Trial
Why?
- Scheduling, partitioning, and multiplexing scarce GPU resources efficiently is critical to utilization and cost control.
- Integrations with ML platforms (Kubeflow, Ray, K8s device plugins) streamline developer workflows.
What?
- Adopt GPU device plugins, topology-aware scheduling, resource quotas, and autoscaling policies.
- Pilot workload-specific orchestration (e.g., low-latency inference vs large-batch training).
- Integrate with tenant isolation, cost tracking, and quota management systems.