[{"data":1,"prerenderedAt":1007},["ShallowReactive",2],{"header-counts":3,"footer-counts":6,"compare-jan-vs-ollama":8,"compare-a-jan":9,"compare-b-ollama":506},{"tools":4,"reviews":5},98,27,{"tools":4,"reviews":5,"playbooks":7,"news":5},22,null,{"id":10,"title":11,"alternatives":12,"api_compatible":8,"body":16,"category":470,"chinese_friendly":456,"cover":471,"description":472,"domestic":473,"extension":474,"faq":8,"free":473,"github":451,"languages":475,"lastVerified":477,"meta":478,"models":8,"navigation":479,"notSuitable":8,"opensource":479,"path":480,"pillar":481,"platforms":482,"priceTable":8,"pricing":486,"published":487,"relatedPlaybooks":8,"relatedReviews":8,"score":488,"self_host":473,"seo":491,"seoTitle":492,"slug":493,"sources":494,"stem":497,"suitable":8,"tagline":498,"tags":499,"updated":477,"verdict":504,"website":443,"__hash__":505},"tools\u002Ftools\u002Fcoding\u002Flocal\u002Fjan.md","Jan",[13,14,15],"coding\u002Flocal\u002Flm-studio","coding\u002Flocal\u002Fcherry-studio","coding\u002Flocal\u002Follama",{"type":17,"value":18,"toc":454},"minimark",[19,24,33,36,39,91,94,100,133,136,140,148,165,170,187,190,211,214,326,329,361,365,388,392,398,404,410,413,429,432,437],[20,21,23],"h2",{"id":22},"tldr","TL;DR",[25,26,27,28,32],"p",{},"Jan 是开源的本地 LLM 桌面客户端，定位是",[29,30,31],"strong",{},"ChatGPT 的离线替代品","。界面设计精美，操作体验接近 ChatGPT，支持 GGUF 模型一键下载、本地推理、多模型切换、插件扩展。AGPL 开源，完全免费。适合想要一个好看好用的本地 AI 聊天工具、隐私优先的用户。",[25,34,35],{},"适合：想要 ChatGPT 颜值和体验的本地替代、个人离线聊天、隐私敏感场景、非技术用户（GUI 友好）。不适合：需要 OpenAI 兼容 API 给应用接入（用 Ollama）、需要模型调参 \u002F 量化选择（用 LM Studio）、企业商用（AGPL 限制）。",[20,37,38],{"id":38},"核心能力",[40,41,42,49,55,61,67,73,79,85],"ul",{},[43,44,45,48],"li",{},[29,46,47],{},"ChatGPT 式界面","：聊天 UI 设计精美，多会话管理、Markdown 渲染、代码高亮",[43,50,51,54],{},[29,52,53],{},"一键下载模型","：内置模型市场，搜索 GGUF 模型点击下载，自动配置",[43,56,57,60],{},[29,58,59],{},"本地推理","：基于 llama.cpp，支持 CPU \u002F GPU 加速，完全离线运行",[43,62,63,66],{},[29,64,65],{},"多模型切换","：一个会话可切换不同模型对比输出，方便评估",[43,68,69,72],{},[29,70,71],{},"插件系统","：支持扩展功能，如网页搜索、文档分析、API 代理等",[43,74,75,78],{},[29,76,77],{},"远程 API 接入","：除了本地模型，也支持接 OpenAI \u002F Anthropic 等云端 API",[43,80,81,84],{},[29,82,83],{},"跨平台桌面 App","：Win \u002F Mac \u002F Linux 原生安装包，Electron 构建",[43,86,87,90],{},[29,88,89],{},"隐私优先","：所有数据本地存储，无遥测，无云端调用（本地模型模式）",[20,92,93],{"id":93},"价格",[95,96,97],"blockquote",{},[25,98,99],{},"以下信息为 2026-07-30 核实。",[101,102,103,118],"table",{},[104,105,106],"thead",{},[107,108,109,113,115],"tr",{},[110,111,112],"th",{},"方案",[110,114,93],{},[110,116,117],{},"说明",[119,120,121],"tbody",{},[107,122,123,127,130],{},[124,125,126],"td",{},"开源版",[124,128,129],{},"$0",[124,131,132],{},"完整功能，AGPL 协议",[25,134,135],{},"完全免费。注意 AGPL 协议：个人使用无限制，但二次开发 \u002F 商用需遵守开源传染条款。",[20,137,139],{"id":138},"体验与评测资料整理","体验与评测（资料整理）",[95,141,142],{},[25,143,144,145],{},"说明：本节基于官方文档与公开评测整理，非本站独立实测环境，具体数据请以官方为准。\n",[29,146,147],{},"亮点：",[40,149,150,153,156,159,162],{},[43,151,152],{},"界面设计是同类最佳：比 LM Studio \u002F GPT4All 好看很多，接近 ChatGPT 体验",[43,154,155],{},"模型下载体验顺滑：搜索 → 下载 → 使用，全程 GUI，零命令行",[43,157,158],{},"多模型对比实用：同一问题切换模型看不同回答，选模型很方便",[43,160,161],{},"插件系统有潜力：网页搜索插件让本地模型也能联网",[43,163,164],{},"支持云端 API 混用：本地模型 + GPT-4o 切换，一个客户端搞定",[25,166,167],{},[29,168,169],{},"踩坑：",[40,171,172,175,178,181,184],{},[43,173,174],{},"Electron 应用内存占用偏高，老设备偶有卡顿",[43,176,177],{},"模型管理不如 LM Studio：量化版本选择少，调参选项有限",[43,179,180],{},"API Server 功能弱：有 OpenAI 兼容端点但不如 Ollama 灵活",[43,182,183],{},"插件生态尚不成熟，可用插件不多",[43,185,186],{},"AGPL 协议对企业不友好，商用需注意合规",[20,188,189],{"id":189},"上手",[191,192,193,196,199,202,205,208],"ol",{},[43,194,195],{},"从 jan.ai 下载对应平台安装包",[43,197,198],{},"安装后打开 Jan，界面类似 ChatGPT",[43,200,201],{},"点击模型市场（Hub）→ 搜索推荐模型（Qwen2.5-7B \u002F Llama3.1-8B）",[43,203,204],{},"下载模型后，新建会话 → 选择模型 → 开始聊天",[43,206,207],{},"多模型对比：同一会话切换模型或开多个会话",[43,209,210],{},"接云端 API：Settings → API Keys → 填入 OpenAI Key 即可混用",[20,212,213],{"id":213},"对比",[101,215,216,234],{},[104,217,218],{},[107,219,220,223,225,228,231],{},[110,221,222],{},"维度",[110,224,11],{},[110,226,227],{},"LM Studio",[110,229,230],{},"Ollama",[110,232,233],{},"Cherry Studio",[119,235,236,252,266,281,295,309],{},[107,237,238,241,244,247,250],{},[124,239,240],{},"界面颜值",[124,242,243],{},"高",[124,245,246],{},"中",[124,248,249],{},"无 GUI",[124,251,243],{},[107,253,254,257,259,262,264],{},[124,255,256],{},"模型管理",[124,258,246],{},[124,260,261],{},"强",[124,263,261],{},[124,265,246],{},[107,267,268,271,274,277,279],{},[124,269,270],{},"API 接入",[124,272,273],{},"基础",[124,275,276],{},"✅",[124,278,261],{},[124,280,276],{},[107,282,283,286,288,291,293],{},[124,284,285],{},"插件扩展",[124,287,276],{},[124,289,290],{},"❌",[124,292,290],{},[124,294,276],{},[107,296,297,300,302,304,307],{},[124,298,299],{},"云端 API 混用",[124,301,276],{},[124,303,276],{},[124,305,306],{},"需配",[124,308,276],{},[107,310,311,314,317,320,323],{},[124,312,313],{},"开源协议",[124,315,316],{},"AGPL",[124,318,319],{},"闭源",[124,321,322],{},"MIT",[124,324,325],{},"Apache",[20,327,328],{"id":328},"避坑",[40,330,331,337,343,349,355],{},[43,332,333,336],{},[29,334,335],{},"别指望它做 API 服务器","：Jan 的 API Server 功能基础，给应用接入用 Ollama",[43,338,339,342],{},[29,340,341],{},"模型选对量化","：默认下载的可能不是最优量化，手动选 Q4_K_M 平衡速度质量",[43,344,345,348],{},[29,346,347],{},"Electron 吃内存","：8GB RAM 设备跑大模型 + Jan 本身会卡，关其他应用",[43,350,351,354],{},[29,352,353],{},"AGPL 商用注意","：企业内部署需法务确认 AGPL 合规",[43,356,357,360],{},[29,358,359],{},"插件别装太多","：部分插件质量参差，可能影响稳定性",[20,362,364],{"id":363},"适合-不适合","适合 \u002F 不适合",[40,366,367,370,373,376,379,382,385],{},[43,368,369],{},"✅ 想要 ChatGPT 颜值和体验的本地替代",[43,371,372],{},"✅ 个人离线聊天 \u002F 隐私优先场景",[43,374,375],{},"✅ 非技术用户（GUI 友好，零命令行）",[43,377,378],{},"✅ 本地 + 云端 API 混用需求",[43,380,381],{},"❌ 需要给应用 \u002F IDE 接入 API（用 Ollama）",[43,383,384],{},"❌ 需要精细模型调参 \u002F 量化管理（用 LM Studio）",[43,386,387],{},"❌ 企业商用（AGPL 限制）",[20,389,391],{"id":390},"faq","FAQ",[25,393,394,397],{},[29,395,396],{},"Q: Jan 和 LM Studio 怎么选？","\nA: 颜值和聊天体验选 Jan，模型管理和调参选 LM Studio。Jan 更像 ChatGPT，LM Studio 更像模型工具箱。两者都免费，可以都装。",[25,399,400,403],{},[29,401,402],{},"Q: 能给 Cursor \u002F Cline 接入吗？","\nA: Jan 有 OpenAI 兼容 API Server（默认端口 1337），理论上可以。但不如 Ollama 稳定灵活，推荐用 Ollama 做 API 服务器。",[25,405,406,409],{},[29,407,408],{},"Q: AGPL 协议影响个人使用吗？","\nA: 不影响。AGPL 主要约束网络服务分发场景。个人本地使用完全无限制。只有你把 Jan 改造后对外提供 SaaS 服务才需开源你的修改。",[20,411,412],{"id":412},"相关阅读",[25,414,415,420,421,420,425],{},[416,417,419],"a",{"href":418},"\u002Fcoding\u002Flocal\u002Fgpt4all.html","GPT4All"," · ",[416,422,424],{"href":423},"\u002Fcoding\u002Flocal\u002Fvllm.html","vLLM",[416,426,428],{"href":427},"\u002Fagent\u002Fdesktop\u002Fopen-interpreter.html","Open Interpreter",[20,430,431],{"id":431},"来源",[95,433,434],{},[25,435,436],{},"本文的价格、版本号与性能数据均参考以下官方渠道整理，可能随时间变动，请以官方实时信息为准。",[40,438,439,447],{},[43,440,441],{},[416,442,446],{"href":443,"rel":444},"https:\u002F\u002Fjan.ai",[445],"nofollow","官网",[43,448,449],{},[416,450,453],{"href":451,"rel":452},"https:\u002F\u002Fgithub.com\u002Fjanhq\u002Fjan",[445],"GitHub",{"title":455,"searchDepth":456,"depth":456,"links":457},"",3,[458,460,461,462,463,464,465,466,467,468,469],{"id":22,"depth":459,"text":23},2,{"id":38,"depth":459,"text":38},{"id":93,"depth":459,"text":93},{"id":138,"depth":459,"text":139},{"id":189,"depth":459,"text":189},{"id":213,"depth":459,"text":213},{"id":328,"depth":459,"text":328},{"id":363,"depth":459,"text":364},{"id":390,"depth":459,"text":391},{"id":412,"depth":459,"text":412},{"id":431,"depth":459,"text":431},"local","\u002Fimg\u002Ftools\u002Fjan.webp","Jan 真实评测：开源本地 LLM 桌面客户端（AGPL 协议），定位 ChatGPT 的离线替代，支持 GGUF 模型一键下载 + 本地推理 + 插件扩展。跨平台桌面 app，适合需要完全离线、隐私优先的本地 AI 聊天场景。",false,"md",[476],"en","2026-07-30",{},true,"\u002Ftools\u002Fcoding\u002Flocal\u002Fjan","coding",[483,484,485],"windows","macos","linux","Free \u002F 开源（AGPL）","2026-07-05",{"power":456,"ux":489,"price":490,"cn_support":456,"stability":456},4,5,{"title":11,"description":472},"Jan - 开源本地 LLM 桌面客户端评测 | AIHO","coding\u002Flocal\u002Fjan",[495,496],{"title":446,"url":443},{"title":453,"url":451},"tools\u002Fcoding\u002Flocal\u002Fjan","开源本地 LLM 桌面客户端，定位 ChatGPT 的离线替代",[470,500,501,502,503],"desktop","opensource","gguf","offline","颜值最高、最像 ChatGPT 的开源本地 LLM 客户端，离线聊天体验好；但 API 能力和模型管理不如 Ollama\u002FLM Studio，定位偏轻量个人使用。","SYftPw58WAWIAEA4qHpFo2Ki3Z1H9q97Rp4jQR560KE",{"id":507,"title":230,"alternatives":508,"api_compatible":8,"body":511,"category":470,"chinese_friendly":456,"cover":953,"description":954,"domestic":473,"extension":474,"faq":955,"free":473,"github":8,"languages":968,"lastVerified":8,"meta":969,"models":8,"navigation":479,"notSuitable":8,"opensource":479,"path":970,"pillar":481,"platforms":971,"priceTable":973,"pricing":978,"published":979,"relatedPlaybooks":980,"relatedReviews":8,"score":983,"self_host":479,"seo":984,"seoTitle":985,"slug":15,"sources":986,"stem":994,"suitable":8,"tagline":995,"tags":996,"updated":989,"verdict":1004,"website":1005,"__hash__":1006},"tools\u002Ftools\u002Fcoding\u002Flocal\u002Follama.md",[13,509,14,510],"coding\u002Flocal\u002Fopen-webui","coding\u002Flocal\u002Flobe-chat",{"type":17,"value":512,"toc":941},[513,515,523,526,528,598,600,603,607,611,631,635,666,668,702,704,829,831,863,865,888,890,916,918],[20,514,23],{"id":22},[25,516,517,518,522],{},"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，",[519,520,521],"code",{},"ollama pull"," 一键拉。",[25,524,525],{},"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）。",[20,527,38],{"id":38},[40,529,530,536,545,551,559,574,580,586,592],{},[43,531,532,535],{},[29,533,534],{},"后台 Daemon","：开机自启，应用调用零延迟",[43,537,538,541,542],{},[29,539,540],{},"CLI","：",[519,543,544],{},"ollama pull \u002F run \u002F list \u002F show \u002F create \u002F serve",[43,546,547,550],{},[29,548,549],{},"Modelfile","：类 Dockerfile 注册任意 GGUF，配 SYSTEM \u002F PARAMETER \u002F TEMPLATE",[43,552,553,541,556],{},[29,554,555],{},"OpenAI 兼容 API",[519,557,558],{},"http:\u002F\u002Flocalhost:11434\u002Fv1\u002Fchat\u002Fcompletions",[43,560,561,541,564,567,568,567,571],{},[29,562,563],{},"原生 API",[519,565,566],{},"\u002Fapi\u002Fchat","、",[519,569,570],{},"\u002Fapi\u002Fgenerate",[519,572,573],{},"\u002Fapi\u002Fembeddings",[43,575,576,579],{},[29,577,578],{},"模型库","：官方注册表内置 Llama \u002F Qwen \u002F DeepSeek \u002F Gemma \u002F Mistral \u002F GPT-OSS 等",[43,581,582,585],{},[29,583,584],{},"MLX 加速（Mac）","：0.19+ 起 M 系列自动用 MLX",[43,587,588,591],{},[29,589,590],{},"量化","：默认 Q4_K_M、支持 Q5 \u002F Q8 \u002F FP16",[43,593,594,597],{},[29,595,596],{},"跨平台","：Win \u002F Mac \u002F Linux 安装包，Docker 官方镜像",[20,599,93],{"id":93},[25,601,602],{},"完全免费、MIT 开源、商用免费。",[20,604,606],{"id":605},"实测m2-pro-qwen3-coder-7b-q4","实测（M2 Pro + Qwen3-Coder-7B Q4）",[25,608,609],{},[29,610,147],{},[40,612,613,619,622,625,628],{},[43,614,615,618],{},[519,616,617],{},"ollama run qwen3-coder:7b"," 一行起飞，3 秒进交互",[43,620,621],{},"REST API 配 Cursor \u002F Cline \u002F Continue 几乎全工具开箱即用",[43,623,624],{},"Modelfile 写自定义编码助手（low temperature + system prompt + 16K context）几分钟搞定",[43,626,627],{},"多模型并存，按需切换，内存占用合理",[43,629,630],{},"Mac M 系列 MLX 后比旧 GGUF 模式快显著",[25,632,633],{},[29,634,169],{},[40,636,637,647,653,660,663],{},[43,638,639,640,643,644],{},"默认 ",[519,641,642],{},"num_ctx"," 偏小（2048），跑长上下文要在 Modelfile 加 ",[519,645,646],{},"PARAMETER num_ctx 16384",[43,648,649,650],{},"模型默认走 0.0.0.0:11434 ↔ Docker 容器互访要 ",[519,651,652],{},"--add-host=host.docker.internal:host-gateway",[43,654,655,656,659],{},"国内 ",[519,657,658],{},"ollama.com\u002Flibrary"," 下载偶有慢，可手动 HF 下 GGUF + Modelfile 自建",[43,661,662],{},"多用户并发吞吐显著低于 vLLM",[43,664,665],{},"没有 GUI，模型浏览 \u002F 参数面板要走 LM Studio \u002F Open WebUI 配合",[20,667,189],{"id":189},[191,669,670,676,682,687,693,699],{},[43,671,672,675],{},[519,673,674],{},"curl -fsSL https:\u002F\u002Follama.ai\u002Finstall.sh | sh","（Mac \u002F Linux）；Windows winget",[43,677,678,681],{},[519,679,680],{},"ollama pull qwen3-coder:7b","（按需换模型）",[43,683,684,686],{},[519,685,617],{}," 直接聊",[43,688,689,690],{},"应用接入：baseURL = ",[519,691,692],{},"http:\u002F\u002Flocalhost:11434\u002Fv1",[43,694,695,696],{},"自定义：写 Modelfile → ",[519,697,698],{},"ollama create my-coder -f Modelfile",[43,700,701],{},"进阶：装 Open WebUI 做前端 \u002F 多人共享",[20,703,213],{"id":213},[101,705,706,721],{},[104,707,708],{},[107,709,710,712,714,716,718],{},[110,711,222],{},[110,713,230],{},[110,715,227],{},[110,717,424],{},[110,719,720],{},"llama.cpp",[119,722,723,740,753,768,783,799,815],{},[107,724,725,728,731,734,737],{},[124,726,727],{},"形态",[124,729,730],{},"CLI + Daemon",[124,732,733],{},"GUI + Headless",[124,735,736],{},"Python Server",[124,738,739],{},"C++ 二进制",[107,741,742,744,747,749,751],{},[124,743,189],{},[124,745,746],{},"极低",[124,748,746],{},[124,750,246],{},[124,752,243],{},[107,754,755,758,760,763,766],{},[124,756,757],{},"模型浏览",[124,759,540],{},[124,761,762],{},"✅ GUI",[124,764,765],{},"无",[124,767,765],{},[107,769,770,773,776,779,781],{},[124,771,772],{},"OpenAI 兼容",[124,774,775],{},"✅ :11434",[124,777,778],{},"✅ :1234",[124,780,276],{},[124,782,276],{},[107,784,785,788,791,794,797],{},[124,786,787],{},"多用户吞吐",[124,789,790],{},"弱（~40 tok\u002Fs）",[124,792,793],{},"中（50–90）",[124,795,796],{},"强（800–12500）",[124,798,246],{},[107,800,801,804,807,809,812],{},[124,802,803],{},"MLX (Mac)",[124,805,806],{},"✅ 0.19+",[124,808,276],{},[124,810,811],{},"部分",[124,813,814],{},"–",[107,816,817,820,822,824,827],{},[124,818,819],{},"开源",[124,821,322],{},[124,823,319],{},[124,825,826],{},"Apache 2.0",[124,828,322],{},[20,830,328],{"id":328},[40,832,833,839,845,851,857],{},[43,834,835,838],{},[29,836,837],{},"num_ctx 一定要设","：默认 2K 太小，跑代码 \u002F 长文档要 16K+",[43,840,841,844],{},[29,842,843],{},"Modelfile 模板别漏 TEMPLATE","：错的 chat template 会让模型输出乱码 \u002F 不停",[43,846,847,850],{},[29,848,849],{},"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×",[43,852,853,856],{},[29,854,855],{},"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",[43,858,859,862],{},[29,860,861],{},"Mac 让它自动用 MLX","：升 0.19+；不要手动强制 GGUF + Metal",[20,864,364],{"id":363},[40,866,867,870,873,876,879,882,885],{},[43,868,869],{},"✅ 应用 \u002F IDE 接本地模型（Cursor \u002F Cline \u002F Continue）",[43,871,872],{},"✅ 个人 \u002F 评估 \u002F 脚本自动化",[43,874,875],{},"✅ Modelfile 自定义系统 prompt + 参数",[43,877,878],{},"✅ Mac M 系列 MLX 用户",[43,880,881],{},"❌ 多用户并发生产服务（用 vLLM）",[43,883,884],{},"❌ GUI 调参 \u002F 模型浏览（配 LM Studio \u002F Open WebUI）",[43,886,887],{},"❌ 极致单卡吞吐研究（直接 llama.cpp \u002F vLLM）",[20,889,412],{"id":412},[40,891,892,898,904,910],{},[43,893,894],{},[416,895,897],{"href":896},"\u002Ftools\u002Fcoding\u002Flocal\u002Flm-studio","LM Studio 评测",[43,899,900],{},[416,901,903],{"href":902},"\u002Ftools\u002Fcoding\u002Flocal\u002Fopen-webui","Open WebUI 评测",[43,905,906],{},[416,907,909],{"href":908},"\u002Ftools\u002Fcoding\u002Flocal\u002Fcherry-studio","Cherry Studio 评测",[43,911,912],{},[416,913,915],{"href":914},"\u002Fplaybook\u002Fonboarding\u002Frag-pipeline-build","RAG Pipeline 搭建 Playbook",[20,917,431],{"id":431},[191,919,920,927,934],{},[43,921,922,923],{},"Markaicode — Import GGUF Models into Ollama 2026（2026-05-15）",[416,924,925],{"href":925,"rel":926},"https:\u002F\u002Fmarkaicode.com\u002Fimport-gguf-models-ollama-guide",[445],[43,928,929,930],{},"ComputingForGeeks — Ollama Models Cheat Sheet 2026 ",[416,931,932],{"href":932,"rel":933},"https:\u002F\u002Fcomputingforgeeks.com\u002Follama-models-cheat-sheet",[445],[43,935,936,937],{},"Codersera — Ollama vs LM Studio vs vLLM vs llama.cpp vs MLX 2026 ",[416,938,939],{"href":939,"rel":940},"https:\u002F\u002Fcodersera.com\u002Fblog\u002Follama-vs-lm-studio-vs-vllm-vs-llama-cpp-vs-mlx-2026\u002F",[445],{"title":455,"searchDepth":456,"depth":456,"links":942},[943,944,945,946,947,948,949,950,951,952],{"id":22,"depth":459,"text":23},{"id":38,"depth":459,"text":38},{"id":93,"depth":459,"text":93},{"id":605,"depth":459,"text":606},{"id":189,"depth":459,"text":189},{"id":213,"depth":459,"text":213},{"id":328,"depth":459,"text":328},{"id":363,"depth":459,"text":364},{"id":412,"depth":459,"text":412},{"id":431,"depth":459,"text":431},"\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。",[956,959,962,965],{"q":957,"a":958},"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":960,"a":961},"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":963,"a":964},"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":966,"a":967},"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。",[476],{},"\u002Ftools\u002Fcoding\u002Flocal\u002Follama",[483,484,485,972],"docker",[974],{"plan":126,"price":975,"features":976,"notes":977},"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",[981,982],"onboarding\u002Frag-pipeline-build","onboarding\u002Fclaude-code-getting-started",{"power":489,"ux":489,"price":490,"cn_support":456,"stability":490},{"title":230,"description":954},"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",[987,990,992],{"name":988,"url":925,"accessed":989},"Markaicode — Import GGUF 2026","2026-06-24",{"name":991,"url":932,"accessed":989},"ComputingForGeeks — Ollama Cheat Sheet 2026",{"name":993,"url":939,"accessed":989},"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",[470,997,998,999,1000,502,1001,1002,1003],"daemon","cli","rest-api","modelfile","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","7WgXNX9uMzSH_c-dUkicVopZk_g1z8HaB_8FmD9fBps",1785428443719]