GPU autoscaling on Kubernetes with KEDA: Building an external scaler

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If you run GPU workloads on Kubernetes — vLLM, Triton, training jobs, or the newer agentic inference stacks — you’ve probably hit a familiar problem: the default autoscaling path still reasons about CPU and memory, while the GPU that is actually doing the work stays hidden.

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