GPU Scheduling
GPU scheduling is the process of assigning compute workloads to specific GPUs in a cluster based on resource requirements, availability, priority, and optimization goals. A GPU scheduler decides which model runs on which GPU, when to preempt lower-priority work, and how to balance load across heterogeneous GPU hardware.
GPU scheduling is a critical component of GPU orchestration. Naive scheduling (e.g., round-robin) wastes resources because it ignores GPU memory constraints, model affinity, and workload characteristics. Intelligent scheduling considers GPU type, available memory, current load, and model weight locality.
Advanced GPU schedulers support features like priority queues (enterprise tenants get preferred placement), preemption (high-priority requests can interrupt batch workloads), fair-share scheduling (preventing one tenant from monopolizing resources), and bin-packing (maximizing GPU utilization).
TurbOS, Hoonify's GPU orchestration engine, provides intelligent GPU scheduling that was originally developed for HPC workloads at national laboratories. It handles multi-tenant workload placement, model weight locality optimization, and dynamic rebalancing as demand patterns change.
Related Terms
See how gpu scheduling works in practice.
Explore the Platform