Deploy vLLM on AWS in Under 10 Minutes

Learn to deploy vLLM on AWS infrastructure in under 10 minutes using automated Ansible playbooks. Discover what vLLM is and why it delivers superior inference performance through memory optimization techniques. Explore AWS service quota requirements for GPU instances and identify the best instance types for getting started. Master the prerequisites including Ansible collections, AWS CLI setup, and Hugging Face access token creation. Execute the aws_helper playbook to automatically generate AWS configuration variables including region, GPU instance, VPC, subnet, key pair, and disk size settings through guided prompts. Deploy the vllm_installer playbook to provision instances, install required drivers and Docker, and pull the vLLM container without manual dependency management. Test your fully operational vLLM server endpoint using curl commands and access the complete automation playbooks from the provided GitHub repository.

Syllabus

0:00 Why vLLM and why it’s so fast
1:22 How vLLM optimizes memory & inference performance
3:29 AWS service quota requirement for GPU instances
4:18 Best AWS instance to use for just getting started
5:03 Ansible + collection prerequisites
6:04 AWS CLI and credential setup
7:11 Creating a Hugging Face access token
7:58 Playbook 1 – aws_helper walkthrough
9:56 Reviewing the generated vars file
9:59 Playbook 2 – vllm_installer deployment
10:40 Instance provisioning & dependency installation
11:45 vLLM server is live
12:03 Testing with curl