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Run your own LLM on a cloud server with Ollama (CPU)

Ollama downloads and runs open-source language models with one command. Without a GPU, small and medium models run comfortably on CPU.

Time: 15 min Level: Intermediate OS: Ubuntu 24.04 · x86 / ARM64

Requirements

  • A REGTR cloud server: at least 16 GB RAM for 7–8B models, 32 GB for comfortable use (dedicated CPU recommended)
  • Ubuntu 24.04, x86 or ARM64

Steps

  1. 1Install Ollama

    The script installs Ollama as a systemd service; by default it listens only on 127.0.0.1:11434.

    curl -fsSL https://ollama.com/install.sh | sh
    systemctl status ollama --no-pager
  2. 2Pull a model and try it

    Pick a model that fits your RAM. Roughly (4-bit): 3B ≈ 3 GB, 8B ≈ 6 GB, 14B ≈ 10 GB, 32B ≈ 20 GB of memory.

    ollama pull llama3.1:8b
    ollama run llama3.1:8b "Bulut sunucu nedir? Kısaca anlat."
    # Daha küçük / smaller: ollama pull llama3.2:3b
  3. 3Use the API

    Your apps on the same server can call the model over the HTTP API.

    curl http://127.0.0.1:11434/api/generate -d '{
      "model": "llama3.1:8b",
      "prompt": "Merhaba!",
      "stream": false
    }'
  4. 4Secure remote access

    Don't expose the API to the internet. Connect from your computer through an SSH tunnel; for a web UI, run Open WebUI bound to a local address.

    # Kendi bilgisayarından / from your computer:
    ssh -L 11434:127.0.0.1:11434 deploy@SUNUCU_IP
    
    # İsteğe bağlı web arayüzü (sunucuda, yalnız yerel adres) / optional web UI bound to localhost:
    docker run -d -p 127.0.0.1:3000:8080 --add-host=host.docker.internal:host-gateway \
      -v open-webui:/app/backend/data --name open-webui --restart always \
      ghcr.io/open-webui/open-webui:main

Tips

  • Setting OLLAMA_HOST=0.0.0.0 and opening the port lets anyone use your model; Ollama has no built-in authentication.
  • CPU speed depends on core count and memory bandwidth; dedicated vCPU gives more consistent response times than shared vCPU.
  • REGTR cloud servers have no GPU; they are not suitable for training large models or running very large ones like 70B.

FAQ

Which plan?

For 7–8B models, 8 vCPU / 32 GB dedicated CPU (Dedicated 8) is a good balance. For 14B and above or several concurrent users, 16 vCPU / 64 GB.

Is it suitable for RAG and embeddings?

Yes. Embedding models like nomic-embed-text are fast on CPU and can run on the same server as Qdrant or pgvector.

Updated: 2026-10-23

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