Kubernetes AI workloads
Autoscaling AI Workloads on Kubernetes With KEDA and What it Means for Agentic Systems
KEDA can scale Kubernetes AI workloads on real demand signals such as queue depth, helping model-serving and agent workloads respond faster while reducing idle compute costs ...
Open Source KServe AI Inference Platform Becomes CNCF Project
The CNCF adopts KServe to strengthen cloud-native AI inference on Kubernetes as platforms like Red Hat OpenShift AI expand model-as-a-service capabilities ...
Google Extends Kubernetes Service to Safely Run Agentic AI Workloads
At KubeCon + CloudNativeCon North America 2025, Google unveiled major GKE upgrades — including an AI sandbox, inference gateway, pod snapshots, and 130,000-node clusters — to optimize and secure agentic AI workloads ...
GPU Resource Management for Kubernetes Workloads: From Monolithic Allocation to Intelligent Sharing
AI and ML workloads in Kubernetes are evolving fast—but traditional GPU allocation leads to massive waste and inefficiency. Learn how intelligent GPU allocation, leveraging technologies like MIG, MPS, and time-slicing, enables smarter, ...
Fitting Square Kubernetes Into the Round AI-Native Apps
Kubernetes tamed cloud-native workloads, but AI-native apps push its limits. Can it evolve for GPU-first, data-intensive AI — or is it time for new control planes? ...
