Deploy LLMs on Amazon EKS using vLLM Deep Learning Containers

This article provides a comprehensive guide to deploying large language models (LLMs) on Amazon EKS using vLLM Deep Learning Containers (DLCs), focusing on deploying the DeepSeek-R1-Distill-Qwen-32B model with high-performance infrastructure.

• Solution leverages AWS services including EKS, P4d instances with NVIDIA A100 GPUs, Elastic Fabric Adapter (EFA), and FSx for Lustre
• Uses AWS Deep Learning Containers for vLLM to simplify deployment and optimize performance
• Demonstrates step-by-step process of: - Creating an EKS cluster - Setting up GPU-enabled node groups - Configuring FSx for Lustre storage - Installing necessary Kubernetes controllers - Deploying vLLM server using LeaderWorkerSet pattern
• Provides API endpoints for text completions, chat completions, and embeddings
• Highlights performance benefits of EFA, FSx for Lustre, and Application Load Balancer

The solution aims to help organizations deploy LLMs efficiently, optimize GPU resources, and create scalable, high-performance inference systems with minimal operational overhead.