GPU Management and Resource Scheduling

GPUs are the expensive, scarce engine of modern ML, a single accelerator can cost more per hour than a fleet of CPUs, and an organisation rarely has enough to give every job a dedicated card, so how GPUs are allocated, shared, and scheduled directly determines both cost and how much work gets done. GPU management is the discipline of efficiently matching scarce accelerators to the training and inference jobs that need them: requesting them correctly, sharing a card among small jobs, scheduling competing workloads fairly, and avoiding the rampant waste of idle GPUs sitting reserved but unused. The problem is acute because GPUs differ fundamentally from CPUs in scheduling, they are not freely time-sliced by default, a job either holds a whole card or it does not, so naive allocation leaves a powerful card running at five percent utilisation while other jobs queue. Techniques like GPU requests in the scheduler, fractional sharing via time-slicing and MIG, gang scheduling for distributed jobs, and priority and preemption exist to wring value from this scarce resource. Understanding GPU management matters because at the scale where ML gets expensive, the difference between a well-scheduled and a poorly-scheduled cluster is enormous, often the largest single lever on an ML organisation's compute bill and throughput.