Deploying Generative AI on Amazon EKS: A Practical Guide to Enterprise-Grade Large Model Operations

This article is based on AWS's open-source project AI on EKS (original author: shehuj, source: GitHub, original link: https://github.com/shehuj/generativeAI_on_eks). It provides an in-depth analysis of how to scale the deployment and operation of generative AI models on Amazon EKS (Kubernetes clusters), covering best practices for mainstream inference frameworks such as vLLM, NVIDIA Triton, and HuggingFace TGI, including enterprise-level operational key points like GPU scheduling, auto-scaling, observability, and security compliance.

With the explosion of large language models (LLMs) and generative AI technologies, enterprises face complex engineering challenges in transforming lab models into stable production services:

• Traditional monolithic deployment struggles to meet demands such as scarce GPU resources, sudden request surges, and frequent model iterations
• Enterprises tend to adopt private deployment to meet data privacy and compliance requirements
• Kubernetes + Amazon EKS provides an ideal foundation for generative AI platforms, but building from scratch requires in-depth knowledge of GPU scheduling, network optimization, and other details. The AI on EKS project aims to address this issue.

AI on EKS provides Terraform blueprints, with core architecture components including:

GPU Node Groups & NVIDIA Device Plugins

Automatically configure GPU instances (p4d, g5 series), install NVIDIA device plugins and GPU feature discovery plugins, support CUDA drivers and GPU sharing strategies (time slicing/MPS)

Inference Service Framework Integration

Support frameworks like vLLM (high-throughput inference), NVIDIA Triton (unified multi-framework service), HuggingFace TGI (Transformer optimization), Ray Serve (complex model combination), and provide corresponding Terraform modules and Helm Charts

Auto-scaling & Cost Optimization

Integrate Karpenter for high-performance node scaling, support Spot instances, hybrid instance strategies (On-Demand + Spot), and HPA/VPA request-level scaling

vLLM High-Throughput Inference Deployment

• Configure multi-GPU tensor parallelism to support ultra-large models (e.g., 70B Llama 2)
• Enable continuous batching and prefix caching to improve GPU utilization
• Configure Prometheus monitoring to track throughput, latency, and GPU utilization

NVIDIA Triton Multi-Model Service

• Use TensorRT-LLM to optimize model performance
• Merge requests via dynamic batching, balance latency and throughput with model concurrency control
• Set up model repositories and hot update mechanisms

Ray Serve Complex Workflow Orchestration

• Integrate model services with feature engineering and post-processing logic to implement end-to-end inference pipelines

Log & Metric Collection

• Fluent Bit sends logs to CloudWatch Logs, and Grafana dashboards display metrics like GPU utilization, inference latency, and throughput

Distributed Tracing

• AWS X-Ray/Jaeger implement request-level tracing to identify performance bottlenecks

Alerting & SLO Management

• Prometheus AlertManager configures alert thresholds: excessive inference latency, high GPU memory usage, abnormal pod restarts, queue backlogs, etc.

Network Isolation & Access Control

• AWS PrivateLink connects to the EKS control plane privately, network policies restrict pod communication, and IRSA provides fine-grained access control

Model & Data Security

• Secrets Manager/Vault manage credentials, EBS/S3 encrypt model weights for storage, and filter input/output content

Compliance Auditing

• CloudTrail records API calls, VPC Flow Logs audit network traffic, and AWS Config monitors resource changes

Node Selection Strategy

• Use g5.xlarge for development/testing (cost-effective), p4d.24xlarge for production (multi-GPU support for large models)

Storage Configuration

• Use EFS/FSx for Lustre for shared storage of model weights, configure PV/PVC to avoid repeated data downloads

Network Optimization

• Enable EFA low-latency communication, tune MTU and TCP parameters

Cost Management

• Karpenter integrates Spot instances, auto-scales down nodes during off-peak periods, and uses Savings Plans/Reserved Instances to lock capacity

The AI on EKS project provides a validated blueprint for enterprise generative AI infrastructure, covering key components like inference frameworks, scaling, and observability. The project continuously updates to support new models and technologies, making it an important reference implementation for enterprises planning generative AI platforms.