[{"data":1,"prerenderedAt":119},["ShallowReactive",2],{"seo-verification":3,"marketplace-app-en-ollama":6},{"google":4,"bing":5},"EycwPY2XMyTkVzas3n1ygeNJFGAH513qrMjfDljzsMQ","",{"key":7,"data":8},"marketplace-app-en-ollama",{"slug":9,"slugs":10,"categorySlugs":11,"name":16,"description":17,"phase":18,"unavailableReason":19,"docsUrl":20,"logo":21,"github":22,"tagline":23,"longDescription":24,"features":25,"useCases":32,"steps":42,"faq":61,"specs":80,"compatibleOs":88,"relatedApps":90,"relatedPosts":113,"category":116},"ollama",{"fr":9,"en":9,"ar":9,"es":9},{"fr":12,"en":13,"ar":14,"es":15},"ia","artificial-intelligence","الذكاء-الاصطناعي","inteligencia-artificial","Ollama","Run open-source LLMs locally via a dead-simple API. Pull Llama 3, Mistral, Qwen or DeepSeek in one command — OpenAI-compatible, zero per-token cost.",1,null,"https:\u002F\u002Fservorbit.com\u002Fblog\u002Fhow-to-host-ollama-on-a-vps","https:\u002F\u002Fcdn.simpleicons.org\u002Follama","https:\u002F\u002Fgithub.com\u002Follama\u002Follama","Run open-source LLMs on your VPS — OpenAI-compatible API, zero per-token cost.","Ollama is an open-source LLM runtime that lets you pull and serve large language models (Llama 3, Mistral, Qwen, DeepSeek, Gemma) on your own infrastructure with a single command. It exposes an OpenAI-compatible REST API on port 11434, so any tool or library that works with OpenAI also works with your self-hosted instance — without changing a single line of code.\n\nDeployed on a ServOrbit VPS, Ollama becomes your private AI inference backend: unlimited requests at a fixed monthly cost, with all prompts and responses staying on your server. It pairs directly with Open WebUI for a chat interface, Flowise or n8n for automation, or any custom application that calls `\u002Fv1\u002Fchat\u002Fcompletions`.",[26,27,28,29,30,31],"OpenAI-compatible API — swap the base URL and your existing code works instantly","One-command model management: `ollama pull llama3.1:8b`, `ollama list`, `ollama rm`","Supports Llama 3, Mistral, Qwen, DeepSeek, Gemma, Phi and 100+ other models","CPU-only mode works out of the box; optional GPU passthrough for faster inference","Multi-model server: load several models concurrently, each on its own port or context","Persistent model storage in a Docker volume — models survive container restarts",[33,36,39],{"title":34,"body":35},"Private AI backend","Replace OpenAI API calls with your own Ollama endpoint. Your prompts never leave your VPS — ideal for processing contracts, internal documents, or any sensitive data.",{"title":37,"body":38},"Local dev & testing","Run a 7B or 13B model on a dedicated VPS to prototype AI features before wiring up a production LLM. Fixed cost, no surprise bills, reset with `ollama rm`.",{"title":40,"body":41},"Shared team AI stack","Pair Ollama with Open WebUI to give your whole team a shared ChatGPT-like interface powered by open-source models — on infrastructure you control.",[43,46,49,52,55,58],{"title":44,"body":45},"Create the VPS","Order a ServOrbit VPS with at least 8 GB of RAM for a 7 to 8 billion parameter model quantised to Q4 on CPU, 16 GB for larger models. The system installed is Ubuntu 24.04. For production-grade response times, a server fitted with a GPU changes the experience markedly.",{"title":47,"body":48},"Deploy Ollama","Installing from your client area creates the `ollama\u002Follama` container, mounts the `ollama` volume on `\u002Froot\u002F.ollama` so your models survive reboots, and publishes the service on the loopback interface, at `127.0.0.1:11434`. Nothing is exposed to the internet at this stage: that is deliberate, since Ollama has no authentication of any kind.",{"title":50,"body":51},"Connect for the first time","Ollama has no sign-in screen: it is an HTTP API paired with a command line. Over SSH on the VPS, run `docker exec -it ollama ollama pull llama3` then `docker exec -it ollama ollama run llama3`. Then plug in an interface — Open WebUI for instance — rather than exposing the API as it stands.",{"title":53,"body":54},"Pull your first model","Fetch a model with `docker exec -it ollama ollama pull llama3.1:8b`, check that it is registered with `docker exec -it ollama ollama list`, then test from the VPS: `curl http:\u002F\u002F127.0.0.1:11434\u002Fapi\u002Fgenerate -d '{\"model\":\"llama3.1:8b\",\"prompt\":\"Hello\"}'`.",{"title":56,"body":57},"Account for the lack of authentication","Ollama asks for no credentials and knows nothing of API keys: anyone who reaches the service can query your models and pull new ones. That is why ServOrbit attaches no domain to it — a vhost's encryption would protect the transport, it would identify nobody. Two usages hold up: work through an SSH tunnel, or put an authenticating gateway in front of Ollama — LiteLLM for instance — and publish only that.",{"title":59,"body":60},"Connect your tools","In Open WebUI, open Settings then Connections and enter `http:\u002F\u002Flocalhost:11434`. From an OpenAI SDK running on the VPS itself, set `base_url='http:\u002F\u002F127.0.0.1:11434\u002Fv1'` and `api_key='ollama'`. From any other machine there is no public entry point: open an SSH tunnel with `ssh -L 11434:127.0.0.1:11434 root@\u003Cyour-vps-ip>` then point at `http:\u002F\u002Flocalhost:11434\u002Fv1`. The port may be reassigned at install time: use the one shown on the app's card in your client area, 11434 being only the catalogue value.",[62,65,68,71,74,77],{"q":63,"a":64},"What is Ollama?","Ollama is an open-source runtime that downloads and serves large language models (LLMs) via a local REST API. Think of it as a local inference server: you pull a model once, and any application can call it just like it would call the OpenAI API.",{"q":66,"a":67},"How much RAM does Ollama need?","A 7B parameter model in Q4 quantization requires roughly 5–6 GB of RAM. Plan for 8 GB minimum to run a 7B\u002F8B model comfortably. For 13B models, 16 GB; for 70B models (slow on CPU), 64 GB or a GPU is recommended.",{"q":69,"a":70},"Is Ollama compatible with the OpenAI SDK?","Yes. Ollama exposes an OpenAI-compatible endpoint at `\u002Fv1\u002Fchat\u002Fcompletions`, `\u002Fv1\u002Fcompletions`, and `\u002Fv1\u002Fembeddings`. Change `base_url` to your Ollama host and set `api_key` to any non-empty string — existing code requires no other changes.",{"q":72,"a":73},"Does Ollama work without a GPU?","Yes. CPU-only inference works out of the box. Smaller quantized models (Q4_K_M) run at usable speeds on modern CPUs for dev or low-volume use. For production latency on 7B+ models, a GPU accelerates inference 10–30×.",{"q":75,"a":76},"How do I add more models?","Run `docker exec -it ollama ollama pull \u003Cmodel-name>` from your VPS. Browse available models at ollama.com\u002Flibrary. Popular choices: `llama3.1:8b`, `mistral:7b`, `qwen2.5:7b`, `deepseek-r1:7b`, `gemma2:9b`.",{"q":78,"a":79},"Can I pair Ollama with Open WebUI?","Yes — they are designed to work together. Open WebUI auto-detects a local Ollama instance and lists all pulled models in its model selector. Both can run on the same VPS; Open WebUI adds a full chat UI, user management, and RAG on top of Ollama's inference engine.",{"ram":81,"cpu":82,"stack":83,"port":87},"8 GB minimum (16 GB recommended)","2 vCPU",[84,85,86],"Docker","Go","llama.cpp","11434",[89],"ubuntu-24.04",[91,99,108],{"name":92,"slug":93,"categorySlug":13,"categoryName":94,"categoryColor":95,"logo":96,"tagline":97,"description":98},"Open WebUI","open-webui","Artificial Intelligence","text-purple-400 bg-purple-500\u002F10","https:\u002F\u002Fcdn.jsdelivr.net\u002Fgh\u002Fselfhst\u002Ficons\u002Fsvg\u002Fopen-webui.svg","Web interface for your LLMs — Ollama, OpenAI, Mistral — hosted on your own server.","Web interface to interact with your local or remote LLMs. Your data stays on your infrastructure — no third party involved.",{"name":100,"slug":101,"categorySlug":102,"categoryName":103,"categoryColor":104,"logo":105,"tagline":106,"description":107},"Flowise","flowise","automation-workflows","Automation & Workflows","text-brand-action bg-brand-action\u002F10","https:\u002F\u002Fcdn.jsdelivr.net\u002Fgh\u002Fselfhst\u002Ficons\u002Fsvg\u002Fflowise.svg","Build LLM pipelines and AI chatbots with drag-and-drop — visual LangChain.","Build LLM automation pipelines with drag-and-drop. Visual LangChain chains, connected to your existing tools.",{"name":109,"slug":109,"categorySlug":102,"categoryName":103,"categoryColor":104,"logo":110,"tagline":111,"description":112},"n8n","https:\u002F\u002Fcdn.simpleicons.org\u002Fn8n","Automate your business processes with 400+ integrations — 100% self-hosted on your VPS.","Automate your workflows across applications without code. Over 400 integrations, 100% self-hosted on your VPS.",[114,115],"heberger-ollama-vps","ollama-vs-localai",{"key":12,"slug":13,"name":94,"objective":117,"icon":118,"color":95},"Build, host and run AI solutions.","ai",1793820390363]