Category: Partner solutions

In this post, we explore how AWS and NVIDIA Run:ai are extending GPU fractionalization and orchestration capabilities beyond traditional cloud regions to edge environments, including AWS Local Zones, Outposts, and EKS Hybrid Nodes. The collaboration addresses the growing demand for distributed AI/ML workloads that require efficient GPU resource management across geographically separated locations while maintaining consistent performance, compliance, and cost optimization .

In this post, we demonstrate how to utilize the Kubecost Amazon EKS add-on to reduce infrastructure costs and enhance Kubernetes efficiency through Container Request Right Sizing, which helps identify and fix inefficient container resource configurations. We explore how to review Kubecost’s right sizing recommendations and implement them through either one-time updates or scheduled automated resizing within Amazon EKS environments for continuous resource optimization.

This post was co-authored with Chad Chapman of NVIDIA. Introduction In the fast-paced world of artificial intelligence and machine learning, GPU resources are both critical and in high demand. In this blog, we will cover key challenges related to GPU utilization in Artificial Intelligence and Machine Learning applications, and how NVIDIA Run:ai fractional GPU technology […]