How to Deploy Llama 2 on DigitalOcean for $5/Month: Complete Self-Hosting Guide

Description and use

The guide demonstrates deploying Llama 2 7B on a $5/month DigitalOcean droplet using INT4 quantization and Docker, enabling unlimited inference, zero latency, and full data control.

Problem

High API costs for large-scale LLM inference and lack of control over latency and data privacy.

Outcome

The guide demonstrates that a $5/month DigitalOcean droplet can host Llama 2 7B with INT4 quantization, providing unlimited inference, deterministic outputs, and no vendor lock‑in.

Workflow

• Create a DigitalOcean droplet (Ubuntu 22.04, 1 GB RAM, 25 GB SSD, 1 vCPU).
• Secure the server: update, install tools, configure UFW, create a non‑root deployer user.
• Install Docker and Docker Compose on the droplet.
• Download the GGML‑quantized Llama 2 7B (INT4) model (~4 GB) to a local machine.
• Transfer the model file to the droplet via scp.
• Create a project directory with Dockerfile and docker‑compose.yml that installs Ollama and exposes port 11434.
• Build the Docker image with docker‑compose build and start the service with docker‑compose up -d.
• Verify the API is reachable via curl http://localhost:11434/api/tags and test inference.
• Use the REST API (or a client) to send prompts and receive responses.
• Monitor logs and restart policy ensures high availability.

Evidence

I discovered something better last year: self-hosting Llama 2 on a $5/month DigitalOcean Droplet.

Source

Quantization reduces model size by representing weights with fewer bits: INT4 reduces Llama 2 7B to ~4 GB.

Source

Create the Droplet – Region: US East, Image: Ubuntu 22.04 LTS, Size: $5/month (1GB RAM).

Source