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Design a notebook platform for 300 researchers on 64 GPUs. How do you share, reclaim and account for the GPUs?

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.

Updated Oct 2026 · Learn how AI infrastructure works through explanations, worked examples and diagrams, then practise applying it to interview questions.

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.

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The concepts behind this question

Use these concept explanations to review the ideas behind this question.

Core
🗂️ Scheduling & OrchestrationSign in
MIG, MPS and Time-SlicingMIG, MPS and time-slicing share a GPU in different ways. Compare memory capacity, execution behavior and isolation before choosing a mechanism.
Advanced
🗂️ Scheduling & Orchestration🔒 Premium
Multi-Tenancy, Quotas and Fair ShareFollow three teams through GPU borrowing and reclaim. Distinguish quotas, reservations and fair sharing; see why a returning team may leave another above its nominal share, and why failures or indivisible jobs change what can be promised.
Advanced
🗂️ Scheduling & Orchestration🔒 Premium
Gang Scheduling with Kueue and VolcanoGang scheduling treats a distributed job’s required workers as one admission unit. It prevents partial allocations from holding resources while waiting for the rest of the job.
Foundational
🗂️ Scheduling & Orchestration
Kubernetes GPU SchedulingKubernetes coordinates GPU discovery, scheduling and container access through several components. Understanding their separate roles makes allocation failures easier to diagnose.
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UP NEXT ON YOUR JOURNEY
Next in this trackHow do you give a Kubernetes pod RDMA access to the fabric for NCCL, and what goes wrong when it is done naively?Next in this trackUpgrade the GPU driver across 2,000 live nodes without breaking running jobs. Walk me through the plan and what can go wrong.Next in this trackA 64-node gang-scheduled job has been pending for six hours while the cluster shows free GPUs. Debug it live.
FEDITOR'S NOTE

Separate average activity from required concurrent capacity. Use valid device profiles, account for their different resources, and do not promise transparent migration of arbitrary notebook or CUDA state.

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