mig

See the GPU-instance and compute-instance boundaries, budget a small slice, and distinguish memory capacity controls from bandwidth isolation.

Size adapter and full-parameter jobs before comparing RTX PRO 6000 and H100. Test memory headroom, MIG packing, software support and real communication costs.

Compare the resource promises of MIG, time-slicing and MPS, then test peak memory and simultaneous demand before packing workloads.

Design fractional GPU admission around explicit shapes and enforceable guarantees, then reconcile reservations with the runtime.

DRA gives the scheduler device claims and attributes to work with. Compare that with extended-resource counts, then check the deployed driver and controller support before promising sharing, partitioning or gang placement.

Size notebook capacity from concurrent demand, memory needs and burst tolerance. Choose a sharing mode for the trust model, and make idle release explicit about which state can actually be restored.