How to Deploy Llama 2 on DigitalOcean for $5/Month

说明与用途

在 DigitalOcean 上以每月 $5 部署 Llama 2 7B，使用 4‑bit 量化和 Docker 化的 Flask API，将 API 成本降至 <$5/月。

问题

High cost of commercial AI APIs for high‑volume token workloads.

结果

Running Llama 2 7B quantized on a $5/month Droplet reduces inference cost from ~$90/month to <$5/month, while providing a production‑grade REST API.

工作流

• Create a $5/month DigitalOcean Droplet (Ubuntu 22.04, 1 GB RAM, 25 GB SSD).
• SSH into the Droplet and update packages.
• Install Docker and Docker‑Compose.
• Create a non‑root user and add to docker group.
• Create a project directory and write a Dockerfile that builds llama.cpp, installs Flask, downloads the 4‑bit quantized model, and exposes port 5000.
• Write a simple Flask API server (api_server.py) that starts llama.cpp and handles inference requests.
• Build the Docker image and run the container.
• Verify the API is reachable (e.g., curl http://localhost:5000/health).
• Optionally scale by increasing Droplet size or adding replicas.
• Monitor usage and cost via DigitalOcean dashboard.
• Use the REST API in your applications for content generation, support automation, or semantic search.

证据

Run it yourself? That's $5/month, plus electricity you're paying for anyway.

来源

Llama 2 7B quantized fits comfortably on $5/month hardware.

来源