Production-Grade LLM Inference Service: Architectural Practice Based on AWS EKS and GPU Auto-Scaling

Original Author/Maintainer: AntonMingov Source Platform: GitHub Original Title: ai-inference-service Original Link: https://github.com/AntonMingov/ai-inference-service Source Publication/Update Time: 2026-06-01T09:44:11Z

This article details how to build a production-grade large language model (LLM) inference service on AWS EKS, covering GPU auto-scaling, load balancing, service discovery, and cost optimization strategies, providing actionable deployment solutions for AI engineering teams. Subsequent floors will break down the core content into modules.

Converting LLMs from research prototypes to production services faces complex challenges: handling high-concurrency requests, ensuring low-latency responses, implementing elastic scaling, and maintaining stability and reliability while keeping costs under control. This project provides a complete reference implementation, demonstrating the deployment scheme of LLM inference services with GPU auto-scaling on AWS EKS.

The core architecture is based on Kubernetes and AWS managed services:

• Infrastructure Layer: AWS EKS as the container orchestration platform, with GPU nodes using EC2 P4d/G5 instances (equipped with A100/A10G GPUs);
• Model Service Layer: vLLM or TGI inference engines, supporting optimizations like continuous batching and paged attention;
• Load Balancing Layer: AWS ALB or NGINX Ingress for traffic distribution;
• Auto-Scaling: Cluster Autoscaler (node-level) + HPA (pod-level) for elastic adjustment.

Key implementations for GPU scaling:

• Node-level: Cluster Autoscaler monitors pending GPU pods and triggers node expansion when resources are insufficient (min/max node counts are set to control costs);
• Pod-level: A custom Metrics Server exposes GPU utilization, and HPA dynamically adjusts the number of pod replicas based on metrics;
• Stability: Scaling cooling periods prevent frequent fluctuations, and graceful shutdown ensures requests are completed.

Core optimizations of the vLLM inference engine:

• PagedAttention: KV cache paging management reduces memory fragmentation and improves concurrent throughput;
• Continuous Batching: Dynamic batching of new requests increases GPU utilization;
• Quantization Support: AWQ/GPTQ formats enable running large models under memory constraints or improving concurrency;
• Speculative Decoding: Draft model prediction + main model verification accelerates generation while maintaining quality.

Service Discovery: Model registry records information, dynamic routing distributes traffic by model identifier (supports A/B testing and canary releases), and readiness probes ensure healthy pods receive requests; Monitoring: Prometheus collects metrics like GPU utilization, DCGM provides GPU details, Fluent Bit aggregates logs, Jaeger/X-Ray tracks requests, and Alertmanager triggers alerts; Security: Network isolation (private subnets + NAT), IRSA fine-grained permissions, input/output filtering, data encryption, and audit logs for compliance.

Cost Optimization: Spot instances save 70% of costs (with graceful interruption handling), multi-model GPU resource sharing, auto-scaling during off-hours, and reserved instances/Savings Plans reduce base load costs; Deployment Operations: Terraform defines AWS resources, GitOps (ArgoCD/Flux) for continuous deployment, MLflow tracks model versions (supports rollback), and disaster recovery (etcd backups + failover plans).

Key project takeaways:

1. Layered scaling: Node + pod-level auto-adjustment for efficient resource utilization;
2. Engine optimization: vLLM features maximize hardware returns;
3. Observability: Comprehensive monitoring system to detect issues timely;
4. Security first: Defense-in-depth ensures system safety;
5. Cost awareness: Multi-strategy control of cloud resource costs.

Cloud-native elastic architecture will become the standard choice for enterprise LLM applications.