How to Run Ollama on a VPS: Self-Host Llama 3 in 2026

Running a local LLM on your own VPS gives you a private, cost-predictable AI API. No per-token pricing, no data leaving your infrastructure. The catch: you need enough RAM, and the setup requires a few extra steps compared to a cloud API.

This guide covers the full setup: installing Ollama, pulling a model, keeping it running with systemd, and exposing a secure API through Nginx.

For VPS recommendations and RAM requirements by model size, see Best VPS for Ollama in 2026.

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Requirements

What	Minimum	Recommended
RAM	8GB	16GB
Storage	20GB free	40GB+
CPU	2 vCPU	4+ vCPU
OS	Ubuntu 22.04	Ubuntu 22.04

Good options: Hetzner CX32 (€8.30/mo, 8GB RAM — see the full CX32 pricing breakdown) or Contabo Cloud VPS S ($7/mo, 8GB RAM). A 4GB VPS will fail to load most useful models.

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Step 1: Install Ollama

curl -fsSL https://ollama.com/install.sh | sh

This installs the ollama binary and creates a systemd service automatically. Verify:

ollama --version
systemctl status ollama

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Step 2: Pull a Model

# Llama 3.1 8B — best balance of quality and speed on CPU
ollama pull llama3.1

# Mistral 7B — fast, good for code and structured output
ollama pull mistral

# Phi-3 Mini — runs on 4GB RAM, fast but less capable
ollama pull phi3

Model download sizes:

• llama3.1 (8B) — ~4.7GB
• mistral (7B) — ~4.1GB
• phi3 (3.8B) — ~2.3GB

Models are stored in ~/.ollama/models. Make sure your VPS has enough disk space before pulling.

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Step 3: Test Locally

# Chat in terminal
ollama run llama3.1

# Or call the REST API directly
curl http://localhost:11434/api/generate -d '{
  "model": "llama3.1",
  "prompt": "What is the capital of France?",
  "stream": false
}'

The API is OpenAI-compatible for /api/chat calls, making it easy to swap with existing integrations.

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Step 4: Keep Ollama Running (systemd)

Ollama’s installer creates a systemd service automatically, but verify it’s set to start on reboot:

sudo systemctl enable ollama
sudo systemctl start ollama
sudo systemctl status ollama

If the service isn’t created (older installs), create it manually:

sudo nano /etc/systemd/system/ollama.service

[Unit]
Description=Ollama Service
After=network-online.target

[Service]
ExecStart=/usr/local/bin/ollama serve
User=ollama
Group=ollama
Restart=always
RestartSec=3
Environment="OLLAMA_HOST=127.0.0.1:11434"

[Install]
WantedBy=default.target

sudo systemctl daemon-reload
sudo systemctl enable --now ollama

The OLLAMA_HOST=127.0.0.1 binding is important — it keeps Ollama on localhost only. Nginx handles external access in the next step.

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Step 5: Nginx Reverse Proxy with API Key Auth

Never expose port 11434 directly to the internet. Use Nginx as a gatekeeper.

sudo apt install nginx -y
sudo nano /etc/nginx/sites-available/ollama

server {
    listen 443 ssl;
    server_name your-domain.com;

    ssl_certificate /etc/letsencrypt/live/your-domain.com/fullchain.pem;
    ssl_certificate_key /etc/letsencrypt/live/your-domain.com/privkey.pem;

    # Simple API key check via header
    if ($http_x_api_key != "your-secret-key-here") {
        return 401;
    }

    location / {
        proxy_pass http://127.0.0.1:11434;
        proxy_set_header Host $host;
        proxy_read_timeout 300s;   # LLM responses can be slow
        proxy_send_timeout 300s;
    }
}

sudo ln -s /etc/nginx/sites-available/ollama /etc/nginx/sites-enabled/
sudo certbot --nginx -d your-domain.com
sudo nginx -t && sudo systemctl reload nginx

Now call your API with the key:

curl https://your-domain.com/api/generate \
  -H "X-API-Key: your-secret-key-here" \
  -d '{"model": "llama3.1", "prompt": "Hello", "stream": false}'

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Step 6: Use with n8n or Custom Scripts

Ollama’s API is compatible with the OpenAI SDK. In Node.js:

import OpenAI from 'openai';

const client = new OpenAI({
  baseURL: 'https://your-domain.com/v1',
  apiKey: 'your-secret-key-here',
});

const response = await client.chat.completions.create({
  model: 'llama3.1',
  messages: [{ role: 'user', content: 'Explain PM2 cluster mode' }],
});

For n8n self-hosted workflows, use the Ollama node or the HTTP Request node pointing to your Nginx endpoint.

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Performance Tips

• Preload models at startup to avoid cold-start latency:

  # Add to /etc/systemd/system/ollama.service [Service] section:
  ExecStartPost=/bin/sh -c 'sleep 5 && ollama run llama3.1 ""'

• Limit context length for faster responses on CPU:

  ollama run llama3.1 --num-ctx 2048

• Monitor RAM usage — if the VPS starts swapping heavily, the model is too large. Switch to phi3 or upgrade to 16GB RAM.

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What’s Next

Self-hosted Ollama pairs well with n8n for AI automation workflows. See our n8n on VPS setup guide for the full n8n + Ollama stack on a single VPS.

For VPS provider comparisons at the 8GB RAM tier (what Ollama needs), see Best VPS for Ollama.