Deploy vLLM with OpenAI API on GPU Cloud (2026 Guide)

Sunny Smith, Co-Founder, Massed Compute

June 2026

Deploy vLLM with an OpenAI-compatible API on GPU infrastructure. Complete provisioning, configuration, and verification workflow for production-ready inference endpoints.

vLLM

OpenAI API

GPU

Inference

Ubuntu

Machine Learning

⚡ AI Agent Shortcut

This entire deployment exists as a tested, machine-readable recipe in the Massed Compute MCP. Skip the manual steps and deploy with natural language commands through compatible AI agents.

vLLM provides high-performance inference for large language models with an OpenAI-compatible API. This guide walks through launching a GPU VM, installing vLLM with proper dependencies, configuring a systemd service, and running verification tests to ensure your inference endpoint is production-ready.

You’ll get a fully functional /v1/chat/completions endpoint that accepts OpenAI-format requests while leveraging GPU acceleration for fast inference. The setup includes automatic service management, error handling, and comprehensive testing.

Stack Overview

Component	Version	Purpose
Ubuntu Server	24.04 LTS	Base OS with NVIDIA drivers
vLLM	0.6.3.post1	High-performance inference engine
PyTorch	2.4.0	CUDA 12.1 backend
Transformers	4.45.2	Model loading and tokenization
LM Format Enforcer	Latest	Guided decoding support

Requirements

Resource	Minimum	Recommended
GPU Memory	24GB	48GB+ for larger models
System RAM	32GB	72GB+ for model caching
vCPU	8 cores	14+ cores for throughput
Storage	256GB	512GB+ for multiple models
Network	1Gbps	High bandwidth for model downloads

Massed Compute VM Pricing

Current GPU VM options that meet the minimum requirements for vLLM deployment:

Pricing fetched from the Massed Compute inventory API on June 11, 2026.
SKU	Description	vCPU	RAM	Storage	Price	Capacity
`gpu_1x_A30`	1x A30 (24GB)	16	48 GiB	256 GB	$0.35/hr	0
`gpu_1x_a5000`	1x RTX A5000 (24GB)	10	32 GiB	256 GB	$0.44/hr	0
`gpu_2x_A30`	2x A30 (24GB)	30	96 GiB	512 GB	$0.70/hr	0
`gpu_1x_l40_spot`	1x L40 (48GB) [Spot]	14	72 GiB	625 GB	$0.78/hr	15
`gpu_1x_6000_ada`	1x RTX 6000 ADA (48GB)	12	72 GiB	350 GB	$0.79/hr	14
`gpu_1x_l40`	1x L40 (48GB)	14	72 GiB	625 GB	$0.86/hr	15

Note: Spot instances offer significant savings but may be interrupted. Use on-demand instances for production workloads requiring high availability.

Step-by-Step Deployment

Launch GPU VM

Create a new VM with GPU support and NVIDIA drivers pre-installed:

# Launch via Massed Compute dashboard or API
# Product: gpu_1x_l40 (or preferred GPU SKU)
# Image: 184 (Ubuntu Server 24.04 w/ Drivers)
# SSH Key: attach your public key
# Instance name: vllm-openai-api

Wait for the VM to reach running status and note the SSH connection details.

Verify GPU Access

Confirm NVIDIA drivers and GPU detection:

ssh ubuntu@YOUR_VM_IP 'nvidia-smi --query-gpu=name,memory.total,memory.free --format=csv,noheader'

You should see output showing your GPU name and memory specifications.

Bootstrap vLLM Installation

Run the complete setup script on your VM:

ssh ubuntu@YOUR_VM_IP 'bash -s' <<'EOF'
set -euxo pipefail

VLLM_MODEL="${VLLM_MODEL:-Qwen/Qwen2.5-0.5B-Instruct}"
VLLM_PORT="${VLLM_PORT:-8000}"

sudo apt-get update
sudo DEBIAN_FRONTEND=noninteractive apt-get install -y \
  python3-venv python3-pip git curl

mkdir -p ~/vllm-openai
python3 -m venv ~/vllm-openai/venv
. ~/vllm-openai/venv/bin/activate

python -m pip install --upgrade pip setuptools wheel
python -m pip install \
  --extra-index-url https://download.pytorch.org/whl/cu121 \
  'torch==2.4.0' \
  'vllm==0.6.3.post1' \
  'transformers==4.45.2' \
  lm-format-enforcer

sudo tee /etc/systemd/system/vllm-openai.service >/dev/null <


This installs Python dependencies, creates a virtual environment, and configures vLLM as a systemd service.



4

Verify Service Status
Check that the vLLM service started successfully:
ssh ubuntu@YOUR_VM_IP 'systemctl is-active vllm-openai'
Expected output: active



5

Test Models Endpoint
Wait for model loading and verify the API is responding:
ssh ubuntu@YOUR_VM_IP 'bash -s' <<'EOF'
set -euo pipefail
for _ in $(seq 1 90); do
  if curl -sf http://127.0.0.1:8000/v1/models >/tmp/vllm-models.json; then
    cat /tmp/vllm-models.json
    echo
    echo VLLM_MODELS_OK
    exit 0
  fi
  sleep 10
done
journalctl -u vllm-openai -n 160 --no-pager
exit 1
EOF
This polls the models endpoint with a 15-minute timeout. Model downloading may take several minutes on first startup.



6

Run Chat Completion Test
Verify inference is working with a chat completion request:
ssh ubuntu@YOUR_VM_IP 'bash -s' <<'EOF'
set -euo pipefail
cat >/tmp/vllm-chat.json <

This sends a test prompt and validates the response format and content.



7

Confirm GPU Utilization
Verify the request used GPU resources:
ssh ubuntu@YOUR_VM_IP 'nvidia-smi --query-gpu=name,memory.used,utilization.gpu --format=csv,noheader'
You should see memory usage indicating the model is loaded on the GPU.


Troubleshooting
Common Issues
SSH connection fails: Refresh known_hosts with ssh-keygen -R YOUR_VM_IP if you see host key warnings. Verify the VM is in running status and the IP address is correct.
CUDA not detected: Ensure you launched with image 184 (Ubuntu Server 24.04 w/ Drivers). Run nvidia-smi to confirm driver installation before proceeding with vLLM setup.
Model loading timeout: Large models require significant download time on first startup. Monitor with journalctl -u vllm-openai -f to see download progress. Consider using a smaller model for testing.
Out of memory errors: Reduce --gpu-memory-utilization to 0.5 or lower, switch to a smaller model, or upgrade to a GPU with more VRAM.
Gated model access: Some models require Hugging Face authentication. Set HF_TOKEN environment variable in the systemd service or use an ungated model for testing.
Port 8000 conflicts: Check for existing processes with sudo lsof -i :8000. Stop conflicting services or change VLLM_PORT in the service configuration.
Service crashes: Inspect logs with journalctl -u vllm-openai -n 160 --no-pager to identify startup issues. Common causes include insufficient memory, CUDA driver problems, or model download failures.
Skip All of This: Deploy with an AI Agent
This guide exists as a tested, machine-readable recipe in the Massed Compute MCP. Instead of running these steps manually, you can deploy vLLM through compatible AI agents using natural language.
Add this MCP server to your AI agent configuration:
{
  "mcpServers": {
    "massed-compute": {
      "type": "http",
      "url": "https://vm.massedcompute.com/api/mcp",
      "headers": { "Authorization": "Bearer MC_TOKEN" }
    }
  }
}
Then say:

      "Launch a GPU VM with vLLM and set up an OpenAI-compatible API endpoint. Use the Qwen 2.5 0.5B model for testing and verify chat completions are working."
    
The agent matches your request against the recipe catalog, provisions the right GPU VM, runs the complete setup and verification workflow above, and reports back with connection details and test results. If any step fails, the process stops and provides diagnostic information.
Recipe tested on June 10, 2026

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Quick Setup Reference
For experienced users, here's the essential command sequence:
# 1. Launch GPU VM with image 184
# 2. Verify GPU
ssh ubuntu@YOUR_VM_IP 'nvidia-smi'

# 3. Bootstrap vLLM
ssh ubuntu@YOUR_VM_IP 'bash -s' < bootstrap-script.sh

# 4. Test endpoints
ssh ubuntu@YOUR_VM_IP 'curl -sf http://127.0.0.1:8000/v1/models'
ssh ubuntu@YOUR_VM_IP 'curl -sf http://127.0.0.1:8000/v1/chat/completions -H "Content-Type: application/json" -d "{...}"'

# 5. Monitor service
ssh ubuntu@YOUR_VM_IP 'journalctl -u vllm-openai -f'
Frequently Asked Questions

01What models work with this setup?
Any Hugging Face model supported by vLLM and transformers. Popular choices include Llama, Qwen, Mistral, and Code Llama families. Check vLLM documentation for the latest compatibility matrix. Gated models require a Hugging Face token.


02How do I change the model after deployment?
Edit /etc/systemd/system/vllm-openai.service, update the VLLM_MODEL environment variable, then run sudo systemctl daemon-reload && sudo systemctl restart vllm-openai. The new model will download on restart.


03Can I run multiple models simultaneously?
Yes, but each model requires its own service instance on different ports. Consider GPU memory constraints and use tensor parallelism for large models. You may need multiple GPUs or larger VRAM for multiple concurrent models.


04How do I enable external access to the API?
Change --host 127.0.0.1 to --host 0.0.0.0 in the systemd service, configure firewall rules, and consider adding authentication middleware. For production, use a reverse proxy with TLS termination.


05What's the difference between spot and on-demand pricing?
Spot instances can be interrupted with short notice but offer significant cost savings. On-demand instances provide guaranteed availability. Use spot for development and testing, on-demand for production workloads requiring high availability.