[{"data":1,"prerenderedAt":16},["ShallowReactive",2],{"blog-tag-en-dynamic-resource-allocation":3},[4],{"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",1792488960196]