Optimizing Model Inference Latency with NVIDIA Triton Inference Server on Amazon EKS
Table of Contents Introduction Why Latency Matters in Production ML NVIDIA Triton Inference Server: A Quick Overview Why Run Triton on Amazon EKS? Preparing the AWS Environment 5.1 Creating an EKS Cluster with eksctl 5.2 Setting Up IAM Roles & Service Accounts Deploying Triton on EKS 6.1 Helm Chart Basics 6.2 Customizing values.yaml 6.3 Launching the Deployment Model Repository Layout & Versioning Latency‑Optimization Techniques 8.1 Dynamic Batching 8.2 GPU Allocation & Multi‑Model Sharing 8.3 Model Warm‑up & Cache Management 8.4 Request/Response Serialization Choices 8.5 Network‑Level Tweaks (Service Mesh & Ingress) Monitoring, Profiling, and Observability 9.1 Prometheus & Grafana Integration 9.2 Triton’s Built‑in Metrics 9.3 Tracing with OpenTelemetry Autoscaling for Consistent Latency 10.1 Horizontal Pod Autoscaler (HPA) 10.2 KEDA‑Based Event‑Driven Scaling Real‑World Case Study: 30 % Latency Reduction Best‑Practice Checklist Conclusion Resources Introduction Model inference latency is often the decisive factor between a delightful user experience and a frustrated one. As machine‑learning workloads transition from experimental notebooks to production‑grade services, the need for a robust, low‑latency serving stack becomes paramount. NVIDIA’s Triton Inference Server (formerly TensorRT Inference Server) is purpose‑built for high‑throughput, low‑latency serving of deep‑learning models on CPUs and GPUs. When combined with Amazon Elastic Kubernetes Service (EKS)—a fully managed Kubernetes offering—organizations gain a scalable, secure, and cloud‑native platform for serving models at scale. ...