Deploy Llama 3.3 70B with vLLM on DigitalOcean GPU Droplet

Description and use

Deploying Llama 3.3 70B on a 2‑GPU DigitalOcean droplet with vLLM handles 50+ concurrent requests at $0.00000001 per token, far cheaper than commercial APIs.

Problem

High cost of commercial LLM APIs for production workloads.

Outcome

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

Workflow

• 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

Evidence

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.

Source

vLLM compiles CUDA kernels during installation.

Source