Green Contexts let CUDA work reserve a share of GPU compute

NVIDIA’s Green Contexts let a process assign portions of a GPU’s streaming multiprocessors and work queues to different workloads. That can keep a small latency-sensitive kernel from waiting behind bulk work. Stream priority alone cannot interrupt a block that is already running, so reserving compute resources addresses a different scheduling problem.

NVIDIA · Oct 6, 2026

Why it matters

Useful when one application mixes background GPU throughput work with requests that need predictable responsiveness.

More context

Try the resource-query and stream-creation workflow on supported hardware, then measure both latency and bulk throughput. Driver support began in CUDA 12.4; CUDA 13.1 adds Runtime API access. The tutorial explains an available mechanism rather than announcing a new GPU.

Keep in mind

Reserving resources leaves fewer for other work. The example workload does not establish your application’s speedup, and compute partitioning here should not be mistaken for a security boundary or a hard real-time guarantee.

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