Cost Control Strategies for Cloud GPU Clusters
GPU instances are prohibitively expensive if mismanaged. We've cut our ML infrastructure costs by 60% using a combination of spotting, auto-scaling, and rigorous monitoring.
Table of contents:
Spot Instance Orchestration
Using AWS Spot fleets for stateless inference workloads. We handle interruption signals gracefully by returning the active job to the queue instantly.
Scale-to-Zero Architecture
Our Kubernetes clusters use KEDA to scale down to 0 GPU nodes when the job queue is empty, eliminating overnight and weekend idle costs.
Identifying Memory Leaks
A GPU memory leak means an instance goes unused but billed until restarted. We implemented strict memory monitoring and automatic pod restarts upon detection.