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

Описание и применение

Развертывание Llama 2 на DigitalOcean за $5/мес: 4‑битная квантизация, FastAPI, Docker, 10× экономия по сравнению с API

High cost of commercial LLM APIs for production inference

Running 500+ inferences/day for $5/month, equivalent to $400+ API cost, 10x cheaper, 90% quality

Create DigitalOcean Droplet (Ubuntu 22.04, 1GB RAM, 1 vCPU)
SSH into droplet and update packages
Install dependencies (python3-pip, python3-venv, git, curl, build-essential, libopenblas-dev)
Create application directory /opt/llama2-inference
Set up Python virtual environment and install llama-cpp-python, fastapi, uvicorn, pydantic, python-dotenv, aiofiles
Download 4‑bit quantized Mistral‑7B‑Instruct GGUF model (~3.8 GB) to /opt/llama2-inference/models
Build FastAPI inference server (app.py) with llama_cpp.Llama, concurrent request handling, logging
Containerize the application with Docker (Dockerfile)
Deploy container on the droplet, expose port 8000
Test API with curl
Monitor performance and adjust N_THREADS if needed

This setup has been running 24/7 for three months without touching it.

$5/month Droplet (1GB RAM, 1 vCPU, 25GB SSD) — yes, this actually works

Quantize it to 4-bit precision, and run inference that's 10x cheaper while keeping 90% of the quality.