GPU partitioning is the practice of dividing a graphics processor or AI accelerator into smaller, isolated slices so multiple workloads can run at the same time without fully sharing the same device. The split may be enforced by hardware features, virtualization, or scheduling software, depending on the platform. Each slice gets a defined amount of compute, memory, and sometimes bandwidth.
In cyber security, partitioning matters because shared accelerators are valuable and easy to overload. Strong isolation helps prevent a noisy tenant, runaway agent, or untrusted job from consuming all GPU time and causing latency spikes, service degradation, or denial-of-wallet costs. Defenders use partitioning to separate tenants, limit blast radius, and keep critical workloads responsive. If isolation is weak, attackers may exploit resource contention rather than break in directly, turning shared compute into an operational attack surface.