[{"data":1,"prerenderedAt":2463},["ShallowReactive",2],{"header-counts":3,"footer-counts":6,"alt-main-open-webui":8,"alt-list-open-webui":587},{"tools":4,"reviews":5},98,27,{"tools":4,"reviews":5,"playbooks":7,"news":5},23,{"id":9,"title":10,"alternatives":11,"api_compatible":16,"body":32,"category":512,"chinese_friendly":513,"cover":514,"description":515,"domestic":516,"extension":517,"faq":518,"free":531,"github":532,"languages":533,"lastVerified":536,"meta":537,"models":538,"navigation":531,"notSuitable":538,"opensource":531,"path":539,"pillar":540,"platforms":541,"priceTable":547,"pricing":558,"published":559,"relatedPlaybooks":560,"relatedReviews":538,"score":563,"self_host":531,"seo":565,"seoTitle":566,"slug":567,"sources":568,"stem":576,"suitable":538,"tagline":577,"tags":578,"updated":571,"verdict":584,"website":585,"__hash__":586},"tools\u002Ftools\u002Fcoding\u002Flocal\u002Fopen-webui.md","Open WebUI",[12,13,14,15],"coding\u002Flocal\u002Flobe-chat","coding\u002Flocal\u002Fcherry-studio","coding\u002Flocal\u002Follama","coding\u002Flocal\u002Flm-studio",[17,18,19,20,21,22,23,24,25,26,27,28,29,30,31],"OpenAI","Anthropic","Google","Grok","Mistral","Cohere","阿里通义","百度文心","腾讯混元","Moonshot Kimi","字节豆包","DeepSeek","智谱 GLM","Ollama","Hugging Face",{"type":33,"value":34,"toc":497},"minimark",[35,40,44,47,50,115,118,121,125,130,159,164,189,192,227,230,367,370,413,417,440,443,470,473],[36,37,39],"h2",{"id":38},"tldr","TL;DR",[41,42,43],"p",{},"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。",[41,45,46],{},"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。",[36,48,49],{"id":49},"核心能力",[51,52,53,61,67,73,79,85,91,97,103,109],"ul",{},[54,55,56,60],"li",{},[57,58,59],"strong",{},"多模型后端","：Ollama \u002F OpenAI \u002F vLLM \u002F Anthropic \u002F Groq \u002F LocalAI \u002F 任意 OpenAI 兼容",[54,62,63,66],{},[57,64,65],{},"多用户 + RBAC","：注册 \u002F 邀请 \u002F 角色权限 \u002F 工作区隔离",[54,68,69,72],{},[57,70,71],{},"RAG 知识库","：上传文档 \u002F 网址 \u002F SearXNG 联网搜索 → 向量化 → 对话引用",[54,74,75,78],{},[57,76,77],{},"Tools \u002F Functions","：Python 写函数即扩展（联网 \u002F 计算器 \u002F 自定义 API）",[54,80,81,84],{},[57,82,83],{},"mcpo","：MCP-to-OpenAPI 代理，任意 MCP 服务器接进来",[54,86,87,90],{},[57,88,89],{},"oikb","：知识库同步本地文件夹 \u002F GitHub \u002F S3 \u002F Confluence 等 40+ 源",[54,92,93,96],{},[57,94,95],{},"open-terminal \u002F cptr","：给 AI 真实终端 + 文件 + 沙箱执行",[54,98,99,102],{},[57,100,101],{},"图像生成","：Stable Diffusion \u002F DALL·E \u002F 自托管接入",[54,104,105,108],{},[57,106,107],{},"语音输入 \u002F TTS","：内置",[54,110,111,114],{},[57,112,113],{},"企业 LTS","：custom branding + SLA + 长期支持版本（联系销售）",[36,116,117],{"id":117},"价格",[41,119,120],{},"完全免费、MIT 开源、商用免费。Enterprise 提供品牌定制 + SLA + LTS。",[36,122,124],{"id":123},"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）",[41,126,127],{},[57,128,129],{},"亮点：",[51,131,132,140,143,150,153,156],{},[54,133,134,135,139],{},"单条 ",[136,137,138],"code",{},"docker run"," 五分钟上线",[54,141,142],{},"自带的多用户 + 角色权限省去重新搭 Auth",[54,144,145,146,149],{},"RAG 直传 30 个 PDF 后向量化顺利，对话中 ",[136,147,148],{},"#知识库"," 引用准确",[54,151,152],{},"mcpo 把 GitHub MCP 服务器接进来，团队对话里直接 issue \u002F PR 操作",[54,154,155],{},"模型切换流畅，OpenAI + Ollama 并存",[54,157,158],{},"SearXNG 联网搜索给模型实时信息，过时知识截止问题缓解",[41,160,161],{},[57,162,163],{},"踩坑：",[51,165,166,169,176,183,186],{},[54,167,168],{},"Docker 镜像 ~1.5GB，首次拉取偏慢",[54,170,171,172,175],{},"默认 ",[136,173,174],{},"0.0.0.0"," 公网暴露要加 HTTPS + 反代",[54,177,178,179,182],{},"嵌入模型 ",[136,180,181],{},"sentence-transformers"," 中文效果一般，建议换 bge-m3",[54,184,185],{},"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）",[54,187,188],{},"版本升级要看 changelog，部分 minor 含 breaking 改动",[36,190,191],{"id":191},"上手",[193,194,195,201,208,211,214,217,220],"ol",{},[54,196,197,198],{},"装 Docker → ",[136,199,200],{},"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",[54,202,203,204,207],{},"浏览器开 ",[136,205,206],{},"http:\u002F\u002Flocalhost:3000"," → 注册第一个账号（管理员）",[54,209,210],{},"设置 → Connections → 连接 Ollama \u002F 加 OpenAI Key",[54,212,213],{},"Models → Pull \u002F Discover 模型",[54,215,216],{},"Workspaces → 建知识库 → 上传文档",[54,218,219],{},"Tools → 启用 \u002F 写自定义函数",[54,221,222,223,226],{},"生产部署：Nginx 反代 + Let's Encrypt + 备份 ",[136,224,225],{},"\u002Fapp\u002Fbackend\u002Fdata"," volume",[36,228,229],{"id":229},"对比",[231,232,233,254],"table",{},[234,235,236],"thead",{},[237,238,239,243,245,248,251],"tr",{},[240,241,242],"th",{},"维度",[240,244,10],{},[240,246,247],{},"LobeChat",[240,249,250],{},"Cherry Studio",[240,252,253],{},"LM Studio",[255,256,257,274,290,305,319,334,351],"tbody",{},[237,258,259,263,266,269,272],{},[260,261,262],"td",{},"形态",[260,264,265],{},"Docker \u002F 桌面",[260,267,268],{},"Web + 桌面",[260,270,271],{},"桌面",[260,273,271],{},[237,275,276,279,282,285,288],{},[260,277,278],{},"多用户",[260,280,281],{},"✅ 一等",[260,283,284],{},"✅",[260,286,287],{},"无",[260,289,287],{},[237,291,292,295,298,300,302],{},[260,293,294],{},"RAG",[260,296,297],{},"✅ 强 + oikb",[260,299,284],{},[260,301,284],{},[260,303,304],{},"弱",[237,306,307,310,313,315,317],{},[260,308,309],{},"工具 \u002F MCP",[260,311,312],{},"✅ mcpo",[260,314,284],{},[260,316,284],{},[260,318,304],{},[237,320,321,324,327,330,332],{},[260,322,323],{},"自托管",[260,325,326],{},"✅ Docker \u002F K8s",[260,328,329],{},"✅ Docker",[260,331,287],{},[260,333,287],{},[237,335,336,339,342,345,348],{},[260,337,338],{},"GitHub Stars",[260,340,341],{},"126k+",[260,343,344],{},"72k+",[260,346,347],{},"60k+",[260,349,350],{},"–",[237,352,353,356,359,361,364],{},[260,354,355],{},"开源协议",[260,357,358],{},"MIT",[260,360,358],{},[260,362,363],{},"AGPL-3.0",[260,365,366],{},"闭源（免费）",[36,368,369],{"id":369},"避坑",[51,371,372,378,386,395,401,407],{},[54,373,374,377],{},[57,375,376],{},"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",[54,379,380,385],{},[57,381,382,383,226],{},"备份 ",[136,384,225],{},"：知识库 \u002F 用户 \u002F 对话全在里面",[54,387,388,391,392,394],{},[57,389,390],{},"中文 RAG 换嵌入模型","：默认 ",[136,393,181],{}," 中文一般，配 bge-m3 或硅基流动嵌入 API",[54,396,397,400],{},[57,398,399],{},"mcpo 工具范围谨慎","：MCP 给 AI 真实能力，第三方服务器审一遍",[54,402,403,406],{},[57,404,405],{},"后端吞吐看 Ollama","：5+ 并发上 vLLM 后端，Ollama 单 worker 会排队",[54,408,409,412],{},[57,410,411],{},"升级前看 changelog","：weekly 更新，偶有 breaking",[36,414,416],{"id":415},"适合-不适合","适合 \u002F 不适合",[51,418,419,422,425,428,431,434,437],{},[54,420,421],{},"✅ 团队 \u002F 家庭 \u002F 公司多人共享 AI 平台",[54,423,424],{},"✅ 要 Web 端访问 \u002F 移动端兼容",[54,426,427],{},"✅ 自托管 \u002F 完全控制数据",[54,429,430],{},"✅ MCP \u002F 工具调用刚需",[54,432,433],{},"❌ 单人桌面体验（用 Cherry Studio）",[54,435,436],{},"❌ 零运维 \u002F 不愿碰 Docker",[54,438,439],{},"❌ iOS 原生 App 主力",[36,441,442],{"id":442},"相关阅读",[51,444,445,452,458,464],{},[54,446,447],{},[448,449,451],"a",{"href":450},"\u002Ftools\u002Fcoding\u002Flocal\u002Flobe-chat","LobeChat 评测",[54,453,454],{},[448,455,457],{"href":456},"\u002Ftools\u002Fcoding\u002Flocal\u002Fcherry-studio","Cherry Studio 评测",[54,459,460],{},[448,461,463],{"href":462},"\u002Ftools\u002Fcoding\u002Flocal\u002Follama","Ollama 评测",[54,465,466],{},[448,467,469],{"href":468},"\u002Fplaybook\u002Fonboarding\u002Frag-pipeline-build","RAG Pipeline 搭建 Playbook",[36,471,472],{"id":472},"来源",[193,474,475,483,490],{},[54,476,477,478],{},"Open WebUI 官方文档 ",[448,479,480],{"href":480,"rel":481},"https:\u002F\u002Fdocs.openwebui.com\u002F",[482],"nofollow",[54,484,485,486],{},"Local AI Master — Open WebUI Setup Guide 2026 ",[448,487,488],{"href":488,"rel":489},"https:\u002F\u002Flocalaimaster.com\u002Fblog\u002Fopen-webui-setup-guide",[482],[54,491,492,493],{},"AIToolDiscovery — Set Up Open-WebUI with Ollama 2026 ",[448,494,495],{"href":495,"rel":496},"https:\u002F\u002Fwww.aitooldiscovery.com\u002Fhow-to\u002Fsetup-open-webui-ollama",[482],{"title":498,"searchDepth":499,"depth":499,"links":500},"",3,[501,503,504,505,506,507,508,509,510,511],{"id":38,"depth":502,"text":39},2,{"id":49,"depth":502,"text":49},{"id":117,"depth":502,"text":117},{"id":123,"depth":502,"text":124},{"id":191,"depth":502,"text":191},{"id":229,"depth":502,"text":229},{"id":369,"depth":502,"text":369},{"id":415,"depth":502,"text":416},{"id":442,"depth":502,"text":442},{"id":472,"depth":502,"text":472},"local",4,"\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,"md",[519,522,525,528],{"q":520,"a":521},"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":523,"a":524},"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":526,"a":527},"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":529,"a":530},"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。",true,"https:\u002F\u002Fgithub.com\u002Fopen-webui\u002Fopen-webui",[534,535],"en","zh","2026-08-02",{},null,"\u002Ftools\u002Fcoding\u002Flocal\u002Fopen-webui","coding",[542,543,544,545,546],"docker","linux","macos","windows","kubernetes",[548,553],{"plan":549,"price":550,"features":551,"notes":552},"开源版","免费","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","MIT 协议",{"plan":554,"price":555,"features":556,"notes":557},"Enterprise","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","2026-06-19",[561,562],"onboarding\u002Frag-pipeline-build","onboarding\u002Fclaude-code-getting-started",{"power":564,"ux":513,"price":564,"cn_support":513,"stability":564},5,{"title":10,"description":515},"Open WebUI 评测 2026：自托管 ChatGPT 替代，Ollama 前端部署指南","coding\u002Flocal\u002Fopen-webui",[569,572,574],{"name":570,"url":480,"accessed":571},"Open WebUI 官方文档","2026-06-24",{"name":573,"url":488,"accessed":571},"Local AI Master — Open WebUI Setup Guide 2026",{"name":575,"url":495,"accessed":571},"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",[512,579,542,580,581,582,583],"self-host","rag","multi-user","ollama","open-source","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",[588,1080,1518,2003],{"id":589,"title":247,"alternatives":590,"api_compatible":591,"body":592,"category":512,"chinese_friendly":564,"cover":1032,"description":1033,"domestic":531,"extension":517,"faq":1034,"free":531,"github":1004,"languages":1047,"lastVerified":536,"meta":1048,"models":538,"navigation":531,"notSuitable":538,"opensource":531,"path":450,"pillar":540,"platforms":1049,"priceTable":1051,"pricing":1058,"published":559,"relatedPlaybooks":1059,"relatedReviews":538,"score":1060,"self_host":531,"seo":1061,"seoTitle":1062,"slug":12,"sources":1063,"stem":1070,"suitable":538,"tagline":1071,"tags":1072,"updated":571,"verdict":1077,"website":1078,"__hash__":1079},"tools\u002Ftools\u002Fcoding\u002Flocal\u002Flobe-chat.md",[13,567,14,15],[17,18,19,20,21,22,23,24,25,26,27,28,29,30,31],{"type":33,"value":593,"toc":1020},[594,596,603,606,608,673,675,688,691,695,699,719,723,740,742,768,770,906,908,946,948,974,976,995,997],[36,595,39],{"id":38},[41,597,598,599,602],{},"LobeChat 是 LobeHub 团队的开源 AI 聊天框架，2023 年发布、GitHub 72k+ stars、MIT 协议。",[57,600,601],{},"Web + 桌面 + Docker 自托管三形态","，把 OpenAI \u002F Claude \u002F Gemini \u002F DeepSeek \u002F Qwen \u002F Kimi \u002F Ollama \u002F LM Studio 等 80+ 模型聚合到一个现代设计的客户端里。内置 RAG 知识库 + 插件市场 + 助手市场 + 多模型对比 + MCP，是当下综合最强的多模型 AI 客户端之一。",[41,604,605],{},"适合：需要 Web 端访问、Docker 自托管、多模型对比、丰富助手市场的用户；中文重度用户；想给团队 \u002F 家庭部署一个共享 AI 工作台。不适合：只用桌面 + 不需要 Web（Cherry Studio 同样优秀且更精细）、强企业 RBAC + 多租户（Open WebUI 多用户更完善）。",[36,607,49],{"id":49},[51,609,610,616,622,628,634,640,646,651,657,663],{},[54,611,612,615],{},[57,613,614],{},"多模型聚合","：OpenAI \u002F Claude \u002F Gemini \u002F DeepSeek \u002F Qwen \u002F Kimi \u002F 豆包 \u002F Groq \u002F Together \u002F OpenRouter \u002F Ollama \u002F LM Studio",[54,617,618,621],{},[57,619,620],{},"多模型对比","：同 prompt 给多模型并排回答",[54,623,624,627],{},[57,625,626],{},"本地 RAG 知识库","：上传 PDF \u002F Word \u002F 网页 → 向量化 → 检索引用",[54,629,630,633],{},[57,631,632],{},"插件市场","：联网搜索 \u002F 代码执行 \u002F 图像生成 \u002F 翻译等几十款官方插件",[54,635,636,639],{},[57,637,638],{},"助手市场","：几百个预设 AI 角色，一键导入",[54,641,642,645],{},[57,643,644],{},"MCP 协议","：扩展任意工具能力",[54,647,648],{},[57,649,650],{},"代码解释器 \u002F 文件上传 \u002F TTS \u002F 多模态",[54,652,653,656],{},[57,654,655],{},"Web + 桌面 + Docker","：三形态，数据可完全本地",[54,658,659,662],{},[57,660,661],{},"LobeHub Cloud","：官方云托管，免部署",[54,664,665,668,669,672],{},[57,666,667],{},"快捷指令 \u002F 工作流","：自定义 prompt 模板，",[136,670,671],{},"\u002Fpodcast-summary"," 类用法",[36,674,117],{"id":117},[51,676,677,683],{},[54,678,679,682],{},[57,680,681],{},"自托管 \u002F 桌面","：完全免费、MIT 开源",[54,684,685,687],{},[57,686,661],{},"：订阅制，云端托管 + 团队协作 + 同步",[41,689,690],{},"模型 API 费用按你自己的供应商付费；本地 Ollama \u002F LM Studio 零成本。",[36,692,694],{"id":693},"实测m2-自托管-docker连-openai-deepseek-本地-ollama","实测（M2 + 自托管 Docker，连 OpenAI + DeepSeek + 本地 Ollama）",[41,696,697],{},[57,698,129],{},[51,700,701,704,707,710,713,716],{},[54,702,703],{},"界面颜值是这一类工具里第一档（深色 \u002F 透明 \u002F 现代感）",[54,705,706],{},"多模型并排对比对选型极其有用：写一道复杂题，Claude \u002F GPT \u002F DeepSeek 直接对比答案",[54,708,709],{},"知识库 RAG 上传 50+ PDF 后检索准确，引用片段可视化",[54,711,712],{},"助手市场拿来即用——「Code Reviewer」「Translation Polish」节省 prompt 编写",[54,714,715],{},"Docker 一键部署，团队 5 人共享流畅",[54,717,718],{},"多平台数据同步（Cloud \u002F WebDAV）",[41,720,721],{},[57,722,163],{},[51,724,725,728,731,734,737],{},[54,726,727],{},"自托管要熟悉 Docker + 反代 + HTTPS",[54,729,730],{},"国内连 OpenAI \u002F Claude 需自带网络方案",[54,732,733],{},"Web 版数据存 LobeHub，隐私敏感场景走桌面 \u002F Docker",[54,735,736],{},"插件市场质量参差，要自筛",[54,738,739],{},"团队多人共享需配 LobeHub Cloud 或自建数据库（Postgres + S3）",[36,741,191],{"id":191},[193,743,744,747,753,756,759,762,765],{},[54,745,746],{},"选形态：Web（chat.lobehub.com 注册即用） \u002F 桌面（GitHub Releases 下载） \u002F Docker",[54,748,749,750],{},"Docker：",[136,751,752],{},"docker run -d -p 3210:3210 -e OPENAI_API_KEY=sk-xxx --name lobe-chat lobehub\u002Flobe-chat",[54,754,755],{},"设置 → AI 服务商 → 添加 OpenAI \u002F Claude \u002F DeepSeek \u002F Ollama",[54,757,758],{},"模型选择器测试对话",[54,760,761],{},"知识库：拖文件 → 等向量化 → 对话引用",[54,763,764],{},"助手市场拉「Code Reviewer」「论文翻译润色」试用",[54,766,767],{},"进阶：插件市场启用联网搜索 \u002F 代码执行；MCP 自定义工具",[36,769,229],{"id":229},[231,771,772,786],{},[234,773,774],{},[237,775,776,778,780,782,784],{},[240,777,242],{},[240,779,247],{},[240,781,250],{},[240,783,10],{},[240,785,253],{},[255,787,788,800,814,826,840,856,869,882,894],{},[237,789,790,792,794,796,798],{},[260,791,262],{},[260,793,655],{},[260,795,271],{},[260,797,265],{},[260,799,271],{},[237,801,802,804,807,809,811],{},[260,803,614],{},[260,805,806],{},"✅ 80+",[260,808,284],{},[260,810,284],{},[260,812,813],{},"本地为主",[237,815,816,818,820,822,824],{},[260,817,620],{},[260,819,281],{},[260,821,284],{},[260,823,304],{},[260,825,304],{},[237,827,828,831,833,835,838],{},[260,829,830],{},"知识库 RAG",[260,832,284],{},[260,834,284],{},[260,836,837],{},"✅ + oikb",[260,839,304],{},[237,841,842,845,848,851,854],{},[260,843,844],{},"插件 \u002F 助手市场",[260,846,847],{},"✅ 丰富",[260,849,850],{},"300+ 助手",[260,852,853],{},"Tools",[260,855,304],{},[237,857,858,861,863,865,867],{},[260,859,860],{},"MCP",[260,862,284],{},[260,864,284],{},[260,866,312],{},[260,868,304],{},[237,870,871,873,876,878,880],{},[260,872,278],{},[260,874,875],{},"配 Cloud \u002F 自建",[260,877,287],{},[260,879,281],{},[260,881,287],{},[237,883,884,886,888,890,892],{},[260,885,338],{},[260,887,344],{},[260,889,347],{},[260,891,341],{},[260,893,350],{},[237,895,896,898,900,902,904],{},[260,897,355],{},[260,899,358],{},[260,901,363],{},[260,903,358],{},[260,905,366],{},[36,907,369],{"id":369},[51,909,910,916,922,928,934,940],{},[54,911,912,915],{},[57,913,914],{},"Web 版数据不本地","：隐私敏感选桌面或 Docker 自托管",[54,917,918,921],{},[57,919,920],{},"国内连海外模型走中转","：直连 OpenAI \u002F Claude 不稳，配 OpenRouter \u002F Ofox \u002F 国内中转",[54,923,924,927],{},[57,925,926],{},"Docker 自托管暴露公网","：上反代 + HTTPS + Auth + 备份数据库",[54,929,930,933],{},[57,931,932],{},"嵌入模型中文优化","：默认嵌入对中文一般，配 bge-m3 \u002F 硅基流动 Pro 版",[54,935,936,939],{},[57,937,938],{},"插件市场审一遍","：第三方插件可执行代码，团队部署谨慎启用",[54,941,942,945],{},[57,943,944],{},"同步选 Cloud vs WebDAV","：团队多端走 LobeHub Cloud；个人多设备 WebDAV 即可",[36,947,416],{"id":415},[51,949,950,953,956,959,962,965,968,971],{},[54,951,952],{},"✅ Web + 桌面双形态需求",[54,954,955],{},"✅ Docker 自托管 \u002F 团队共享",[54,957,958],{},"✅ 多模型对比 \u002F 选型",[54,960,961],{},"✅ 中文重度用户",[54,963,964],{},"✅ 助手市场 \u002F 插件生态用户",[54,966,967],{},"❌ 强企业 RBAC + 多租户（Open WebUI 更完善）",[54,969,970],{},"❌ 只要桌面 + 数据完全本地（Cherry Studio 同样优秀）",[54,972,973],{},"❌ 完全不会碰 Docker",[36,975,442],{"id":442},[51,977,978,982,987,991],{},[54,979,980],{},[448,981,457],{"href":456},[54,983,984],{},[448,985,986],{"href":539},"Open WebUI 评测",[54,988,989],{},[448,990,463],{"href":462},[54,992,993],{},[448,994,469],{"href":468},[36,996,472],{"id":472},[193,998,999,1006,1013],{},[54,1000,1001,1002],{},"LobeChat GitHub 仓库（72k+ stars，MIT）",[448,1003,1004],{"href":1004,"rel":1005},"https:\u002F\u002Fgithub.com\u002Flobehub\u002Flobe-chat",[482],[54,1007,1008,1009],{},"腾讯云开发者社区 — Lobe Chat 本地化 AI 聊天终极桌面客户端（2026-01）",[448,1010,1011],{"href":1011,"rel":1012},"https:\u002F\u002Fcloud.tencent.com\u002Fdeveloper\u002Farticle\u002F2622150",[482],[54,1014,1015,1016],{},"Ofox.ai — LobeChat 完全配置指南 2026（2026-04-17）",[448,1017,1018],{"href":1018,"rel":1019},"https:\u002F\u002Fofox.ai\u002Fzh\u002Fblog\u002Flobechat-api-configuration-guide-2026",[482],{"title":498,"searchDepth":499,"depth":499,"links":1021},[1022,1023,1024,1025,1026,1027,1028,1029,1030,1031],{"id":38,"depth":502,"text":39},{"id":49,"depth":502,"text":49},{"id":117,"depth":502,"text":117},{"id":693,"depth":502,"text":694},{"id":191,"depth":502,"text":191},{"id":229,"depth":502,"text":229},{"id":369,"depth":502,"text":369},{"id":415,"depth":502,"text":416},{"id":442,"depth":502,"text":442},{"id":472,"depth":502,"text":472},"\u002Fimg\u002Ftools\u002Flobe-chat.webp","LobeChat 真实评测：LobeHub 团队开源 AI 聊天框架，GitHub 72k+ stars、MIT 协议。Web + 桌面（Win\u002FMac\u002FLinux\u002FDocker）双形态，支持 OpenAI \u002F Claude \u002F Gemini \u002F DeepSeek \u002F Qwen \u002F Ollama 等 80+ 模型，内置 RAG 知识库 + 插件市场 + 助手市场 + 多模型对比。",[1035,1038,1041,1044],{"q":1036,"a":1037},"Web 版 vs 桌面版 vs Docker 自托管，怎么选？","Web 版（chat.lobehub.com）最快上手但数据存 LobeHub 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":1039,"a":1040},"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":1042,"a":1043},"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":1045,"a":1046},"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。",[535,534],{},[1050,545,544,543,542],"web",[1052,1054],{"plan":681,"price":550,"features":1053,"notes":552},"全功能 \u002F 80+ 模型 \u002F 知识库 \u002F 插件 \u002F 助手市场",{"plan":661,"price":1055,"features":1056,"notes":1057},"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 订阅",[561,562],{"power":564,"ux":564,"price":564,"cn_support":564,"stability":513},{"title":247,"description":1033},"LobeChat - 开源 AI 聊天框架评测与部署 | AIHO",[1064,1066,1068],{"name":1065,"url":1004,"accessed":571},"LobeChat GitHub",{"name":1067,"url":1011,"accessed":571},"腾讯云开发者社区 — Lobe Chat 终极桌面客户端",{"name":1069,"url":1018,"accessed":571},"Ofox.ai — LobeChat 完全配置指南 2026","tools\u002Fcoding\u002Flocal\u002Flobe-chat","现代设计的开源 AI 聊天框架——Web + 桌面双形态、72k+ stars、多模型 + 知识库 + 插件市场",[512,1050,1073,1074,580,1075,1076,583],"desktop","multi-model","plugin","mcp","颜值与功能双优的多模型 AI 聊天客户端。要 Web + 桌面双形态、自托管 Docker、多模型对比、丰富助手市场——LobeChat 是综合最强；纯桌面体验 Cherry Studio 同样优秀。","https:\u002F\u002Flobehub.com","sAhQ5CHDobJa--z6NbtgysTQDwTBYsoGPXR0zat-5-w",{"id":1081,"title":250,"alternatives":1082,"api_compatible":538,"body":1083,"category":512,"chinese_friendly":564,"cover":1469,"description":1470,"domestic":531,"extension":517,"faq":1471,"free":531,"github":1484,"languages":1485,"lastVerified":536,"meta":1486,"models":538,"navigation":531,"notSuitable":538,"opensource":531,"path":456,"pillar":540,"platforms":1487,"priceTable":1489,"pricing":1497,"published":559,"relatedPlaybooks":1498,"relatedReviews":538,"score":1500,"self_host":531,"seo":1501,"seoTitle":1502,"slug":13,"sources":1503,"stem":1510,"suitable":538,"tagline":1511,"tags":1512,"updated":571,"verdict":1515,"website":1516,"__hash__":1517},"tools\u002Ftools\u002Fcoding\u002Flocal\u002Fcherry-studio.md",[12,15,14,567],{"type":33,"value":1084,"toc":1457},[1085,1087,1090,1093,1095,1136,1138,1150,1156,1160,1164,1181,1185,1202,1204,1228,1230,1351,1353,1385,1387,1410,1412,1432,1434],[36,1086,39],{"id":38},[41,1088,1089],{},"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。",[41,1091,1092],{},"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。",[36,1094,49],{"id":49},[51,1096,1097,1102,1107,1113,1118,1124,1130],{},[54,1098,1099,1101],{},[57,1100,614],{},"：OpenAI \u002F Claude \u002F Gemini \u002F DeepSeek \u002F Qwen \u002F Kimi \u002F Moonshot 等云端 + Ollama \u002F LM Studio 本地",[54,1103,1104,1106],{},[57,1105,626],{},"：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",[54,1108,1109,1112],{},[57,1110,1111],{},"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",[54,1114,1115,1117],{},[57,1116,644],{},"：扩展工具调用 \u002F 联网搜索 \u002F 文件操作",[54,1119,1120,1123],{},[57,1121,1122],{},"数据本地优先","：对话历史本地存储，WebDAV 同步，不上传第三方",[54,1125,1126,1129],{},[57,1127,1128],{},"多模态","：图片识别 \u002F PDF 阅读 \u002F Markdown + Mermaid + 代码高亮",[54,1131,1132,1135],{},[57,1133,1134],{},"AI 绘画 + 翻译","：内置主流 SD \u002F DALL·E \u002F 翻译 API 集成",[36,1137,117],{"id":117},[51,1139,1140,1145],{},[54,1141,1142,1144],{},[57,1143,549],{},"：完全免费，AGPL-3.0",[54,1146,1147,1149],{},[57,1148,554],{},"：私有化部署 + 团队协作 + 资源管控，联系销售",[1151,1152,1153],"blockquote",{},[41,1154,1155],{},"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。",[36,1157,1159],{"id":1158},"实测mac-m2-中型知识库","实测（Mac M2 + 中型知识库）",[41,1161,1162],{},[57,1163,129],{},[51,1165,1166,1169,1172,1175,1178],{},[54,1167,1168],{},"中文 UI \u002F 文档 \u002F 社区都顶级，零门槛上手",[54,1170,1171],{},"本地 RAG 拖入 30+ PDF 后向量化 \u003C 2 分钟（用 bge-m3）",[54,1173,1174],{},"多模型并排回答：让 Claude \u002F GPT \u002F DeepSeek 同回一个问题做比较",[54,1176,1177],{},"MCP 接 Brave Search + 自定义工具流畅",[54,1179,1180],{},"WebDAV 同步坚果云 \u002F 阿里云盘，桌面 + 移动设备数据互通",[41,1182,1183],{},[57,1184,163],{},[51,1186,1187,1190,1193,1196,1199],{},[54,1188,1189],{},"没有 Web 端 \u002F Docker 自托管（要这个用 LobeChat）",[54,1191,1192],{},"iOS 版尚未发布（roadmap 中）",[54,1194,1195],{},"大型 PDF（>100 MB）向量化偶有失败，要切小",[54,1197,1198],{},"助手市场质量参差，要自筛",[54,1200,1201],{},"模型 API 调用全靠你自己付费，新手要先理解 API Key 概念",[36,1203,191],{"id":191},[193,1205,1206,1209,1212,1219,1222,1225],{},[54,1207,1208],{},"cherry-ai.com 下载客户端（或 GitHub releases）",[54,1210,1211],{},"设置 → 模型服务 → 填 OpenAI \u002F Claude \u002F DeepSeek API Key",[54,1213,1214,1215],{},"（可选）本地：装 Ollama → Cherry Studio 自动识别 endpoint ",[448,1216,1217],{"href":1217,"rel":1218},"http:\u002F\u002Flocalhost:11434",[482],[54,1220,1221],{},"新建知识库 → 拖文件 \u002F 加网址 → 等向量化",[54,1223,1224],{},"新对话 → 选模型 → 勾知识库 → 提问",[54,1226,1227],{},"进阶：自定义助手（System Prompt）+ MCP 扩展工具",[36,1229,229],{"id":229},[231,1231,1232,1246],{},[234,1233,1234],{},[237,1235,1236,1238,1240,1242,1244],{},[240,1237,242],{},[240,1239,250],{},[240,1241,247],{},[240,1243,253],{},[240,1245,10],{},[255,1247,1248,1260,1273,1286,1298,1312,1327,1339],{},[237,1249,1250,1252,1254,1256,1258],{},[260,1251,262],{},[260,1253,271],{},[260,1255,268],{},[260,1257,271],{},[260,1259,265],{},[237,1261,1262,1264,1267,1269,1271],{},[260,1263,614],{},[260,1265,1266],{},"✅ 云 + 本地",[260,1268,1266],{},[260,1270,813],{},[260,1272,1266],{},[237,1274,1275,1277,1280,1282,1284],{},[260,1276,830],{},[260,1278,1279],{},"✅ 强",[260,1281,1279],{},[260,1283,304],{},[260,1285,284],{},[237,1287,1288,1290,1292,1294,1296],{},[260,1289,860],{},[260,1291,284],{},[260,1293,284],{},[260,1295,304],{},[260,1297,284],{},[237,1299,1300,1303,1306,1308,1310],{},[260,1301,1302],{},"自托管 \u002F Web",[260,1304,1305],{},"无 Web",[260,1307,329],{},[260,1309,287],{},[260,1311,329],{},[237,1313,1314,1317,1320,1322,1325],{},[260,1315,1316],{},"中文",[260,1318,1319],{},"5\u002F5",[260,1321,1319],{},[260,1323,1324],{},"4\u002F5",[260,1326,1324],{},[237,1328,1329,1331,1333,1335,1337],{},[260,1330,355],{},[260,1332,363],{},[260,1334,358],{},[260,1336,366],{},[260,1338,358],{},[237,1340,1341,1343,1345,1347,1349],{},[260,1342,338],{},[260,1344,347],{},[260,1346,344],{},[260,1348,350],{},[260,1350,341],{},[36,1352,369],{"id":369},[51,1354,1355,1361,1367,1373,1379],{},[54,1356,1357,1360],{},[57,1358,1359],{},"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",[54,1362,1363,1366],{},[57,1364,1365],{},"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",[54,1368,1369,1372],{},[57,1370,1371],{},"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",[54,1374,1375,1378],{},[57,1376,1377],{},"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",[54,1380,1381,1384],{},[57,1382,1383],{},"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",[36,1386,416],{"id":415},[51,1388,1389,1392,1395,1398,1401,1404,1407],{},[54,1390,1391],{},"✅ 中文用户、AI 重度使用 \u002F 多模型管理",[54,1393,1394],{},"✅ 需要本地 RAG 知识库",[54,1396,1397],{},"✅ 关注数据隐私 \u002F 本地存储",[54,1399,1400],{},"✅ 想用 Ollama \u002F LM Studio 本地模型",[54,1402,1403],{},"❌ 需要 Web 端 \u002F Docker 自托管",[54,1405,1406],{},"❌ 团队多人共享 \u002F SSO",[54,1408,1409],{},"❌ iOS 主力用户",[36,1411,442],{"id":442},[51,1413,1414,1418,1424,1428],{},[54,1415,1416],{},[448,1417,451],{"href":450},[54,1419,1420],{},[448,1421,1423],{"href":1422},"\u002Ftools\u002Fcoding\u002Flocal\u002Flm-studio","LM Studio 评测",[54,1425,1426],{},[448,1427,463],{"href":462},[54,1429,1430],{},[448,1431,469],{"href":468},[36,1433,472],{"id":472},[193,1435,1436,1443,1450],{},[54,1437,1438,1439],{},"Cherry Studio 官网（功能 + 下载）",[448,1440,1441],{"href":1441,"rel":1442},"https:\u002F\u002Fwww.cherry-ai.com\u002F",[482],[54,1444,1445,1446],{},"MBLUO Studio — Cherry Studio 评测 2026 ",[448,1447,1448],{"href":1448,"rel":1449},"https:\u002F\u002Fmbluostudio.com\u002Ftools\u002Fcherry-studio",[482],[54,1451,1452,1453],{},"Cursor IDE 博客 — Cherry Studio 完全指南（2025-03）",[448,1454,1455],{"href":1455,"rel":1456},"https:\u002F\u002Fwww.cursor-ide.com\u002Fblog\u002Fcherry-studio-guide",[482],{"title":498,"searchDepth":499,"depth":499,"links":1458},[1459,1460,1461,1462,1463,1464,1465,1466,1467,1468],{"id":38,"depth":502,"text":39},{"id":49,"depth":502,"text":49},{"id":117,"depth":502,"text":117},{"id":1158,"depth":502,"text":1159},{"id":191,"depth":502,"text":191},{"id":229,"depth":502,"text":229},{"id":369,"depth":502,"text":369},{"id":415,"depth":502,"text":416},{"id":442,"depth":502,"text":442},{"id":472,"depth":502,"text":472},"\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。",[1472,1475,1478,1481],{"q":1473,"a":1474},"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":1476,"a":1477},"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":1479,"a":1480},"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":1482,"a":1483},"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",[535,534],{},[545,544,543,1488],"android",[1490,1493],{"plan":549,"price":550,"features":1491,"notes":1492},"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":554,"price":1494,"features":1495,"notes":1496},"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",[561,1499],"onboarding\u002Fcursor-mcp-deep-integration",{"power":513,"ux":564,"price":564,"cn_support":564,"stability":513},{"title":250,"description":1470},"Cherry Studio 评测 2026：AI 客户端工具，多模型桌面助手，开源免费",[1504,1506,1508],{"name":1505,"url":1441,"accessed":571},"Cherry Studio 官网",{"name":1507,"url":1448,"accessed":571},"MBLUO Studio — Cherry Studio 评测",{"name":1509,"url":1455,"accessed":571},"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",[512,1073,1074,1513,580,583,1514],"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":1519,"title":30,"alternatives":1520,"api_compatible":1521,"body":1522,"category":512,"chinese_friendly":499,"cover":1955,"description":1956,"domestic":516,"extension":517,"faq":1957,"free":531,"github":1970,"languages":1971,"lastVerified":536,"meta":1972,"models":538,"navigation":531,"notSuitable":538,"opensource":531,"path":462,"pillar":540,"platforms":1973,"priceTable":1974,"pricing":1978,"published":559,"relatedPlaybooks":1979,"relatedReviews":538,"score":1980,"self_host":531,"seo":1981,"seoTitle":1982,"slug":14,"sources":1983,"stem":1990,"suitable":538,"tagline":1991,"tags":1992,"updated":571,"verdict":2000,"website":2001,"__hash__":2002},"tools\u002Ftools\u002Fcoding\u002Flocal\u002Follama.md",[15,567,13,12],[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，",[136,1530,1531],{},"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）。",[36,1537,49],{"id":49},[51,1539,1540,1546,1555,1561,1569,1584,1590,1596,1602],{},[54,1541,1542,1545],{},[57,1543,1544],{},"后台 Daemon","：开机自启，应用调用零延迟",[54,1547,1548,1551,1552],{},[57,1549,1550],{},"CLI","：",[136,1553,1554],{},"ollama pull \u002F run \u002F list \u002F show \u002F create \u002F serve",[54,1556,1557,1560],{},[57,1558,1559],{},"Modelfile","：类 Dockerfile 注册任意 GGUF，配 SYSTEM \u002F PARAMETER \u002F TEMPLATE",[54,1562,1563,1551,1566],{},[57,1564,1565],{},"OpenAI 兼容 API",[136,1567,1568],{},"http:\u002F\u002Flocalhost:11434\u002Fv1\u002Fchat\u002Fcompletions",[54,1570,1571,1551,1574,1577,1578,1577,1581],{},[57,1572,1573],{},"原生 API",[136,1575,1576],{},"\u002Fapi\u002Fchat","、",[136,1579,1580],{},"\u002Fapi\u002Fgenerate",[136,1582,1583],{},"\u002Fapi\u002Fembeddings",[54,1585,1586,1589],{},[57,1587,1588],{},"模型库","：官方注册表内置 Llama \u002F Qwen \u002F DeepSeek \u002F Gemma \u002F Mistral \u002F GPT-OSS 等",[54,1591,1592,1595],{},[57,1593,1594],{},"MLX 加速（Mac）","：0.19+ 起 M 系列自动用 MLX",[54,1597,1598,1601],{},[57,1599,1600],{},"量化","：默认 Q4_K_M、支持 Q5 \u002F Q8 \u002F FP16",[54,1603,1604,1607],{},[57,1605,1606],{},"跨平台","：Win \u002F Mac \u002F Linux 安装包，Docker 官方镜像",[36,1609,117],{"id":117},[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],{},[57,1620,129],{},[51,1622,1623,1629,1632,1635,1638],{},[54,1624,1625,1628],{},[136,1626,1627],{},"ollama run qwen3-coder:7b"," 一行起飞，3 秒进交互",[54,1630,1631],{},"REST API 配 Cursor \u002F Cline \u002F Continue 几乎全工具开箱即用",[54,1633,1634],{},"Modelfile 写自定义编码助手（low temperature + system prompt + 16K context）几分钟搞定",[54,1636,1637],{},"多模型并存，按需切换，内存占用合理",[54,1639,1640],{},"Mac M 系列 MLX 后比旧 GGUF 模式快显著",[41,1642,1643],{},[57,1644,163],{},[51,1646,1647,1656,1662,1669,1672],{},[54,1648,171,1649,1652,1653],{},[136,1650,1651],{},"num_ctx"," 偏小（2048），跑长上下文要在 Modelfile 加 ",[136,1654,1655],{},"PARAMETER num_ctx 16384",[54,1657,1658,1659],{},"模型默认走 0.0.0.0:11434 ↔ Docker 容器互访要 ",[136,1660,1661],{},"--add-host=host.docker.internal:host-gateway",[54,1663,1664,1665,1668],{},"国内 ",[136,1666,1667],{},"ollama.com\u002Flibrary"," 下载偶有慢，可手动 HF 下 GGUF + Modelfile 自建",[54,1670,1671],{},"多用户并发吞吐显著低于 vLLM",[54,1673,1674],{},"没有 GUI，模型浏览 \u002F 参数面板要走 LM Studio \u002F Open WebUI 配合",[36,1676,191],{"id":191},[193,1678,1679,1685,1691,1696,1702,1708],{},[54,1680,1681,1684],{},[136,1682,1683],{},"curl -fsSL https:\u002F\u002Follama.ai\u002Finstall.sh | sh","（Mac \u002F Linux）；Windows winget",[54,1686,1687,1690],{},[136,1688,1689],{},"ollama pull qwen3-coder:7b","（按需换模型）",[54,1692,1693,1695],{},[136,1694,1627],{}," 直接聊",[54,1697,1698,1699],{},"应用接入：baseURL = ",[136,1700,1701],{},"http:\u002F\u002Flocalhost:11434\u002Fv1",[54,1703,1704,1705],{},"自定义：写 Modelfile → ",[136,1706,1707],{},"ollama create my-coder -f Modelfile",[54,1709,1710],{},"进阶：装 Open WebUI 做前端 \u002F 多人共享",[36,1712,229],{"id":229},[231,1714,1715,1731],{},[234,1716,1717],{},[237,1718,1719,1721,1723,1725,1728],{},[240,1720,242],{},[240,1722,30],{},[240,1724,253],{},[240,1726,1727],{},"vLLM",[240,1729,1730],{},"llama.cpp",[255,1732,1733,1749,1764,1778,1793,1809,1824],{},[237,1734,1735,1737,1740,1743,1746],{},[260,1736,262],{},[260,1738,1739],{},"CLI + Daemon",[260,1741,1742],{},"GUI + Headless",[260,1744,1745],{},"Python Server",[260,1747,1748],{},"C++ 二进制",[237,1750,1751,1753,1756,1758,1761],{},[260,1752,191],{},[260,1754,1755],{},"极低",[260,1757,1755],{},[260,1759,1760],{},"中",[260,1762,1763],{},"高",[237,1765,1766,1769,1771,1774,1776],{},[260,1767,1768],{},"模型浏览",[260,1770,1550],{},[260,1772,1773],{},"✅ GUI",[260,1775,287],{},[260,1777,287],{},[237,1779,1780,1783,1786,1789,1791],{},[260,1781,1782],{},"OpenAI 兼容",[260,1784,1785],{},"✅ :11434",[260,1787,1788],{},"✅ :1234",[260,1790,284],{},[260,1792,284],{},[237,1794,1795,1798,1801,1804,1807],{},[260,1796,1797],{},"多用户吞吐",[260,1799,1800],{},"弱（~40 tok\u002Fs）",[260,1802,1803],{},"中（50–90）",[260,1805,1806],{},"强（800–12500）",[260,1808,1760],{},[237,1810,1811,1814,1817,1819,1822],{},[260,1812,1813],{},"MLX (Mac)",[260,1815,1816],{},"✅ 0.19+",[260,1818,284],{},[260,1820,1821],{},"部分",[260,1823,350],{},[237,1825,1826,1829,1831,1834,1837],{},[260,1827,1828],{},"开源",[260,1830,358],{},[260,1832,1833],{},"闭源",[260,1835,1836],{},"Apache 2.0",[260,1838,358],{},[36,1840,369],{"id":369},[51,1842,1843,1849,1855,1861,1867],{},[54,1844,1845,1848],{},[57,1846,1847],{},"num_ctx 一定要设","：默认 2K 太小，跑代码 \u002F 长文档要 16K+",[54,1850,1851,1854],{},[57,1852,1853],{},"Modelfile 模板别漏 TEMPLATE","：错的 chat template 会让模型输出乱码 \u002F 不停",[54,1856,1857,1860],{},[57,1858,1859],{},"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×",[54,1862,1863,1866],{},[57,1864,1865],{},"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",[54,1868,1869,1872],{},[57,1870,1871],{},"Mac 让它自动用 MLX","：升 0.19+；不要手动强制 GGUF + Metal",[36,1874,416],{"id":415},[51,1876,1877,1880,1883,1886,1889,1892,1895],{},[54,1878,1879],{},"✅ 应用 \u002F IDE 接本地模型（Cursor \u002F Cline \u002F Continue）",[54,1881,1882],{},"✅ 个人 \u002F 评估 \u002F 脚本自动化",[54,1884,1885],{},"✅ Modelfile 自定义系统 prompt + 参数",[54,1887,1888],{},"✅ Mac M 系列 MLX 用户",[54,1890,1891],{},"❌ 多用户并发生产服务（用 vLLM）",[54,1893,1894],{},"❌ GUI 调参 \u002F 模型浏览（配 LM Studio \u002F Open WebUI）",[54,1896,1897],{},"❌ 极致单卡吞吐研究（直接 llama.cpp \u002F vLLM）",[36,1899,442],{"id":442},[51,1901,1902,1906,1910,1914],{},[54,1903,1904],{},[448,1905,1423],{"href":1422},[54,1907,1908],{},[448,1909,986],{"href":539},[54,1911,1912],{},[448,1913,457],{"href":456},[54,1915,1916],{},[448,1917,469],{"href":468},[36,1919,472],{"id":472},[193,1921,1922,1929,1936],{},[54,1923,1924,1925],{},"Markaicode — Import GGUF Models into Ollama 2026（2026-05-15）",[448,1926,1927],{"href":1927,"rel":1928},"https:\u002F\u002Fmarkaicode.com\u002Fimport-gguf-models-ollama-guide",[482],[54,1930,1931,1932],{},"ComputingForGeeks — Ollama Models Cheat Sheet 2026 ",[448,1933,1934],{"href":1934,"rel":1935},"https:\u002F\u002Fcomputingforgeeks.com\u002Follama-models-cheat-sheet",[482],[54,1937,1938,1939],{},"Codersera — Ollama vs LM Studio vs vLLM vs llama.cpp vs MLX 2026 ",[448,1940,1941],{"href":1941,"rel":1942},"https:\u002F\u002Fcodersera.com\u002Fblog\u002Follama-vs-lm-studio-vs-vllm-vs-llama-cpp-vs-mlx-2026\u002F",[482],{"title":498,"searchDepth":499,"depth":499,"links":1944},[1945,1946,1947,1948,1949,1950,1951,1952,1953,1954],{"id":38,"depth":502,"text":39},{"id":49,"depth":502,"text":49},{"id":117,"depth":502,"text":117},{"id":1615,"depth":502,"text":1616},{"id":191,"depth":502,"text":191},{"id":229,"depth":502,"text":229},{"id":369,"depth":502,"text":369},{"id":415,"depth":502,"text":416},{"id":442,"depth":502,"text":442},{"id":472,"depth":502,"text":472},"\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",[534],{},[545,544,543,542],[1975],{"plan":549,"price":550,"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）",[561,562],{"power":513,"ux":513,"price":564,"cn_support":499,"stability":564},{"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":571},"Markaicode — Import GGUF 2026",{"name":1987,"url":1934,"accessed":571},"ComputingForGeeks — Ollama Cheat Sheet 2026",{"name":1989,"url":1941,"accessed":571},"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",[512,1993,1994,1995,1996,1997,1998,1999,583],"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":253,"alternatives":2005,"api_compatible":538,"body":2006,"category":512,"chinese_friendly":499,"cover":2417,"description":2418,"domestic":516,"extension":517,"faq":2419,"free":531,"github":538,"languages":2432,"lastVerified":536,"meta":2433,"models":538,"navigation":531,"notSuitable":538,"opensource":516,"path":1422,"pillar":540,"platforms":2434,"priceTable":2435,"pricing":2443,"published":559,"relatedPlaybooks":2444,"relatedReviews":538,"score":2445,"self_host":531,"seo":2446,"seoTitle":2447,"slug":15,"sources":2448,"stem":2454,"suitable":538,"tagline":2455,"tags":2456,"updated":571,"verdict":2460,"website":2461,"__hash__":2462},"tools\u002Ftools\u002Fcoding\u002Flocal\u002Flm-studio.md",[14,567,13,12],{"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 + ",[136,2014,2015],{},"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）。",[36,2021,49],{"id":49},[51,2023,2024,2030,2036,2042,2051,2060,2066,2072],{},[54,2025,2026,2029],{},[57,2027,2028],{},"模型浏览器","：内置 Hugging Face 检索，按 GGUF \u002F MLX \u002F 大小筛选、一键下载",[54,2031,2032,2035],{},[57,2033,2034],{},"聊天界面","：System Prompt \u002F temperature \u002F top-p \u002F context size 可视化调参",[54,2037,2038,2041],{},[57,2039,2040],{},"多模型并存 \u002F 切换","：同时加载多模型在不同会话中比较",[54,2043,2044,1551,2047,2050],{},[57,2045,2046],{},"OpenAI 兼容 Local Server",[136,2048,2049],{},"http:\u002F\u002Flocalhost:1234\u002Fv1","，任何 SDK 即接即用",[54,2052,2053,1551,2056,2059],{},[57,2054,2055],{},"Headless \u002F CLI",[136,2057,2058],{},"lms server start --port 1234","，无 GUI 可跑",[54,2061,2062,2065],{},[57,2063,2064],{},"PDF \u002F 文档对话","：内置基础 RAG，丢文件就能聊",[54,2067,2068,2071],{},[57,2069,2070],{},"MLX 原生支持（Mac）","：M1+ 上比 GGUF + Metal 快 30–50%",[54,2073,2074,2077],{},[57,2075,2076],{},"持续批处理","：Codersera 2026 测得 50–90 tok\u002Fs（消费级 GPU + 中等模型）",[36,2079,117],{"id":117},[51,2081,2082,2088],{},[54,2083,2084,2087],{},[57,2085,2086],{},"个人 \u002F 评估","：免费，全功能可用",[54,2089,2090,2093],{},[57,2091,2092],{},"商用","：邮件 \u002F 官网联系 LM Studio 团队",[1151,2095,2096],{},[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],{},[57,2106,129],{},[51,2108,2109,2112,2115,2118,2121],{},[54,2110,2111],{},"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",[54,2113,2114],{},"加载 7B Q4 模型 \u003C 3 秒，生成 ~75 tok\u002Fs",[54,2116,2117],{},"Local Server 开了 Cursor 直接接 baseURL → 本地代码补全零成本",[54,2119,2120],{},"MLX 版同模型 ~110 tok\u002Fs，差距显著",[54,2122,2123],{},"多窗口加载 2 个模型并排测，调 prompt 直观",[41,2125,2126],{},[57,2127,163],{},[51,2129,2130,2133,2136,2139,2142],{},[54,2131,2132],{},"模型库依赖 Hugging Face，国内访问要镜像 \u002F 代理",[54,2134,2135],{},"GPU 显存吃满后会自动 offload 到 CPU，无提示就慢下来",[54,2137,2138],{},"Headless 模式相对 Ollama 偏新，文档稍少",[54,2140,2141],{},"闭源应用（虽免费），不适合企业合规挂钩",[54,2143,2144],{},"中文 UI 可用但部分菜单仍英文",[36,2146,191],{"id":191},[193,2148,2149,2152,2155,2158,2161,2168],{},[54,2150,2151],{},"lmstudio.ai 下载（Mac \u002F Windows \u002F Linux）",[54,2153,2154],{},"打开 → Discover 标签 → 搜模型（如 qwen3-coder、deepseek-v3 GGUF\u002FMLX）→ Download",[54,2156,2157],{},"Chat 标签 → 选模型 → 调参聊天",[54,2159,2160],{},"Local Server 标签 → Start Server → 默认端口 1234",[54,2162,2163,2164,2167],{},"在你的应用里：",[136,2165,2166],{},"baseURL = \"http:\u002F\u002Flocalhost:1234\u002Fv1\"","，API Key 任意",[54,2169,2170,2171],{},"Headless：",[136,2172,2058],{},[36,2174,229],{"id":229},[231,2176,2177,2191],{},[234,2178,2179],{},[237,2180,2181,2183,2185,2187,2189],{},[240,2182,242],{},[240,2184,253],{},[240,2186,30],{},[240,2188,10],{},[240,2190,1730],{},[255,2192,2193,2209,2224,2238,2250,2262,2275,2287],{},[237,2194,2195,2197,2200,2203,2206],{},[260,2196,262],{},[260,2198,2199],{},"GUI + CLI",[260,2201,2202],{},"CLI Daemon",[260,2204,2205],{},"Docker UI",[260,2207,2208],{},"二进制",[237,2210,2211,2213,2216,2219,2221],{},[260,2212,1768],{},[260,2214,2215],{},"✅ 内置",[260,2217,2218],{},"CLI pull",[260,2220,287],{},[260,2222,2223],{},"手动",[237,2225,2226,2229,2231,2234,2236],{},[260,2227,2228],{},"参数调优 GUI",[260,2230,284],{},[260,2232,2233],{},"❌",[260,2235,1821],{},[260,2237,2233],{},[237,2239,2240,2242,2244,2246,2248],{},[260,2241,1565],{},[260,2243,1788],{},[260,2245,1785],{},[260,2247,284],{},[260,2249,284],{},[237,2251,2252,2254,2256,2258,2260],{},[260,2253,1813],{},[260,2255,284],{},[260,2257,1816],{},[260,2259,350],{},[260,2261,350],{},[237,2263,2264,2267,2269,2271,2273],{},[260,2265,2266],{},"多用户并发",[260,2268,304],{},[260,2270,304],{},[260,2272,284],{},[260,2274,1760],{},[237,2276,2277,2279,2281,2283,2285],{},[260,2278,1828],{},[260,2280,366],{},[260,2282,358],{},[260,2284,358],{},[260,2286,358],{},[237,2288,2289,2292,2294,2297,2299],{},[260,2290,2291],{},"上手难度",[260,2293,1755],{},[260,2295,2296],{},"低",[260,2298,1760],{},[260,2300,1763],{},[36,2302,369],{"id":369},[51,2304,2305,2311,2317,2323,2329],{},[54,2306,2307,2310],{},[57,2308,2309],{},"国内下模型走镜像","：HF 直连慢 \u002F 卡，配 HF_ENDPOINT=hf-mirror.com",[54,2312,2313,2316],{},[57,2314,2315],{},"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",[54,2318,2319,2322],{},[57,2320,2321],{},"MLX 优先（Mac M 系列）","：能下 MLX 版就别下 GGUF，速度差距明显",[54,2324,2325,2328],{},[57,2326,2327],{},"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",[54,2330,2331,2334],{},[57,2332,2333],{},"闭源合规要核","：企业内部使用前查 license；商用必须联系官方",[36,2336,416],{"id":415},[51,2338,2339,2342,2345,2348,2351,2354,2357],{},[54,2340,2341],{},"✅ 本地 LLM 入门 \u002F 评估",[54,2343,2344],{},"✅ Mac M 系列用户",[54,2346,2347],{},"✅ 想给 Cursor \u002F Cline 接本地 OpenAI 兼容 endpoint",[54,2349,2350],{},"✅ 需要 GUI 调参 \u002F 模型比较",[54,2352,2353],{},"❌ 多用户并发生产服务",[54,2355,2356],{},"❌ 嵌入式 \u002F 边缘设备",[54,2358,2359],{},"❌ 强合规 \u002F 必须开源审计",[36,2361,442],{"id":442},[51,2363,2364,2368,2372,2376],{},[54,2365,2366],{},[448,2367,463],{"href":462},[54,2369,2370],{},[448,2371,986],{"href":539},[54,2373,2374],{},[448,2375,457],{"href":456},[54,2377,2378],{},[448,2379,2381],{"href":2380},"\u002Fplaybook\u002Fonboarding\u002Fclaude-code-getting-started","Claude Code 上手 Playbook",[36,2383,472],{"id":472},[193,2385,2386,2393,2400],{},[54,2387,2388,2389],{},"LM Studio 官网 ",[448,2390,2391],{"href":2391,"rel":2392},"https:\u002F\u002Flmstudio.ai\u002F",[482],[54,2394,2395,2396],{},"Codersera — LM Studio Complete Guide 2026 ",[448,2397,2398],{"href":2398,"rel":2399},"https:\u002F\u002Fcodersera.com\u002Fblog\u002Flm-studio-complete-guide-2026\u002F",[482],[54,2401,1938,2402],{},[448,2403,1941],{"href":1941,"rel":2404},[482],{"title":498,"searchDepth":499,"depth":499,"links":2406},[2407,2408,2409,2410,2411,2412,2413,2414,2415,2416],{"id":38,"depth":502,"text":39},{"id":49,"depth":502,"text":49},{"id":117,"depth":502,"text":117},{"id":2101,"depth":502,"text":2102},{"id":191,"depth":502,"text":191},{"id":229,"depth":502,"text":229},{"id":369,"depth":502,"text":369},{"id":415,"depth":502,"text":416},{"id":442,"depth":502,"text":442},{"id":472,"depth":502,"text":472},"\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。",[534,535],{},[545,544,543],[2436,2439],{"plan":2086,"price":550,"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 商用咨询",[561,562],{"power":513,"ux":564,"price":564,"cn_support":499,"stability":513},{"title":253,"description":2418},"LM Studio 评测 2026：本地运行开源大模型，图形化界面，AI 模型管理",[2449,2451,2453],{"name":2450,"url":2391,"accessed":571},"LM Studio 官网",{"name":2452,"url":2398,"accessed":571},"Codersera — LM Studio Complete Guide 2026",{"name":1989,"url":1941,"accessed":571},"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",[512,2457,1997,1998,2458,2459,1999],"gui","llama-cpp","mac","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","Pj3jNb1Z4S55e91UkW1ZMgI86ps_Wm7yCc7zeXPF05U",1785660641137]