SNOW Corp. operates 1,000+ A100 GPUs serving 200 million users across three top-ranked GenAI applications (Snow, Epik, B612), handling 1,200+ AI workflows subject to extreme traffic volatility from viral AI trends.

The core bottleneck was Kubernetes' native GPU scheduling, which treats GPUs as atomic resources — forcing a 2x over-provisioning penalty on Train-to-Inference pipelines with no reliable visibility into actual GPU saturation.

This talk covers integrating HAMi for vGPU virtualization and extending KEDA with a custom Consumer Saturation metric for proactive autoscaling, hiding warm-up latency by scaling before traffic arrives.

We’ll detail the implementation: scheduler config, Prometheus metrics, and multi-region scaling via Helm GitOps. Results: >50% GPU waste cut and 91% faster recovery during surges. You’ll get a production blueprint for efficient, shared GPU platforms.