MLOps GPU Inference Platform Using Nvidia Triton Inference Server

Scalable Inference using NVIDIA Triton & AWS EKS

"If it isn’t observable, automated, and scalable, it isn’t production-ready."

This project is an end-to-end implementation of a high-concurrency inference ecosystem designed to solve the "GPU Waste" problem. By leveraging NVIDIA Triton on AWS EKS, I’ve built a system that treats AI models as first-class citizens in a production-grade inference platform, maximizing throughput and reducing infrastructure costs.

System Architecture

The Stack

• Inference Server: NVIDIA Triton (PyTorch, ONNX, TensorRT)
• Orchestration: Kubernetes (EKS/GKE)
• Infrastructure as Code: Terraform (GPU/CPU node groups, VPC, IAM)
• Automation: Python CI/CD pipelines, GitHub Actions
• Deployment Packaging: Helm charts (Triton, monitoring, plugins)
• Storage: EFS CSI Driver, S3/ECR model repo
• Networking: Amazon VPC CNI, Calico (optional), Istio (service mesh)
• Observability: Prometheus, Grafana, Loki, DCGM Exporter, nvidia-smi
• Security: IAM roles, Kubernetes RBAC, Secrets Manager/KMS, Network Policies

🧱 Architecture Overview

Layer	Purpose
VPC + Subnets	Isolated, AZ-resilient network for GPU workloads
IAM + Policies	Fine-grained access for operators, CI/CD, and IRSA
EKS Cluster	Managed Kubernetes control plane with GPU node groups
Node Groups	Separate GPU and general-purpose pools for cost control
ALB Controller (IRSA)	Ingress with service account–scoped permissions
Triton Inference	GPU-enabled pods serving models via gRPC/HTTP
EFS	Shared model repository mounted into Triton pods
CI/CD	GitHub Actions + Terraform + Helm for secure automation
Observability	Prometheus, Grafana, Loki, CloudWatch integration
Service Mesh	Istio for canary rollouts

🔐 Security & Compliance

• IAM roles scoped via IRSA
• GitHub OIDC trust for CI/CD
• Secrets managed via Kubernetes
• zero Trust: mTLS via Istio service mesh

📦 Reproducibility & Lifecycle :

• Infrastructure-as-code via Terraform
• Helm charts for Triton + observability stack
• Modular teardown and rebuild workflows
• Versioned container images via Amazon ECR
• Models hosted in the aws EFS using the kubernetes persistent volume

🚀 Key Engineering Wins

1. Optimized GPU Utilization Implemented Dynamic Batching and Instance Groups. This allows the server to group individual inference requests together on the fly, significantly increasing throughput without manual tuning.

2. Multi-Model Concurrency Configured the ecosystem to serve multiple models (Image Classification & NLP) simultaneously on a single GPU/Node, reducing infrastructure overhead and costs—a critical requirement for Fortune 500 efficiency.

3. SRE-First Observability Integrated custom Prometheus exporters to track:

   • Inference Latency (p95/p99)

   • Request Queue Time

   • GPU Memory Utilization

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