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Multi-Tenancy, Quotas and Fair Share
A shared GPU pool is cheaper than ten private ones because ten teams' demand is smoother than one team's, and it only works if the sharing is enforced. Quotas say what each team is guaranteed, borrowing lets idle guarantees be used by others, fair share decides who waits when everyone wants more, and preemption reclaims borrowed capacity. This page works the arithmetic that makes pooling worth it, the layers of isolation a tenant needs, and the incentive problems (hoarding, gaming, the research-versus-product tension) that any policy has to survive.
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Kubernetes, Slurm & GPU SchedulingEight research teams share 1,024 GPUs. Design the quota and fairness policy, and tell me how they will game it.→AI Infrastructure System DesignDesign a GPU credit system: accounts, spending rates, priorities and fairness. Start with the data model.→GPU & Accelerator ArchitectureMIG versus MPS: what isolation does each give you when sharing a GPU, and which would you pick for a multi-tenant inference node?→Kubernetes, Slurm & GPU SchedulingOne fleet: training that wants every idle GPU, and inference with a p99 SLO. Separate pools, or one pool with preemption? Show the numbers.→Open-Weights Models & Serving EnginesOne tenant's traffic is pushing everyone else past their latency objective. What do you do?→Networking, Interconnects & StorageDesign storage for a multi-tenant GPU cloud. How do you keep one customer's checkpoint burst from slowing another's training run?→
