Productionizing GPU Inference on EKS with KServe and NVIDIA Triton

Abstract

The increasing use of AI-based applications has brought about a necessity for operationalizing GPU inference on a large scale in production environments. But, the deployment and management of GPU-accelerated machine learning models in the wild are still major challenges that arise from the complexity of infrastructure orchestration, cost management, and model lifecycle automation. This paper is an exploration of a complete framework for productionising GPU inference on Amazon Elastic Kubernetes Service (EKS) with the help of KServe and NVIDIA Triton Inference Server, thus providing a simplified route from model deployment to large-scale checkpointed inference. Amazon EKS provides a controlled Kubernetes base that takes care of the automatic scaling, resilience, and security, whereas KServe makes it easy for the models to be served with the help of standardized APIs and native autoscaling for inference workloads. NVIDIA Triton complements this stack by offering the best performance, single or multi-framework, through GPU optimization, dynamic batching, and model ensemble all very important features to the maximum use of the hardware. They all together form a complete pipeline that is compatible with present-day MLOps practices, thus helping the continuous integration, model versioning, and automated rollouts. This paper also talks about the ways of keeping a balance between the performance and the cost such as GPU sharing, autoscaling policies, and efficient pod scheduling. Using this EKS–KServe–Triton trio, organizations can turn experimental ML models into production-grade, scalable inference services, thus closing the gap between model development and real-world deployment with a cloud-native, cost-optimized approach