What is GPU scheduling fairness in shared ML infrastructure?

GPU scheduling fairness ensures equitable allocation of GPU resources across competing ML workloads.

In shared GPU clusters, multiple training and inference workloads compete for limited GPU resources. Fair scheduling prevents starvation and ensures SLA compliance. Techniques include priority queues, quota enforcement, preemption, and bin packing optimization. Advanced schedulers consider memory fragmentation and compute intensity.