[{"data":1,"prerenderedAt":24},["ShallowReactive",2],{"blog-tag-en-gpu-scheduling":3},[4,16],{"path":5,"title":6,"description":7,"date":8,"tags":9},"\u002Fblog\u002Fen\u002Fk3s-ai-inference-gpu-scheduling-dra-keda-kserve","Kubernetes Hasn't Changed Its Design in 10 Years — Here Are 3 Assumptions Your K3s Cluster Must Rethink for AI Inference Workloads","Ten years after Kubernetes launched, AI agents and LLM inference have upended three of its core assumptions: how GPUs are allocated, how autoscaling decisions are made, and how model serving is built. Here's how to rethink your K3s cluster with Dynamic Resource Allocation, KEDA, and KServe.","2026-08-31",[10,11,12,13,14,15],"k3s","kubernetes","ai-workload","gpu-scheduling","dynamic-resource-allocation","mlops",{"path":17,"title":18,"description":19,"date":20,"tags":21},"\u002Fblog\u002Fen\u002Fkubernetes-mlops-gpu-scheduling-talent-shortage","In the Age of AI-Written Code, Why Are Infrastructure Engineers Getting Raises? Inside the 'MLOps Talent Shortage' Fueled by the Corporate AI Adoption Rush","Generative AI has made it possible for almost anyone to write code, yet as more companies adopt AI, demand for MLOps talent who can reliably run GPUs and model serving on Kubernetes keeps rising. Here's why, and how a managed Kubernetes platform can help.","2026-07-30",[11,10,15,22,13,23],"ai-infrastructure","devops",1792488960074]