2026 ",[469,1940,1941],{"href":1941,"rel":1942},"https:\u002F\u002Fcodersera.com\u002Fblog\u002Follama-vs-lm-studio-vs-vllm-vs-llama-cpp-vs-mlx-2026\u002F",[503],{"title":519,"searchDepth":520,"depth":520,"links":1944},[1945,1946,1947,1948,1949,1950,1951,1952,1953,1954],{"id":38,"depth":523,"text":39},{"id":54,"depth":523,"text":54},{"id":125,"depth":523,"text":125},{"id":1615,"depth":523,"text":1616},{"id":195,"depth":523,"text":195},{"id":225,"depth":523,"text":225},{"id":392,"depth":523,"text":392},{"id":433,"depth":523,"text":434},{"id":463,"depth":523,"text":463},{"id":493,"depth":523,"text":493},"\u002Fimg\u002Ftools\u002Follama.webp","Ollama 真实评测：本地 LLM 的事实标准 Daemon，CLI + REST API，模型库 + Modelfile + GGUF 一站式。0.19+ 在 Mac M 系列用 MLX 加速；OpenAI 兼容端点 11434；MIT 开源 + 跨平台。",[1958,1961,1964,1967],{"q":1959,"a":1960},"和 LM Studio 怎么选？","Ollama = Daemon + CLI，开机自启在 11434 端口跑，应用 \u002F IDE 调它最方便。LM Studio = GUI，模型浏览 \u002F 调参 \u002F 聊天体验更好。两者底层都基于 llama.cpp，Mac M 系列上都已切 MLX。",{"q":1962,"a":1963},"Modelfile 是什么？","类 Dockerfile 的模型配置：`FROM .\u002Fxxx.gguf` + PARAMETER \u002F TEMPLATE \u002F SYSTEM。把任意 GGUF 注册成本地模型供调用。`ollama create my-model -f Modelfile`。",{"q":1965,"a":1966},"OpenAI 兼容端点？","`http:\u002F\u002Flocalhost:11434\u002Fv1`。任何 OpenAI SDK 改 baseURL 即用。也可走原生 `\u002Fapi\u002Fchat`、`\u002Fapi\u002Fgenerate`。",{"q":1968,"a":1969},"并发能力？","单用户原型场景顺滑（~40 tok\u002Fs peak），多用户并发明显不如 vLLM（vLLM 的 PagedAttention + 连续批处理高 16–20×）。生产并发选 vLLM。","https:\u002F\u002Fgithub.com\u002Follama\u002Follama",[554],{},[562,563,564,565],[1975],{"plan":670,"price":568,"features":1976,"notes":1977},"完整 CLI + REST API + Modelfile + 模型库 + MIT 协议","全平台、商用免费","完全免费 + 开源（MIT）",[578,579],{"power":581,"ux":581,"price":534,"cn_support":520,"stability":534},{"title":30,"description":1956},"Ollama 评测 2026：本地运行大模型，开源 AI 模型管理工具，私有化部署指南",[1984,1986,1988],{"name":1985,"url":1927,"accessed":588},"Markaicode — Import GGUF 2026",{"name":1987,"url":1934,"accessed":588},"ComputingForGeeks — Ollama Cheat Sheet 2026",{"name":1989,"url":1941,"accessed":588},"Codersera — Ollama vs LM Studio vs vLLM 2026","tools\u002Fcoding\u002Flocal\u002Follama","本地 LLM 的 Daemon——CLI + REST API 后台跑，给 Cursor \u002F Cline \u002F Open WebUI 接本地模型最低门槛",[533,1993,1994,1995,1996,1997,1998,1999,601],"daemon","cli","rest-api","modelfile","gguf","mlx","openai-compatible","本地 LLM 的 Daemon 事实标准，CLI \u002F Modelfile \u002F REST API 三件套配合最广泛。GUI 偏好用户走 LM Studio；多用户并发生产用 vLLM；其他场景几乎默认 Ollama。","https:\u002F\u002Follama.com","yL3ZqN3rlWsImgvBFSYlFraTqj9ki9gSJlMbXVmruyg",{"id":2004,"title":249,"alternatives":2005,"api_compatible":557,"body":2006,"category":533,"chinese_friendly":520,"cover":2417,"description":2418,"domestic":1471,"extension":538,"faq":2419,"free":537,"github":557,"languages":2432,"lastVerified":555,"meta":2433,"models":557,"navigation":537,"notSuitable":557,"opensource":1471,"path":953,"pillar":559,"platforms":2434,"priceTable":2435,"pricing":2443,"published":576,"relatedPlaybooks":2444,"relatedReviews":557,"score":2445,"self_host":537,"seo":2446,"seoTitle":2447,"slug":15,"sources":2448,"stem":2454,"suitable":557,"tagline":2455,"tags":2456,"updated":588,"verdict":2460,"website":2461,"__hash__":2462},"tools\u002Ftools\u002Fcoding\u002Flocal\u002Flm-studio.md",[14,13,12,584],{"type":33,"value":2007,"toc":2405},[2008,2010,2017,2020,2022,2078,2080,2094,2099,2103,2107,2124,2128,2145,2147,2173,2175,2301,2303,2335,2337,2360,2362,2382,2384],[36,2009,39],{"id":38},[41,2011,2012,2013,2016],{},"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 + ",[119,2014,2015],{},"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。",[41,2018,2019],{},"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）。",[36,2021,54],{"id":54},[56,2023,2024,2030,2036,2042,2051,2060,2066,2072],{},[59,2025,2026,2029],{},[45,2027,2028],{},"模型浏览器","：内置 Hugging Face 检索，按 GGUF \u002F MLX \u002F 大小筛选、一键下载",[59,2031,2032,2035],{},[45,2033,2034],{},"聊天界面","：System Prompt \u002F temperature \u002F top-p \u002F context size 可视化调参",[59,2037,2038,2041],{},[45,2039,2040],{},"多模型并存 \u002F 切换","：同时加载多模型在不同会话中比较",[59,2043,2044,1551,2047,2050],{},[45,2045,2046],{},"OpenAI 兼容 Local Server",[119,2048,2049],{},"http:\u002F\u002Flocalhost:1234\u002Fv1","，任何 SDK 即接即用",[59,2052,2053,1551,2056,2059],{},[45,2054,2055],{},"Headless \u002F CLI",[119,2057,2058],{},"lms server start --port 1234","，无 GUI 可跑",[59,2061,2062,2065],{},[45,2063,2064],{},"PDF \u002F 文档对话","：内置基础 RAG，丢文件就能聊",[59,2067,2068,2071],{},[45,2069,2070],{},"MLX 原生支持（Mac）","：M1+ 上比 GGUF + Metal 快 30–50%",[59,2073,2074,2077],{},[45,2075,2076],{},"持续批处理","：Codersera 2026 测得 50–90 tok\u002Fs（消费级 GPU + 中等模型）",[36,2079,125],{"id":125},[56,2081,2082,2088],{},[59,2083,2084,2087],{},[45,2085,2086],{},"个人 \u002F 评估","：免费，全功能可用",[59,2089,2090,2093],{},[45,2091,2092],{},"商用","：邮件 \u002F 官网联系 LM Studio 团队",[679,2095,2096],{},[41,2097,2098],{},"模型本身免费（开源权重），LM Studio 不抽水任何 token 费用。",[36,2100,2102],{"id":2101},"实测mac-m2-pro-qwen3-coder-7b-gguf-q4_k_m","实测（Mac M2 Pro + Qwen3-Coder-7B GGUF Q4_K_M）",[41,2104,2105],{},[45,2106,150],{},[56,2108,2109,2112,2115,2118,2121],{},[59,2110,2111],{},"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",[59,2113,2114],{},"加载 7B Q4 模型 \u003C 3 秒，生成 ~75 tok\u002Fs",[59,2116,2117],{},"Local Server 开了 Cursor 直接接 baseURL → 本地代码补全零成本",[59,2119,2120],{},"MLX 版同模型 ~110 tok\u002Fs，差距显著",[59,2122,2123],{},"多窗口加载 2 个模型并排测，调 prompt 直观",[41,2125,2126],{},[45,2127,175],{},[56,2129,2130,2133,2136,2139,2142],{},[59,2131,2132],{},"模型库依赖 Hugging Face，国内访问要镜像 \u002F 代理",[59,2134,2135],{},"GPU 显存吃满后会自动 offload 到 CPU，无提示就慢下来",[59,2137,2138],{},"Headless 模式相对 Ollama 偏新，文档稍少",[59,2140,2141],{},"闭源应用（虽免费），不适合企业合规挂钩",[59,2143,2144],{},"中文 UI 可用但部分菜单仍英文",[36,2146,195],{"id":195},[197,2148,2149,2152,2155,2158,2161,2168],{},[59,2150,2151],{},"lmstudio.ai 下载（Mac \u002F Windows \u002F Linux）",[59,2153,2154],{},"打开 → Discover 标签 → 搜模型（如 qwen3-coder、deepseek-v3 GGUF\u002FMLX）→ Download",[59,2156,2157],{},"Chat 标签 → 选模型 → 调参聊天",[59,2159,2160],{},"Local Server 标签 → Start Server → 默认端口 1234",[59,2162,2163,2164,2167],{},"在你的应用里：",[119,2165,2166],{},"baseURL = \"http:\u002F\u002Flocalhost:1234\u002Fv1\"","，API Key 任意",[59,2169,2170,2171],{},"Headless：",[119,2172,2058],{},[36,2174,225],{"id":225},[227,2176,2177,2191],{},[230,2178,2179],{},[233,2180,2181,2183,2185,2187,2189],{},[236,2182,238],{},[236,2184,249],{},[236,2186,30],{},[236,2188,246],{},[236,2190,1730],{},[251,2192,2193,2209,2224,2238,2250,2262,2275,2287],{},[233,2194,2195,2197,2200,2203,2206],{},[256,2196,258],{},[256,2198,2199],{},"GUI + CLI",[256,2201,2202],{},"CLI Daemon",[256,2204,2205],{},"Docker UI",[256,2207,2208],{},"二进制",[233,2210,2211,2213,2216,2219,2221],{},[256,2212,1768],{},[256,2214,2215],{},"✅ 内置",[256,2217,2218],{},"CLI pull",[256,2220,352],{},[256,2222,2223],{},"手动",[233,2225,2226,2229,2231,2234,2236],{},[256,2227,2228],{},"参数调优 GUI",[256,2230,278],{},[256,2232,2233],{},"❌",[256,2235,1821],{},[256,2237,2233],{},[233,2239,2240,2242,2244,2246,2248],{},[256,2241,1565],{},[256,2243,1788],{},[256,2245,1785],{},[256,2247,278],{},[256,2249,278],{},[233,2251,2252,2254,2256,2258,2260],{},[256,2253,1813],{},[256,2255,278],{},[256,2257,1816],{},[256,2259,373],{},[256,2261,373],{},[233,2263,2264,2267,2269,2271,2273],{},[256,2265,2266],{},"多用户并发",[256,2268,295],{},[256,2270,295],{},[256,2272,278],{},[256,2274,1760],{},[233,2276,2277,2279,2281,2283,2285],{},[256,2278,1828],{},[256,2280,389],{},[256,2282,381],{},[256,2284,381],{},[256,2286,381],{},[233,2288,2289,2292,2294,2297,2299],{},[256,2290,2291],{},"上手难度",[256,2293,1755],{},[256,2295,2296],{},"低",[256,2298,1760],{},[256,2300,1763],{},[36,2302,392],{"id":392},[56,2304,2305,2311,2317,2323,2329],{},[59,2306,2307,2310],{},[45,2308,2309],{},"国内下模型走镜像","：HF 直连慢 \u002F 卡，配 HF_ENDPOINT=hf-mirror.com",[59,2312,2313,2316],{},[45,2314,2315],{},"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",[59,2318,2319,2322],{},[45,2320,2321],{},"MLX 优先（Mac M 系列）","：能下 MLX 版就别下 GGUF，速度差距明显",[59,2324,2325,2328],{},[45,2326,2327],{},"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",[59,2330,2331,2334],{},[45,2332,2333],{},"闭源合规要核","：企业内部使用前查 license；商用必须联系官方",[36,2336,434],{"id":433},[56,2338,2339,2342,2345,2348,2351,2354,2357],{},[59,2340,2341],{},"✅ 本地 LLM 入门 \u002F 评估",[59,2343,2344],{},"✅ Mac M 系列用户",[59,2346,2347],{},"✅ 想给 Cursor \u002F Cline 接本地 OpenAI 兼容 endpoint",[59,2349,2350],{},"✅ 需要 GUI 调参 \u002F 模型比较",[59,2352,2353],{},"❌ 多用户并发生产服务",[59,2355,2356],{},"❌ 嵌入式 \u002F 边缘设备",[59,2358,2359],{},"❌ 强合规 \u002F 必须开源审计",[36,2361,463],{"id":463},[56,2363,2364,2368,2372,2376],{},[59,2365,2366],{},[469,2367,484],{"href":483},[59,2369,2370],{},[469,2371,478],{"href":477},[59,2373,2374],{},[469,2375,472],{"href":471},[59,2377,2378],{},[469,2379,2381],{"href":2380},"\u002Fplaybook\u002Fonboarding\u002Fclaude-code-getting-started","Claude Code 上手 Playbook",[36,2383,493],{"id":493},[197,2385,2386,2393,2400],{},[59,2387,2388,2389],{},"LM Studio 官网 ",[469,2390,2391],{"href":2391,"rel":2392},"https:\u002F\u002Flmstudio.ai\u002F",[503],[59,2394,2395,2396],{},"Codersera — LM Studio Complete Guide 2026 ",[469,2397,2398],{"href":2398,"rel":2399},"https:\u002F\u002Fcodersera.com\u002Fblog\u002Flm-studio-complete-guide-2026\u002F",[503],[59,2401,1938,2402],{},[469,2403,1941],{"href":1941,"rel":2404},[503],{"title":519,"searchDepth":520,"depth":520,"links":2406},[2407,2408,2409,2410,2411,2412,2413,2414,2415,2416],{"id":38,"depth":523,"text":39},{"id":54,"depth":523,"text":54},{"id":125,"depth":523,"text":125},{"id":2101,"depth":523,"text":2102},{"id":195,"depth":523,"text":195},{"id":225,"depth":523,"text":225},{"id":392,"depth":523,"text":392},{"id":433,"depth":523,"text":434},{"id":463,"depth":523,"text":463},{"id":493,"depth":523,"text":493},"\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。",[2420,2423,2426,2429],{"q":2421,"a":2422},"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":2424,"a":2425},"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":2427,"a":2428},"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":2430,"a":2431},"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。",[554,553],{},[562,563,564],[2436,2439],{"plan":2086,"price":568,"features":2437,"notes":2438},"全功能 GUI + Headless API + GGUF\u002FMLX","供个人 \u002F 评估使用",{"plan":2092,"price":2440,"features":2441,"notes":2442},"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 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