[{"data":1,"prerenderedAt":1031},["ShallowReactive",2],{"header-counts":3,"footer-counts":6,"compare-lm-studio-vs-ollama":9,"compare-a-lm-studio":10,"compare-b-ollama":561},{"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":493,"chinese_friendly":480,"cover":494,"description":495,"domestic":496,"extension":497,"faq":498,"free":496,"github":9,"languages":511,"lastVerified":9,"meta":514,"models":9,"navigation":515,"notSuitable":9,"opensource":496,"path":516,"pillar":517,"platforms":518,"priceTable":522,"pricing":531,"published":532,"relatedPlaybooks":533,"relatedReviews":9,"score":536,"self_host":515,"seo":539,"seoTitle":9,"slug":540,"sources":541,"stem":549,"suitable":9,"tagline":550,"tags":551,"updated":544,"verdict":558,"website":559,"__hash__":560},"tools\u002Ftools\u002Fcoding\u002Flocal\u002Flm-studio.md","LM Studio",[14,15,16,17],"coding\u002Flocal\u002Follama","coding\u002Flocal\u002Fopen-webui","coding\u002Flocal\u002Fcherry-studio","coding\u002Flocal\u002Flobe-chat",{"type":19,"value":20,"toc":478},"minimark",[21,26,35,38,41,101,104,118,124,128,133,150,155,172,175,202,205,359,362,394,398,421,424,451,454],[22,23,25],"h2",{"id":24},"tldr","TL;DR",[27,28,29,30,34],"p",{},"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 + ",[31,32,33],"code",{},"lms"," CLI，可在服务器跑 OpenAI 兼容 API（默认 :1234）。个人 \u002F 评估完全免费，商用咨询。",[27,36,37],{},"适合：本地 LLM 入门 \u002F 评估、Mac 用户、需要 GUI 调参 \u002F 模型比较、想给 IDE \u002F 应用接本地 OpenAI 兼容 endpoint 的开发者。不适合：多用户并发生产服务（用 vLLM）、嵌入式 \u002F 边缘部署（用 llama.cpp）、纯 CLI 工作流（用 Ollama）。",[22,39,40],{"id":40},"核心能力",[42,43,44,52,58,64,74,83,89,95],"ul",{},[45,46,47,51],"li",{},[48,49,50],"strong",{},"模型浏览器","：内置 Hugging Face 检索，按 GGUF \u002F MLX \u002F 大小筛选、一键下载",[45,53,54,57],{},[48,55,56],{},"聊天界面","：System Prompt \u002F temperature \u002F top-p \u002F context size 可视化调参",[45,59,60,63],{},[48,61,62],{},"多模型并存 \u002F 切换","：同时加载多模型在不同会话中比较",[45,65,66,69,70,73],{},[48,67,68],{},"OpenAI 兼容 Local Server","：",[31,71,72],{},"http:\u002F\u002Flocalhost:1234\u002Fv1","，任何 SDK 即接即用",[45,75,76,69,79,82],{},[48,77,78],{},"Headless \u002F CLI",[31,80,81],{},"lms server start --port 1234","，无 GUI 可跑",[45,84,85,88],{},[48,86,87],{},"PDF \u002F 文档对话","：内置基础 RAG，丢文件就能聊",[45,90,91,94],{},[48,92,93],{},"MLX 原生支持（Mac）","：M1+ 上比 GGUF + Metal 快 30–50%",[45,96,97,100],{},[48,98,99],{},"持续批处理","：Codersera 2026 测得 50–90 tok\u002Fs（消费级 GPU + 中等模型）",[22,102,103],{"id":103},"价格",[42,105,106,112],{},[45,107,108,111],{},[48,109,110],{},"个人 \u002F 评估","：免费，全功能可用",[45,113,114,117],{},[48,115,116],{},"商用","：邮件 \u002F 官网联系 LM Studio 团队",[119,120,121],"blockquote",{},[27,122,123],{},"模型本身免费（开源权重），LM Studio 不抽水任何 token 费用。",[22,125,127],{"id":126},"实测mac-m2-pro-qwen3-coder-7b-gguf-q4_k_m","实测（Mac M2 Pro + Qwen3-Coder-7B GGUF Q4_K_M）",[27,129,130],{},[48,131,132],{},"亮点：",[42,134,135,138,141,144,147],{},[45,136,137],{},"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",[45,139,140],{},"加载 7B Q4 模型 \u003C 3 秒，生成 ~75 tok\u002Fs",[45,142,143],{},"Local Server 开了 Cursor 直接接 baseURL → 本地代码补全零成本",[45,145,146],{},"MLX 版同模型 ~110 tok\u002Fs，差距显著",[45,148,149],{},"多窗口加载 2 个模型并排测，调 prompt 直观",[27,151,152],{},[48,153,154],{},"踩坑：",[42,156,157,160,163,166,169],{},[45,158,159],{},"模型库依赖 Hugging Face，国内访问要镜像 \u002F 代理",[45,161,162],{},"GPU 显存吃满后会自动 offload 到 CPU，无提示就慢下来",[45,164,165],{},"Headless 模式相对 Ollama 偏新，文档稍少",[45,167,168],{},"闭源应用（虽免费），不适合企业合规挂钩",[45,170,171],{},"中文 UI 可用但部分菜单仍英文",[22,173,174],{"id":174},"上手",[176,177,178,181,184,187,190,197],"ol",{},[45,179,180],{},"lmstudio.ai 下载（Mac \u002F Windows \u002F Linux）",[45,182,183],{},"打开 → Discover 标签 → 搜模型（如 qwen3-coder、deepseek-v3 GGUF\u002FMLX）→ Download",[45,185,186],{},"Chat 标签 → 选模型 → 调参聊天",[45,188,189],{},"Local Server 标签 → Start Server → 默认端口 1234",[45,191,192,193,196],{},"在你的应用里：",[31,194,195],{},"baseURL = \"http:\u002F\u002Flocalhost:1234\u002Fv1\"","，API Key 任意",[45,198,199,200],{},"Headless：",[31,201,81],{},[22,203,204],{"id":204},"对比",[206,207,208,229],"table",{},[209,210,211],"thead",{},[212,213,214,218,220,223,226],"tr",{},[215,216,217],"th",{},"维度",[215,219,12],{},[215,221,222],{},"Ollama",[215,224,225],{},"Open WebUI",[215,227,228],{},"llama.cpp",[230,231,232,250,267,283,298,313,328,343],"tbody",{},[212,233,234,238,241,244,247],{},[235,236,237],"td",{},"形态",[235,239,240],{},"GUI + CLI",[235,242,243],{},"CLI Daemon",[235,245,246],{},"Docker UI",[235,248,249],{},"二进制",[212,251,252,255,258,261,264],{},[235,253,254],{},"模型浏览",[235,256,257],{},"✅ 内置",[235,259,260],{},"CLI pull",[235,262,263],{},"无",[235,265,266],{},"手动",[212,268,269,272,275,278,281],{},[235,270,271],{},"参数调优 GUI",[235,273,274],{},"✅",[235,276,277],{},"❌",[235,279,280],{},"部分",[235,282,277],{},[212,284,285,288,291,294,296],{},[235,286,287],{},"OpenAI 兼容 API",[235,289,290],{},"✅ :1234",[235,292,293],{},"✅ :11434",[235,295,274],{},[235,297,274],{},[212,299,300,303,305,308,311],{},[235,301,302],{},"MLX (Mac)",[235,304,274],{},[235,306,307],{},"✅ 0.19+",[235,309,310],{},"–",[235,312,310],{},[212,314,315,318,321,323,325],{},[235,316,317],{},"多用户并发",[235,319,320],{},"弱",[235,322,320],{},[235,324,274],{},[235,326,327],{},"中",[212,329,330,333,336,339,341],{},[235,331,332],{},"开源",[235,334,335],{},"闭源（免费）",[235,337,338],{},"MIT",[235,340,338],{},[235,342,338],{},[212,344,345,348,351,354,356],{},[235,346,347],{},"上手难度",[235,349,350],{},"极低",[235,352,353],{},"低",[235,355,327],{},[235,357,358],{},"高",[22,360,361],{"id":361},"避坑",[42,363,364,370,376,382,388],{},[45,365,366,369],{},[48,367,368],{},"国内下模型走镜像","：HF 直连慢 \u002F 卡，配 HF_ENDPOINT=hf-mirror.com",[45,371,372,375],{},[48,373,374],{},"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",[45,377,378,381],{},[48,379,380],{},"MLX 优先（Mac M 系列）","：能下 MLX 版就别下 GGUF，速度差距明显",[45,383,384,387],{},[48,385,386],{},"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",[45,389,390,393],{},[48,391,392],{},"闭源合规要核","：企业内部使用前查 license；商用必须联系官方",[22,395,397],{"id":396},"适合-不适合","适合 \u002F 不适合",[42,399,400,403,406,409,412,415,418],{},[45,401,402],{},"✅ 本地 LLM 入门 \u002F 评估",[45,404,405],{},"✅ Mac M 系列用户",[45,407,408],{},"✅ 想给 Cursor \u002F Cline 接本地 OpenAI 兼容 endpoint",[45,410,411],{},"✅ 需要 GUI 调参 \u002F 模型比较",[45,413,414],{},"❌ 多用户并发生产服务",[45,416,417],{},"❌ 嵌入式 \u002F 边缘设备",[45,419,420],{},"❌ 强合规 \u002F 必须开源审计",[22,422,423],{"id":423},"相关阅读",[42,425,426,433,439,445],{},[45,427,428],{},[429,430,432],"a",{"href":431},"\u002Ftools\u002Fcoding\u002Flocal\u002Follama","Ollama 评测",[45,434,435],{},[429,436,438],{"href":437},"\u002Ftools\u002Fcoding\u002Flocal\u002Fopen-webui","Open WebUI 评测",[45,440,441],{},[429,442,444],{"href":443},"\u002Ftools\u002Fcoding\u002Flocal\u002Fcherry-studio","Cherry Studio 评测",[45,446,447],{},[429,448,450],{"href":449},"\u002Fplaybook\u002Fonboarding\u002Fclaude-code-getting-started","Claude Code 上手 Playbook",[22,452,453],{"id":453},"来源",[176,455,456,464,471],{},[45,457,458,459],{},"LM Studio 官网 ",[429,460,461],{"href":461,"rel":462},"https:\u002F\u002Flmstudio.ai\u002F",[463],"nofollow",[45,465,466,467],{},"Codersera — LM Studio Complete Guide 2026 ",[429,468,469],{"href":469,"rel":470},"https:\u002F\u002Fcodersera.com\u002Fblog\u002Flm-studio-complete-guide-2026\u002F",[463],[45,472,473,474],{},"Codersera — Ollama vs LM Studio vs vLLM vs llama.cpp vs MLX 2026 ",[429,475,476],{"href":476,"rel":477},"https:\u002F\u002Fcodersera.com\u002Fblog\u002Follama-vs-lm-studio-vs-vllm-vs-llama-cpp-vs-mlx-2026\u002F",[463],{"title":479,"searchDepth":480,"depth":480,"links":481},"",3,[482,484,485,486,487,488,489,490,491,492],{"id":24,"depth":483,"text":25},2,{"id":40,"depth":483,"text":40},{"id":103,"depth":483,"text":103},{"id":126,"depth":483,"text":127},{"id":174,"depth":483,"text":174},{"id":204,"depth":483,"text":204},{"id":361,"depth":483,"text":361},{"id":396,"depth":483,"text":397},{"id":423,"depth":483,"text":423},{"id":453,"depth":483,"text":453},"local","\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。",false,"md",[499,502,505,508],{"q":500,"a":501},"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":503,"a":504},"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":506,"a":507},"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":509,"a":510},"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。",[512,513],"en","zh",{},true,"\u002Ftools\u002Fcoding\u002Flocal\u002Flm-studio","coding",[519,520,521],"windows","macos","linux",[523,527],{"plan":110,"price":524,"features":525,"notes":526},"免费","全功能 GUI + Headless API + GGUF\u002FMLX","供个人 \u002F 评估使用",{"plan":116,"price":528,"features":529,"notes":530},"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","2026-06-19",[534,535],"onboarding\u002Frag-pipeline-build","onboarding\u002Fclaude-code-getting-started",{"power":537,"ux":538,"price":538,"cn_support":480,"stability":537},4,5,{"title":12,"description":495},"coding\u002Flocal\u002Flm-studio",[542,545,547],{"name":543,"url":461,"accessed":544},"LM Studio 官网","2026-06-24",{"name":546,"url":469,"accessed":544},"Codersera — LM Studio Complete Guide 2026",{"name":548,"url":476,"accessed":544},"Codersera — Ollama vs LM Studio vs vLLM 2026","tools\u002Fcoding\u002Flocal\u002Flm-studio","本地 LLM 的 GUI 首选——模型浏览器 + GGUF\u002FMLX 推理 + OpenAI 兼容 API + Mac 原生优化",[493,552,553,554,555,556,557],"gui","gguf","mlx","llama-cpp","mac","openai-compatible","Mac \u002F Windows 桌面本地 LLM 的 GUI 首选——上手最快、模型浏览最舒服、自带 OpenAI 兼容 API。批量服务 \u002F 多用户场景用 vLLM；纯 CLI \u002F 嵌入应用走 Ollama。","https:\u002F\u002Flmstudio.ai","aNdpYFeU-2Jf8ElPrgRWVGVb70JZJ6MzrXXg5Tc6t3s",{"id":562,"title":222,"alternatives":563,"api_compatible":9,"body":564,"category":493,"chinese_friendly":480,"cover":986,"description":987,"domestic":496,"extension":497,"faq":988,"free":496,"github":9,"languages":1001,"lastVerified":9,"meta":1002,"models":9,"navigation":515,"notSuitable":9,"opensource":515,"path":431,"pillar":517,"platforms":1003,"priceTable":1005,"pricing":1010,"published":532,"relatedPlaybooks":1011,"relatedReviews":9,"score":1012,"self_host":515,"seo":1013,"seoTitle":9,"slug":14,"sources":1014,"stem":1020,"suitable":9,"tagline":1021,"tags":1022,"updated":544,"verdict":1028,"website":1029,"__hash__":1030},"tools\u002Ftools\u002Fcoding\u002Flocal\u002Follama.md",[540,15,16,17],{"type":19,"value":565,"toc":974},[566,568,575,578,580,648,650,653,657,661,681,685,716,718,752,754,869,871,903,905,928,930,951,953],[22,567,25],{"id":24},[27,569,570,571,574],{},"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，",[31,572,573],{},"ollama pull"," 一键拉。",[27,576,577],{},"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）。",[22,579,40],{"id":40},[42,581,582,588,596,602,609,624,630,636,642],{},[45,583,584,587],{},[48,585,586],{},"后台 Daemon","：开机自启，应用调用零延迟",[45,589,590,69,593],{},[48,591,592],{},"CLI",[31,594,595],{},"ollama pull \u002F run \u002F list \u002F show \u002F create \u002F serve",[45,597,598,601],{},[48,599,600],{},"Modelfile","：类 Dockerfile 注册任意 GGUF，配 SYSTEM \u002F PARAMETER \u002F TEMPLATE",[45,603,604,69,606],{},[48,605,287],{},[31,607,608],{},"http:\u002F\u002Flocalhost:11434\u002Fv1\u002Fchat\u002Fcompletions",[45,610,611,69,614,617,618,617,621],{},[48,612,613],{},"原生 API",[31,615,616],{},"\u002Fapi\u002Fchat","、",[31,619,620],{},"\u002Fapi\u002Fgenerate",[31,622,623],{},"\u002Fapi\u002Fembeddings",[45,625,626,629],{},[48,627,628],{},"模型库","：官方注册表内置 Llama \u002F Qwen \u002F DeepSeek \u002F Gemma \u002F Mistral \u002F GPT-OSS 等",[45,631,632,635],{},[48,633,634],{},"MLX 加速（Mac）","：0.19+ 起 M 系列自动用 MLX",[45,637,638,641],{},[48,639,640],{},"量化","：默认 Q4_K_M、支持 Q5 \u002F Q8 \u002F FP16",[45,643,644,647],{},[48,645,646],{},"跨平台","：Win \u002F Mac \u002F Linux 安装包，Docker 官方镜像",[22,649,103],{"id":103},[27,651,652],{},"完全免费、MIT 开源、商用免费。",[22,654,656],{"id":655},"实测m2-pro-qwen3-coder-7b-q4","实测（M2 Pro + Qwen3-Coder-7B Q4）",[27,658,659],{},[48,660,132],{},[42,662,663,669,672,675,678],{},[45,664,665,668],{},[31,666,667],{},"ollama run qwen3-coder:7b"," 一行起飞，3 秒进交互",[45,670,671],{},"REST API 配 Cursor \u002F Cline \u002F Continue 几乎全工具开箱即用",[45,673,674],{},"Modelfile 写自定义编码助手（low temperature + system prompt + 16K context）几分钟搞定",[45,676,677],{},"多模型并存，按需切换，内存占用合理",[45,679,680],{},"Mac M 系列 MLX 后比旧 GGUF 模式快显著",[27,682,683],{},[48,684,154],{},[42,686,687,697,703,710,713],{},[45,688,689,690,693,694],{},"默认 ",[31,691,692],{},"num_ctx"," 偏小（2048），跑长上下文要在 Modelfile 加 ",[31,695,696],{},"PARAMETER num_ctx 16384",[45,698,699,700],{},"模型默认走 0.0.0.0:11434 ↔ Docker 容器互访要 ",[31,701,702],{},"--add-host=host.docker.internal:host-gateway",[45,704,705,706,709],{},"国内 ",[31,707,708],{},"ollama.com\u002Flibrary"," 下载偶有慢，可手动 HF 下 GGUF + Modelfile 自建",[45,711,712],{},"多用户并发吞吐显著低于 vLLM",[45,714,715],{},"没有 GUI，模型浏览 \u002F 参数面板要走 LM Studio \u002F Open WebUI 配合",[22,717,174],{"id":174},[176,719,720,726,732,737,743,749],{},[45,721,722,725],{},[31,723,724],{},"curl -fsSL https:\u002F\u002Follama.ai\u002Finstall.sh | sh","（Mac \u002F Linux）；Windows winget",[45,727,728,731],{},[31,729,730],{},"ollama pull qwen3-coder:7b","（按需换模型）",[45,733,734,736],{},[31,735,667],{}," 直接聊",[45,738,739,740],{},"应用接入：baseURL = ",[31,741,742],{},"http:\u002F\u002Flocalhost:11434\u002Fv1",[45,744,745,746],{},"自定义：写 Modelfile → ",[31,747,748],{},"ollama create my-coder -f Modelfile",[45,750,751],{},"进阶：装 Open WebUI 做前端 \u002F 多人共享",[22,753,204],{"id":204},[206,755,756,771],{},[209,757,758],{},[212,759,760,762,764,766,769],{},[215,761,217],{},[215,763,222],{},[215,765,12],{},[215,767,768],{},"vLLM",[215,770,228],{},[230,772,773,789,801,814,827,843,855],{},[212,774,775,777,780,783,786],{},[235,776,237],{},[235,778,779],{},"CLI + Daemon",[235,781,782],{},"GUI + Headless",[235,784,785],{},"Python Server",[235,787,788],{},"C++ 二进制",[212,790,791,793,795,797,799],{},[235,792,174],{},[235,794,350],{},[235,796,350],{},[235,798,327],{},[235,800,358],{},[212,802,803,805,807,810,812],{},[235,804,254],{},[235,806,592],{},[235,808,809],{},"✅ GUI",[235,811,263],{},[235,813,263],{},[212,815,816,819,821,823,825],{},[235,817,818],{},"OpenAI 兼容",[235,820,293],{},[235,822,290],{},[235,824,274],{},[235,826,274],{},[212,828,829,832,835,838,841],{},[235,830,831],{},"多用户吞吐",[235,833,834],{},"弱（~40 tok\u002Fs）",[235,836,837],{},"中（50–90）",[235,839,840],{},"强（800–12500）",[235,842,327],{},[212,844,845,847,849,851,853],{},[235,846,302],{},[235,848,307],{},[235,850,274],{},[235,852,280],{},[235,854,310],{},[212,856,857,859,861,864,867],{},[235,858,332],{},[235,860,338],{},[235,862,863],{},"闭源",[235,865,866],{},"Apache 2.0",[235,868,338],{},[22,870,361],{"id":361},[42,872,873,879,885,891,897],{},[45,874,875,878],{},[48,876,877],{},"num_ctx 一定要设","：默认 2K 太小，跑代码 \u002F 长文档要 16K+",[45,880,881,884],{},[48,882,883],{},"Modelfile 模板别漏 TEMPLATE","：错的 chat template 会让模型输出乱码 \u002F 不停",[45,886,887,890],{},[48,888,889],{},"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×",[45,892,893,896],{},[48,894,895],{},"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",[45,898,899,902],{},[48,900,901],{},"Mac 让它自动用 MLX","：升 0.19+；不要手动强制 GGUF + Metal",[22,904,397],{"id":396},[42,906,907,910,913,916,919,922,925],{},[45,908,909],{},"✅ 应用 \u002F IDE 接本地模型（Cursor \u002F Cline \u002F Continue）",[45,911,912],{},"✅ 个人 \u002F 评估 \u002F 脚本自动化",[45,914,915],{},"✅ Modelfile 自定义系统 prompt + 参数",[45,917,918],{},"✅ Mac M 系列 MLX 用户",[45,920,921],{},"❌ 多用户并发生产服务（用 vLLM）",[45,923,924],{},"❌ GUI 调参 \u002F 模型浏览（配 LM Studio \u002F Open WebUI）",[45,926,927],{},"❌ 极致单卡吞吐研究（直接 llama.cpp \u002F vLLM）",[22,929,423],{"id":423},[42,931,932,937,941,945],{},[45,933,934],{},[429,935,936],{"href":516},"LM Studio 评测",[45,938,939],{},[429,940,438],{"href":437},[45,942,943],{},[429,944,444],{"href":443},[45,946,947],{},[429,948,950],{"href":949},"\u002Fplaybook\u002Fonboarding\u002Frag-pipeline-build","RAG Pipeline 搭建 Playbook",[22,952,453],{"id":453},[176,954,955,962,969],{},[45,956,957,958],{},"Markaicode — Import GGUF Models into Ollama 2026（2026-05-15）",[429,959,960],{"href":960,"rel":961},"https:\u002F\u002Fmarkaicode.com\u002Fimport-gguf-models-ollama-guide",[463],[45,963,964,965],{},"ComputingForGeeks — Ollama Models Cheat Sheet 2026 ",[429,966,967],{"href":967,"rel":968},"https:\u002F\u002Fcomputingforgeeks.com\u002Follama-models-cheat-sheet",[463],[45,970,473,971],{},[429,972,476],{"href":476,"rel":973},[463],{"title":479,"searchDepth":480,"depth":480,"links":975},[976,977,978,979,980,981,982,983,984,985],{"id":24,"depth":483,"text":25},{"id":40,"depth":483,"text":40},{"id":103,"depth":483,"text":103},{"id":655,"depth":483,"text":656},{"id":174,"depth":483,"text":174},{"id":204,"depth":483,"text":204},{"id":361,"depth":483,"text":361},{"id":396,"depth":483,"text":397},{"id":423,"depth":483,"text":423},{"id":453,"depth":483,"text":453},"\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。",[989,992,995,998],{"q":990,"a":991},"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":993,"a":994},"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":996,"a":997},"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":999,"a":1000},"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。",[512],{},[519,520,521,1004],"docker",[1006],{"plan":1007,"price":524,"features":1008,"notes":1009},"开源版","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）",[534,535],{"power":537,"ux":537,"price":538,"cn_support":480,"stability":538},{"title":222,"description":987},[1015,1017,1019],{"name":1016,"url":960,"accessed":544},"Markaicode — Import GGUF 2026",{"name":1018,"url":967,"accessed":544},"ComputingForGeeks — Ollama Cheat Sheet 2026",{"name":548,"url":476,"accessed":544},"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",[493,1023,1024,1025,1026,553,554,557,1027],"daemon","cli","rest-api","modelfile","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",1784565442397]