[{"data":1,"prerenderedAt":1056},["ShallowReactive",2],{"header-counts":3,"footer-counts":6,"compare-ollama-vs-open-webui":9,"compare-a-ollama":10,"compare-b-open-webui":566},{"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":499,"chinese_friendly":486,"cover":500,"description":501,"domestic":502,"extension":503,"faq":504,"free":502,"github":9,"languages":517,"lastVerified":9,"meta":519,"models":9,"navigation":520,"notSuitable":9,"opensource":520,"path":521,"pillar":522,"platforms":523,"priceTable":528,"pricing":534,"published":535,"relatedPlaybooks":536,"relatedReviews":9,"score":539,"self_host":520,"seo":542,"seoTitle":9,"slug":543,"sources":544,"stem":552,"suitable":9,"tagline":553,"tags":554,"updated":547,"verdict":563,"website":564,"__hash__":565},"tools\u002Ftools\u002Fcoding\u002Flocal\u002Follama.md","Ollama",[14,15,16,17],"coding\u002Flocal\u002Flm-studio","coding\u002Flocal\u002Fopen-webui","coding\u002Flocal\u002Fcherry-studio","coding\u002Flocal\u002Flobe-chat",{"type":19,"value":20,"toc":484},"minimark",[21,26,35,38,41,114,117,120,124,129,149,154,185,188,223,226,365,368,400,404,427,430,457,460],[22,23,25],"h2",{"id":24},"tldr","TL;DR",[27,28,29,30,34],"p",{},"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，",[31,32,33],"code",{},"ollama pull"," 一键拉。",[27,36,37],{},"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）。",[22,39,40],{"id":40},"核心能力",[42,43,44,52,61,67,75,90,96,102,108],"ul",{},[45,46,47,51],"li",{},[48,49,50],"strong",{},"后台 Daemon","：开机自启，应用调用零延迟",[45,53,54,57,58],{},[48,55,56],{},"CLI","：",[31,59,60],{},"ollama pull \u002F run \u002F list \u002F show \u002F create \u002F serve",[45,62,63,66],{},[48,64,65],{},"Modelfile","：类 Dockerfile 注册任意 GGUF，配 SYSTEM \u002F PARAMETER \u002F TEMPLATE",[45,68,69,57,72],{},[48,70,71],{},"OpenAI 兼容 API",[31,73,74],{},"http:\u002F\u002Flocalhost:11434\u002Fv1\u002Fchat\u002Fcompletions",[45,76,77,57,80,83,84,83,87],{},[48,78,79],{},"原生 API",[31,81,82],{},"\u002Fapi\u002Fchat","、",[31,85,86],{},"\u002Fapi\u002Fgenerate",[31,88,89],{},"\u002Fapi\u002Fembeddings",[45,91,92,95],{},[48,93,94],{},"模型库","：官方注册表内置 Llama \u002F Qwen \u002F DeepSeek \u002F Gemma \u002F Mistral \u002F GPT-OSS 等",[45,97,98,101],{},[48,99,100],{},"MLX 加速（Mac）","：0.19+ 起 M 系列自动用 MLX",[45,103,104,107],{},[48,105,106],{},"量化","：默认 Q4_K_M、支持 Q5 \u002F Q8 \u002F FP16",[45,109,110,113],{},[48,111,112],{},"跨平台","：Win \u002F Mac \u002F Linux 安装包，Docker 官方镜像",[22,115,116],{"id":116},"价格",[27,118,119],{},"完全免费、MIT 开源、商用免费。",[22,121,123],{"id":122},"实测m2-pro-qwen3-coder-7b-q4","实测（M2 Pro + Qwen3-Coder-7B Q4）",[27,125,126],{},[48,127,128],{},"亮点：",[42,130,131,137,140,143,146],{},[45,132,133,136],{},[31,134,135],{},"ollama run qwen3-coder:7b"," 一行起飞，3 秒进交互",[45,138,139],{},"REST API 配 Cursor \u002F Cline \u002F Continue 几乎全工具开箱即用",[45,141,142],{},"Modelfile 写自定义编码助手（low temperature + system prompt + 16K context）几分钟搞定",[45,144,145],{},"多模型并存，按需切换，内存占用合理",[45,147,148],{},"Mac M 系列 MLX 后比旧 GGUF 模式快显著",[27,150,151],{},[48,152,153],{},"踩坑：",[42,155,156,166,172,179,182],{},[45,157,158,159,162,163],{},"默认 ",[31,160,161],{},"num_ctx"," 偏小（2048），跑长上下文要在 Modelfile 加 ",[31,164,165],{},"PARAMETER num_ctx 16384",[45,167,168,169],{},"模型默认走 0.0.0.0:11434 ↔ Docker 容器互访要 ",[31,170,171],{},"--add-host=host.docker.internal:host-gateway",[45,173,174,175,178],{},"国内 ",[31,176,177],{},"ollama.com\u002Flibrary"," 下载偶有慢，可手动 HF 下 GGUF + Modelfile 自建",[45,180,181],{},"多用户并发吞吐显著低于 vLLM",[45,183,184],{},"没有 GUI，模型浏览 \u002F 参数面板要走 LM Studio \u002F Open WebUI 配合",[22,186,187],{"id":187},"上手",[189,190,191,197,203,208,214,220],"ol",{},[45,192,193,196],{},[31,194,195],{},"curl -fsSL https:\u002F\u002Follama.ai\u002Finstall.sh | sh","（Mac \u002F Linux）；Windows winget",[45,198,199,202],{},[31,200,201],{},"ollama pull qwen3-coder:7b","（按需换模型）",[45,204,205,207],{},[31,206,135],{}," 直接聊",[45,209,210,211],{},"应用接入：baseURL = ",[31,212,213],{},"http:\u002F\u002Flocalhost:11434\u002Fv1",[45,215,216,217],{},"自定义：写 Modelfile → ",[31,218,219],{},"ollama create my-coder -f Modelfile",[45,221,222],{},"进阶：装 Open WebUI 做前端 \u002F 多人共享",[22,224,225],{"id":225},"对比",[227,228,229,250],"table",{},[230,231,232],"thead",{},[233,234,235,239,241,244,247],"tr",{},[236,237,238],"th",{},"维度",[236,240,12],{},[236,242,243],{},"LM Studio",[236,245,246],{},"vLLM",[236,248,249],{},"llama.cpp",[251,252,253,271,286,301,317,333,349],"tbody",{},[233,254,255,259,262,265,268],{},[256,257,258],"td",{},"形态",[256,260,261],{},"CLI + Daemon",[256,263,264],{},"GUI + Headless",[256,266,267],{},"Python Server",[256,269,270],{},"C++ 二进制",[233,272,273,275,278,280,283],{},[256,274,187],{},[256,276,277],{},"极低",[256,279,277],{},[256,281,282],{},"中",[256,284,285],{},"高",[233,287,288,291,293,296,299],{},[256,289,290],{},"模型浏览",[256,292,56],{},[256,294,295],{},"✅ GUI",[256,297,298],{},"无",[256,300,298],{},[233,302,303,306,309,312,315],{},[256,304,305],{},"OpenAI 兼容",[256,307,308],{},"✅ :11434",[256,310,311],{},"✅ :1234",[256,313,314],{},"✅",[256,316,314],{},[233,318,319,322,325,328,331],{},[256,320,321],{},"多用户吞吐",[256,323,324],{},"弱（~40 tok\u002Fs）",[256,326,327],{},"中（50–90）",[256,329,330],{},"强（800–12500）",[256,332,282],{},[233,334,335,338,341,343,346],{},[256,336,337],{},"MLX (Mac)",[256,339,340],{},"✅ 0.19+",[256,342,314],{},[256,344,345],{},"部分",[256,347,348],{},"–",[233,350,351,354,357,360,363],{},[256,352,353],{},"开源",[256,355,356],{},"MIT",[256,358,359],{},"闭源",[256,361,362],{},"Apache 2.0",[256,364,356],{},[22,366,367],{"id":367},"避坑",[42,369,370,376,382,388,394],{},[45,371,372,375],{},[48,373,374],{},"num_ctx 一定要设","：默认 2K 太小，跑代码 \u002F 长文档要 16K+",[45,377,378,381],{},[48,379,380],{},"Modelfile 模板别漏 TEMPLATE","：错的 chat template 会让模型输出乱码 \u002F 不停",[45,383,384,387],{},[48,385,386],{},"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×",[45,389,390,393],{},[48,391,392],{},"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",[45,395,396,399],{},[48,397,398],{},"Mac 让它自动用 MLX","：升 0.19+；不要手动强制 GGUF + Metal",[22,401,403],{"id":402},"适合-不适合","适合 \u002F 不适合",[42,405,406,409,412,415,418,421,424],{},[45,407,408],{},"✅ 应用 \u002F IDE 接本地模型（Cursor \u002F Cline \u002F Continue）",[45,410,411],{},"✅ 个人 \u002F 评估 \u002F 脚本自动化",[45,413,414],{},"✅ Modelfile 自定义系统 prompt + 参数",[45,416,417],{},"✅ Mac M 系列 MLX 用户",[45,419,420],{},"❌ 多用户并发生产服务（用 vLLM）",[45,422,423],{},"❌ GUI 调参 \u002F 模型浏览（配 LM Studio \u002F Open WebUI）",[45,425,426],{},"❌ 极致单卡吞吐研究（直接 llama.cpp \u002F vLLM）",[22,428,429],{"id":429},"相关阅读",[42,431,432,439,445,451],{},[45,433,434],{},[435,436,438],"a",{"href":437},"\u002Ftools\u002Fcoding\u002Flocal\u002Flm-studio","LM Studio 评测",[45,440,441],{},[435,442,444],{"href":443},"\u002Ftools\u002Fcoding\u002Flocal\u002Fopen-webui","Open WebUI 评测",[45,446,447],{},[435,448,450],{"href":449},"\u002Ftools\u002Fcoding\u002Flocal\u002Fcherry-studio","Cherry Studio 评测",[45,452,453],{},[435,454,456],{"href":455},"\u002Fplaybook\u002Fonboarding\u002Frag-pipeline-build","RAG Pipeline 搭建 Playbook",[22,458,459],{"id":459},"来源",[189,461,462,470,477],{},[45,463,464,465],{},"Markaicode — Import GGUF Models into Ollama 2026（2026-05-15）",[435,466,467],{"href":467,"rel":468},"https:\u002F\u002Fmarkaicode.com\u002Fimport-gguf-models-ollama-guide",[469],"nofollow",[45,471,472,473],{},"ComputingForGeeks — Ollama Models Cheat Sheet 2026 ",[435,474,475],{"href":475,"rel":476},"https:\u002F\u002Fcomputingforgeeks.com\u002Follama-models-cheat-sheet",[469],[45,478,479,480],{},"Codersera — Ollama vs LM Studio vs vLLM vs llama.cpp vs MLX 2026 ",[435,481,482],{"href":482,"rel":483},"https:\u002F\u002Fcodersera.com\u002Fblog\u002Follama-vs-lm-studio-vs-vllm-vs-llama-cpp-vs-mlx-2026\u002F",[469],{"title":485,"searchDepth":486,"depth":486,"links":487},"",3,[488,490,491,492,493,494,495,496,497,498],{"id":24,"depth":489,"text":25},2,{"id":40,"depth":489,"text":40},{"id":116,"depth":489,"text":116},{"id":122,"depth":489,"text":123},{"id":187,"depth":489,"text":187},{"id":225,"depth":489,"text":225},{"id":367,"depth":489,"text":367},{"id":402,"depth":489,"text":403},{"id":429,"depth":489,"text":429},{"id":459,"depth":489,"text":459},"local","\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。",false,"md",[505,508,511,514],{"q":506,"a":507},"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":509,"a":510},"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":512,"a":513},"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":515,"a":516},"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。",[518],"en",{},true,"\u002Ftools\u002Fcoding\u002Flocal\u002Follama","coding",[524,525,526,527],"windows","macos","linux","docker",[529],{"plan":530,"price":531,"features":532,"notes":533},"开源版","免费","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）","2026-06-19",[537,538],"onboarding\u002Frag-pipeline-build","onboarding\u002Fclaude-code-getting-started",{"power":540,"ux":540,"price":541,"cn_support":486,"stability":541},4,5,{"title":12,"description":501},"coding\u002Flocal\u002Follama",[545,548,550],{"name":546,"url":467,"accessed":547},"Markaicode — Import GGUF 2026","2026-06-24",{"name":549,"url":475,"accessed":547},"ComputingForGeeks — Ollama Cheat Sheet 2026",{"name":551,"url":482,"accessed":547},"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",[499,555,556,557,558,559,560,561,562],"daemon","cli","rest-api","modelfile","gguf","mlx","openai-compatible","open-source","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","shirZzL900qiCQXzrJS1r3bv7XatM1q8hPc2mEoq88Y",{"id":567,"title":568,"alternatives":569,"api_compatible":9,"body":570,"category":499,"chinese_friendly":540,"cover":1005,"description":1006,"domestic":502,"extension":503,"faq":1007,"free":502,"github":9,"languages":1020,"lastVerified":9,"meta":1022,"models":9,"navigation":520,"notSuitable":9,"opensource":520,"path":443,"pillar":522,"platforms":1023,"priceTable":1025,"pricing":1034,"published":535,"relatedPlaybooks":1035,"relatedReviews":9,"score":1036,"self_host":520,"seo":1037,"seoTitle":1038,"slug":15,"sources":1039,"stem":1046,"suitable":9,"tagline":1047,"tags":1048,"updated":547,"verdict":1053,"website":1054,"__hash__":1055},"tools\u002Ftools\u002Fcoding\u002Flocal\u002Fopen-webui.md","Open WebUI",[17,16,543,14],{"type":19,"value":571,"toc":993},[572,574,577,580,582,644,646,649,653,657,685,689,713,715,749,751,875,877,920,922,945,947,968,970],[22,573,25],{"id":24},[27,575,576],{},"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。",[27,578,579],{},"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。",[22,581,40],{"id":40},[42,583,584,590,596,602,608,614,620,626,632,638],{},[45,585,586,589],{},[48,587,588],{},"多模型后端","：Ollama \u002F OpenAI \u002F vLLM \u002F Anthropic \u002F Groq \u002F LocalAI \u002F 任意 OpenAI 兼容",[45,591,592,595],{},[48,593,594],{},"多用户 + RBAC","：注册 \u002F 邀请 \u002F 角色权限 \u002F 工作区隔离",[45,597,598,601],{},[48,599,600],{},"RAG 知识库","：上传文档 \u002F 网址 \u002F SearXNG 联网搜索 → 向量化 → 对话引用",[45,603,604,607],{},[48,605,606],{},"Tools \u002F Functions","：Python 写函数即扩展（联网 \u002F 计算器 \u002F 自定义 API）",[45,609,610,613],{},[48,611,612],{},"mcpo","：MCP-to-OpenAPI 代理，任意 MCP 服务器接进来",[45,615,616,619],{},[48,617,618],{},"oikb","：知识库同步本地文件夹 \u002F GitHub \u002F S3 \u002F Confluence 等 40+ 源",[45,621,622,625],{},[48,623,624],{},"open-terminal \u002F cptr","：给 AI 真实终端 + 文件 + 沙箱执行",[45,627,628,631],{},[48,629,630],{},"图像生成","：Stable Diffusion \u002F DALL·E \u002F 自托管接入",[45,633,634,637],{},[48,635,636],{},"语音输入 \u002F TTS","：内置",[45,639,640,643],{},[48,641,642],{},"企业 LTS","：custom branding + SLA + 长期支持版本（联系销售）",[22,645,116],{"id":116},[27,647,648],{},"完全免费、MIT 开源、商用免费。Enterprise 提供品牌定制 + SLA + LTS。",[22,650,652],{"id":651},"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）",[27,654,655],{},[48,656,128],{},[42,658,659,666,669,676,679,682],{},[45,660,661,662,665],{},"单条 ",[31,663,664],{},"docker run"," 五分钟上线",[45,667,668],{},"自带的多用户 + 角色权限省去重新搭 Auth",[45,670,671,672,675],{},"RAG 直传 30 个 PDF 后向量化顺利，对话中 ",[31,673,674],{},"#知识库"," 引用准确",[45,677,678],{},"mcpo 把 GitHub MCP 服务器接进来，团队对话里直接 issue \u002F PR 操作",[45,680,681],{},"模型切换流畅，OpenAI + Ollama 并存",[45,683,684],{},"SearXNG 联网搜索给模型实时信息，过时知识截止问题缓解",[27,686,687],{},[48,688,153],{},[42,690,691,694,700,707,710],{},[45,692,693],{},"Docker 镜像 ~1.5GB，首次拉取偏慢",[45,695,158,696,699],{},[31,697,698],{},"0.0.0.0"," 公网暴露要加 HTTPS + 反代",[45,701,702,703,706],{},"嵌入模型 ",[31,704,705],{},"sentence-transformers"," 中文效果一般，建议换 bge-m3",[45,708,709],{},"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）",[45,711,712],{},"版本升级要看 changelog，部分 minor 含 breaking 改动",[22,714,187],{"id":187},[189,716,717,723,730,733,736,739,742],{},[45,718,719,720],{},"装 Docker → ",[31,721,722],{},"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",[45,724,725,726,729],{},"浏览器开 ",[31,727,728],{},"http:\u002F\u002Flocalhost:3000"," → 注册第一个账号（管理员）",[45,731,732],{},"设置 → Connections → 连接 Ollama \u002F 加 OpenAI Key",[45,734,735],{},"Models → Pull \u002F Discover 模型",[45,737,738],{},"Workspaces → 建知识库 → 上传文档",[45,740,741],{},"Tools → 启用 \u002F 写自定义函数",[45,743,744,745,748],{},"生产部署：Nginx 反代 + Let's Encrypt + 备份 ",[31,746,747],{},"\u002Fapp\u002Fbackend\u002Fdata"," volume",[22,750,225],{"id":225},[227,752,753,769],{},[230,754,755],{},[233,756,757,759,761,764,767],{},[236,758,238],{},[236,760,568],{},[236,762,763],{},"LobeChat",[236,765,766],{},"Cherry Studio",[236,768,243],{},[251,770,771,786,800,815,829,844,860],{},[233,772,773,775,778,781,784],{},[256,774,258],{},[256,776,777],{},"Docker \u002F 桌面",[256,779,780],{},"Web + 桌面",[256,782,783],{},"桌面",[256,785,783],{},[233,787,788,791,794,796,798],{},[256,789,790],{},"多用户",[256,792,793],{},"✅ 一等",[256,795,314],{},[256,797,298],{},[256,799,298],{},[233,801,802,805,808,810,812],{},[256,803,804],{},"RAG",[256,806,807],{},"✅ 强 + oikb",[256,809,314],{},[256,811,314],{},[256,813,814],{},"弱",[233,816,817,820,823,825,827],{},[256,818,819],{},"工具 \u002F MCP",[256,821,822],{},"✅ mcpo",[256,824,314],{},[256,826,314],{},[256,828,814],{},[233,830,831,834,837,840,842],{},[256,832,833],{},"自托管",[256,835,836],{},"✅ Docker \u002F K8s",[256,838,839],{},"✅ Docker",[256,841,298],{},[256,843,298],{},[233,845,846,849,852,855,858],{},[256,847,848],{},"GitHub Stars",[256,850,851],{},"126k+",[256,853,854],{},"72k+",[256,856,857],{},"60k+",[256,859,348],{},[233,861,862,865,867,869,872],{},[256,863,864],{},"开源协议",[256,866,356],{},[256,868,356],{},[256,870,871],{},"AGPL-3.0",[256,873,874],{},"闭源（免费）",[22,876,367],{"id":367},[42,878,879,885,893,902,908,914],{},[45,880,881,884],{},[48,882,883],{},"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",[45,886,887,892],{},[48,888,889,890,748],{},"备份 ",[31,891,747],{},"：知识库 \u002F 用户 \u002F 对话全在里面",[45,894,895,898,899,901],{},[48,896,897],{},"中文 RAG 换嵌入模型","：默认 ",[31,900,705],{}," 中文一般，配 bge-m3 或硅基流动嵌入 API",[45,903,904,907],{},[48,905,906],{},"mcpo 工具范围谨慎","：MCP 给 AI 真实能力，第三方服务器审一遍",[45,909,910,913],{},[48,911,912],{},"后端吞吐看 Ollama","：5+ 并发上 vLLM 后端，Ollama 单 worker 会排队",[45,915,916,919],{},[48,917,918],{},"升级前看 changelog","：weekly 更新，偶有 breaking",[22,921,403],{"id":402},[42,923,924,927,930,933,936,939,942],{},[45,925,926],{},"✅ 团队 \u002F 家庭 \u002F 公司多人共享 AI 平台",[45,928,929],{},"✅ 要 Web 端访问 \u002F 移动端兼容",[45,931,932],{},"✅ 自托管 \u002F 完全控制数据",[45,934,935],{},"✅ MCP \u002F 工具调用刚需",[45,937,938],{},"❌ 单人桌面体验（用 Cherry Studio）",[45,940,941],{},"❌ 零运维 \u002F 不愿碰 Docker",[45,943,944],{},"❌ iOS 原生 App 主力",[22,946,429],{"id":429},[42,948,949,955,959,964],{},[45,950,951],{},[435,952,954],{"href":953},"\u002Ftools\u002Fcoding\u002Flocal\u002Flobe-chat","LobeChat 评测",[45,956,957],{},[435,958,450],{"href":449},[45,960,961],{},[435,962,963],{"href":521},"Ollama 评测",[45,965,966],{},[435,967,456],{"href":455},[22,969,459],{"id":459},[189,971,972,979,986],{},[45,973,974,975],{},"Open WebUI 官方文档 ",[435,976,977],{"href":977,"rel":978},"https:\u002F\u002Fdocs.openwebui.com\u002F",[469],[45,980,981,982],{},"Local AI Master — Open WebUI Setup Guide 2026 ",[435,983,984],{"href":984,"rel":985},"https:\u002F\u002Flocalaimaster.com\u002Fblog\u002Fopen-webui-setup-guide",[469],[45,987,988,989],{},"AIToolDiscovery — Set Up Open-WebUI with Ollama 2026 ",[435,990,991],{"href":991,"rel":992},"https:\u002F\u002Fwww.aitooldiscovery.com\u002Fhow-to\u002Fsetup-open-webui-ollama",[469],{"title":485,"searchDepth":486,"depth":486,"links":994},[995,996,997,998,999,1000,1001,1002,1003,1004],{"id":24,"depth":489,"text":25},{"id":40,"depth":489,"text":40},{"id":116,"depth":489,"text":116},{"id":651,"depth":489,"text":652},{"id":187,"depth":489,"text":187},{"id":225,"depth":489,"text":225},{"id":367,"depth":489,"text":367},{"id":402,"depth":489,"text":403},{"id":429,"depth":489,"text":429},{"id":459,"depth":489,"text":459},"\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。",[1008,1011,1014,1017],{"q":1009,"a":1010},"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":1012,"a":1013},"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":1015,"a":1016},"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":1018,"a":1019},"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。",[518,1021],"zh",{},[527,526,525,524,1024],"kubernetes",[1026,1029],{"plan":530,"price":531,"features":1027,"notes":1028},"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":1030,"price":1031,"features":1032,"notes":1033},"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",[537,538],{"power":541,"ux":540,"price":541,"cn_support":540,"stability":541},{"title":568,"description":1006},"Open WebUI 评测 2026：自托管 ChatGPT 替代，Ollama 前端部署指南",[1040,1042,1044],{"name":1041,"url":977,"accessed":547},"Open WebUI 官方文档",{"name":1043,"url":984,"accessed":547},"Local AI Master — Open WebUI Setup Guide 2026",{"name":1045,"url":991,"accessed":547},"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",[499,1049,527,1050,1051,1052,562],"self-host","rag","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","JCKn_X0aojpl94LKJcqlbs0XSTAxv8S8JgKy70WtsO0",1784565442459]