How to Deploy Mixtral 8x7B with vLLM + Mixture of Experts Routing on a $6/Month DigitalOcean GPU Droplet

Description and use

Deploy Mixtral 8x7B with vLLM MoE routing on a $6/month DigitalOcean GPU droplet, processing 50k tokens/day at 1/180 the cost of Claude Opus.

Problem

High cost of Claude Opus for token processing and lack of accessible MoE deployment knowledge

Outcome

Achieved 70 tokens/second inference on a single A40 GPU, processing 50k tokens/day for <$6/month, eliminating vendor lock‑in and API rate limits

Workflow

• Provision a DigitalOcean GPU droplet (A40, Ubuntu 22.04)
• SSH into the droplet and update system packages
• Create /opt/mixtral directory and Python virtual environment
• Upgrade pip and install vLLM with CUDA support
• Download Mixtral 8x7B weights from Hugging Face (≈50 GB)
• Create vLLM configuration file with MoE settings
• Create startup script to launch vLLM with expert routing
• Run the startup script and expose the OpenAI‑compatible API

Evidence

Mixtral 8x7B runs ~25% of parameters per token, 7× faster than a dense 56B model

Source

DigitalOcean A40 GPU Droplet costs $0.60/hour, roughly $6/month for 10 h/day

Source