Hostinger Server Setup
// Create VPS · Install Docker · Configure network & firewall
| Resource | Hostinger KVM 8 | Why |
|---|---|---|
| vCPU | 8 cores AMD EPYC | Parallel container workloads |
| RAM | 32 GB | Sim.ai alone needs 12 GB |
| Storage | 400 GB NVMe | LLM models + DB growth |
| OS | Ubuntu 24.04 LTS (Noble Numbat) | cgroup v2 · best Docker support |
1
Create Your Hostinger VPS
▲
Go to: hostinger.com/vps-hosting → Choose Plan → KVM 8
During setup select Ubuntu 24.04 LTS as the OS. After provisioning, note your server's public IP from the hPanel dashboard.
Hostinger provisions in under 60 seconds. You'll have hPanel access immediately with a built-in Docker Manager GUI included for free.
2
SSH Into Your Server from Mac
▼
bash · your Mac terminal
ssh root@YOUR_SERVER_IP # Verify Ubuntu version lsb_release -a # Expected: Ubuntu 24.04.x LTS # Confirm cgroup v2 (Ubuntu 24.04 default) stat -fc %T /sys/fs/cgroup # Expected output: cgroup2fs ✓
Replace before running:
| Placeholder | What to put | Where to find it |
|---|---|---|
| YOUR_SERVER_IP | Your Hostinger server's public IP address | hPanel → VPS section → click your server → IP shown at top of dashboard (e.g. 82.115.x.x) |
3
Update System & Install Essentials
▼
bash
apt update && apt upgrade -y apt install -y curl wget git nano ufw fail2ban htop
4
Install Docker & Docker Compose
▼
bash
# Official Docker install — works perfectly on Ubuntu 24.04 curl -fsSL https://get.docker.com -o get-docker.sh sh get-docker.sh apt install -y docker-compose-plugin # Verify docker --version docker compose version
Ubuntu 24.04 ships cgroup v2 by default — Docker detects it automatically. No manual configuration needed.
5
Create Docker Network & Folder Structure
▼
One shared Docker bridge network lets all containers talk to each other by name — completely internal to the server, no extra Hostinger networking needed. Run each command separately:
bash · command 1 of 3
docker network create ai-stack
bash · command 2 of 3
mkdir -p ~/ai-stack/{ollama,claude-code,openclaw,sim}
bash · command 3 of 3
echo "✅ Network and folders ready"
6
Configure Firewall (UFW)
▼
Run each command separately:
bash · command 1 of 5
ufw allow OpenSSH
bash · command 2 of 5
ufw allow 8080
bash · command 3 of 5
ufw allow 3000
bash · command 4 of 5
ufw allow 11434
bash · command 5 of 5 — enable & verify
ufw --force enable && ufw status
Restrict Ollama to your IP only in production:
ufw allow from YOUR_IP to any port 11434Ollama
// Local LLM runner · CPU mode · Port 11434 · ollama/ollama
Docs: docs.ollama.com/docker · Image: hub.docker.com/r/ollama/ollama
Running in CPU mode on your Hostinger KVM 8. Inference is slower than GPU but fully functional — small models (3B–8B) respond in 5–20 seconds which is fine for personal use and curriculum development.
1
Run Ollama Container (CPU mode)
▲
This is a multi-line command. Create a script file, paste the content, make it executable, then run it:
bash · step 1 — create the script file
touch ~/ai-stack/ollama/run-ollama.sh
bash · step 2 — open in nano editor
nano ~/ai-stack/ollama/run-ollama.sh
bash · step 3 — paste this into nano, then press Ctrl+O to save, Ctrl+X to exit
#!/bin/bash docker run -d \ --name ollama \ --network ai-stack \ --restart unless-stopped \ -v ollama-data:/root/.ollama \ -p 11434:11434 \ ollama/ollama
bash · step 4 — make executable and run
chmod +x ~/ai-stack/ollama/run-ollama.sh
bash · step 5 — execute the script
bash ~/ai-stack/ollama/run-ollama.sh
2
Pull Models & Test
▼
bash · command 1 — pull a model
docker exec -it ollama ollama pull llama3.2
bash · command 2 — test the API
curl http://localhost:11434/api/generate -d '{
"model": "llama3.2",
"prompt": "Hello World!"
}'
bash · command 3 — list downloaded models
docker exec -it ollama ollama list
Other containers reach Ollama at
http://ollama:11434 via the ai-stack network.3
Recommended Models by VRAM
▼
| Model | VRAM | Best for |
|---|---|---|
| llama3.2:3b | ~2 GB | Fast responses, low resource |
| llama3.2:8b | ~5 GB | Good quality general use |
| mistral:7b | ~5 GB | Coding + reasoning |
| deepseek-r1:8b | ~6 GB | Strong reasoning tasks |
| gemma2:9b | ~7 GB | Curriculum & education content |
