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

说明与用途

在 DigitalOcean 上以 $5/月 部署 Llama 2：4‑bit 量化、FastAPI、Docker，成本比 API 低 10 倍

问题

High cost of commercial LLM APIs for production inference

结果

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

工作流

• 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

证据

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

来源

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

来源

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

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