TL;DR

GPU fractioning is the practice of running several workloads on one physical GPU, each with its own share of memory and compute, so a small job stops consuming a whole device.

NVIDIA released CUDA 13.4 on 27 October 2026 with a memory boundary the application cannot see, plus an open-source GPU Fractioning Operator that applies per-container shares on every node.

The scheduler writes the contract into four Pod annotations, and any Kubernetes scheduler that can write those annotations can enforce GPU fractions with CUDA.

KAI Scheduler v0.18.0 is the first integration, through a GPU-sharing mode called NvFractions that is enabled with one Helm flag and installs the operator as a dependency.

Time-slicing gives each workload its own error domain and about 1/n of the time; SM-sharing runs everything at once on split streaming multiprocessors inside one shared error domain.

The guaranteed memory request and optional burst limit are the two numbers to verify before a team enables this on a shared…