Deploy Llama 3.3 70B with vLLM on DigitalOcean GPU Droplet

说明与用途

在 DigitalOcean 的 2‑GPU Droplet 上使用 vLLM 部署 Llama 3.3 70B，可在每个令牌成本仅为 $0.00000001 的情况下处理 50+ 并发请求，远低于商业 API。

问题

High cost of commercial LLM APIs for production workloads.

结果

The deployment delivers 500K tokens/day with 2× throughput of a single‑GPU setup, costing ~$300/month versus ~$8,000/month for Claude Opus.

工作流

• Provision a 2x L40 GPU droplet via doctl or dashboard
• SSH into droplet and install Ubuntu dependencies
• Create Python 3.11 virtual environment and install vLLM 0.6.0+ with CUDA 12.1
• Download Llama 2 70B weights from Hugging Face using HF_TOKEN
• Create vLLM config YAML with tensor_parallel_size=2, dtype=float16, gpu_memory_utilization=0.85
• Set up systemd service to run vLLM OpenAI‑compatible API on startup
• Verify service is running and test inference via OpenAI API endpoint

证据

Self‑hosted Llama 3.3 on DigitalOcean costs you $0.00000001 per token after you account for the fixed infrastructure cost spread across actual usage.

来源

vLLM compiles CUDA kernels during installation.

来源