Abstracting AI Infrastructure: Native GPU Scaling for Internal Developer Platforms
Platform Engineering, Friday, May 1st, 2026
Native GPU scaling via keda-gpu-scaler bypasses Prometheus for real-time LLM inference autoscaling in Kubernetes.
Modern Internal Developer Platforms must abstract infrastructure complexity, but AI workloads break traditional CPU-based autoscaling approaches.
ML engineers are forced to become infrastructure experts navigating Prometheus, KEDA, and GPU telemetry tools, creating poor developer experience. The keda-gpu-scaler solves this by deploying as a DaemonSet that directly queries NVIDIA hardware via go-nvml bindings, eliminating Prometheus latency and providing ML engineers with simple, declarative scaling configurations.
This edge-native architecture reduces scaling latency to near-zero and restores the Golden Path for AI infrastructure teams managing LLMs and other GPU-intensive workloads.