[{"data":1,"prerenderedAt":117},["ShallowReactive",2],{"seo-verification":3,"blog-heberger-privategpt-vps-en":6},{"google":4,"bing":5},"EycwPY2XMyTkVzas3n1ygeNJFGAH513qrMjfDljzsMQ","",{"id":7,"slug":8,"title":9,"excerpt":10,"readTime":11,"views":12,"isPinned":13,"publishedAt":14,"category":15,"categories":21,"featuredImage":23,"bgImage":24,"posterImage":25,"relatedSolution":26,"intro":28,"sections":29,"ctaTitle":72,"ctaBody":73,"ctaButton":74,"ctaUrl":75,"relatedPosts":76},16,"heberger-privategpt-vps","How to Host PrivateGPT on a VPS","Deploy PrivateGPT on a VPS to query your confidential documents with a 100% offline RAG AI, with no data leak.",8,792,false,"2026-06-04T00:00:00+00:00",{"id":16,"name":17,"slug":18,"color":19,"icon":20},1,"Artificial Intelligence","intelligence-artificielle","bg-purple-500\u002F10 text-purple-400","ia",[22],{"id":16,"name":17,"slug":18,"color":19,"icon":20},null,"\u002Fblog\u002Fcovers\u002Fbg.svg","\u002Fblog\u002Fcovers\u002Fheberger-privategpt-vps-poster.svg",{"categorySlug":20,"appSlug":27},"privategpt","PrivateGPT lets you ask questions of your own documents (PDF, Word, emails) via an AI, without any data leaving your infrastructure. It is a RAG solution designed for absolute confidentiality. Hosting it on a VPS gives you a private documentary assistant accessible remotely.",[30,34,44,47,69],{"type":31,"title":32,"body":33},"h2","Why self-host PrivateGPT on a VPS","PrivateGPT is designed around RAG (Retrieval-Augmented Generation): it ingests your documents, splits them into fragments, computes embeddings stored in a vector database (Qdrant by default), then answers questions relying solely on this corpus. Its central argument is confidentiality: in local mode, the LLM and the embeddings run on the machine, no request goes out. On a dedicated VPS, you get a secure enterprise documentary assistant, isolated from everyone's workstation, with a centralized knowledge base that the whole team queries, without ever exposing contracts, HR files or legal documents to a cloud service.",{"type":35,"title":36,"items":37},"ul","Concrete benefits of self-hosting",[38,39,40,41,42,43],"Total confidentiality: documents and embeddings never leave the VPS","Centralized Qdrant vector database, queryable by the whole team in one place","No API cost if you use a local LLM via Ollama or llama.cpp","Ingestion of PDF, DOCX, emails and web pages into a single private corpus","OpenAI-compatible API to connect PrivateGPT into your internal tools","Easier compliance for sensitive data (GDPR, professional secrecy)",{"type":31,"title":45,"body":46},"Hardware and software requirements","PrivateGPT in fully local mode is more demanding than other tools because it runs both the embedding model and the generation LLM. Count on at least 8 GB of RAM (16 GB recommended) and 4 vCPU for a quantized 7B model, plus 20 GB of disk for the models, the Qdrant database and the ingested documents. If you offload the LLM to an Ollama on the same VPS, size the RAM accordingly. On the software side: Docker and Docker Compose, Python 3.11 if you install outside a container, and a domain pointing to the VPS to expose the Gradio interface and the API.",{"type":48,"title":49,"steps":50},"steps","Deployment of PrivateGPT in local mode",[51,54,57,60,63,66],{"title":52,"body":53},"Prepare the VPS","Install Docker and clone the repository: `git clone https:\u002F\u002Fgithub.com\u002Fzylon-ai\u002Fprivate-gpt && cd private-gpt`. Create a persistent volume for the ingested data and the Qdrant database so as to survive redeployments.",{"title":55,"body":56},"Choose the execution profile","PrivateGPT works by profiles. For a 100% local deployment with Ollama, start Ollama then pull the models: `ollama pull mistral` for generation and `ollama pull nomic-embed-text` for the embeddings.",{"title":58,"body":59},"Configure settings and launch","Enable the ollama profile in `settings.yaml` then start with `PGPT_PROFILES=ollama make run` or via the provided compose. Check in the logs that the connection to Ollama and to Qdrant is established.",{"title":61,"body":62},"Ingest your documents","Drop your files in the ingestion folder and run `make ingest \u002Fchemin\u002Fvers\u002Fdocs`. PrivateGPT splits, vectorizes and indexes the corpus in Qdrant. Follow the progress in the logs until the indexing is complete.",{"title":64,"body":65},"Set up the reverse proxy and SSL","Expose the Gradio interface (port 8001) behind Caddy or Nginx with an automatic Let's Encrypt certificate. Add basic HTTP authentication, because the UI does not offer it natively.",{"title":67,"body":68},"Lock down access","Close port 8001 at the firewall to leave only the reverse proxy reachable. Your users access `https:\u002F\u002Fdocs.yourdomain.com` and query the corpus in natural language.",{"type":70,"body":71},"tip","The default profile downloads heavy models. For a CPU VPS, switch the LLM and the embeddings to Ollama (`mistral` + `nomic-embed-text`) rather than the llama.cpp bindings compiled locally: you avoid the long compilations, pool the memory with other containers and gain stability during restarts.","Build your private documentary assistant","The ServOrbit Cloud VPS provides the RAM and SSD storage needed to run PrivateGPT and its Qdrant vector database locally, with preconfigured Docker for a fast RAG deployment.","Deploy on a Cloud VPS","\u002Fvps-cloud",[77,90,104],{"id":16,"slug":78,"title":79,"excerpt":80,"readTime":81,"views":82,"isPinned":13,"publishedAt":83,"category":84,"categories":85,"featuredImage":23,"bgImage":24,"posterImage":87,"relatedSolution":88},"heberger-open-webui","How to Host Open WebUI on a VPS","A complete guide to deploying Open WebUI on your own VPS. Keep control of your data, avoid cloud API costs, and access your LLMs from a clean web interface.",6,3200,"2026-06-01T00:00:00+00:00",{"id":16,"name":17,"slug":18,"color":19,"icon":20},[86],{"id":16,"name":17,"slug":18,"color":19,"icon":20},"\u002Fblog\u002Fcovers\u002Fheberger-open-webui-poster.svg",{"categorySlug":20,"appSlug":89},"open-webui",{"id":91,"slug":92,"title":93,"excerpt":94,"readTime":95,"views":96,"isPinned":13,"publishedAt":97,"category":98,"categories":99,"featuredImage":23,"bgImage":24,"posterImage":101,"relatedSolution":102},2,"deployer-dify-vps","Deploying Dify on a VPS: AI Workflows Under Control","Install Dify on a ServOrbit VPS to build agents, chatflows and AI apps without losing control of your data.",7,2480,"2026-02-11T00:00:00+00:00",{"id":16,"name":17,"slug":18,"color":19,"icon":20},[100],{"id":16,"name":17,"slug":18,"color":19,"icon":20},"\u002Fblog\u002Fcovers\u002Fdeployer-dify-vps-poster.svg",{"categorySlug":20,"appSlug":103},"dify",{"id":81,"slug":105,"title":106,"excerpt":107,"readTime":108,"views":109,"isPinned":13,"publishedAt":110,"category":111,"categories":112,"featuredImage":23,"bgImage":24,"posterImage":114,"relatedSolution":115},"openclaw-vps","Deploying OpenClaw on a VPS","A guide to deploying OpenClaw on your own VPS. Keep control of your AI agents and your data within your own infrastructure.",5,422,"2026-06-11T00:00:00+00:00",{"id":16,"name":17,"slug":18,"color":19,"icon":20},[113],{"id":16,"name":17,"slug":18,"color":19,"icon":20},"\u002Fblog\u002Fcovers\u002Fopenclaw-vps-poster.svg",{"categorySlug":20,"appSlug":116},"openclaw",1785628446154]