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Design the physical layer for a multi-tenant GPU cloud. What changes versus a single-tenant cluster?

Choose a supported tenant boundary, secure the surrounding services, and verify cleanup before reassignment. Calculate fragmentation from actual requests.

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

Choose a supported tenant boundary, secure the surrounding services, and verify cleanup before reassignment. Calculate fragmentation from actual requests.

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The concepts behind this question

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

📐 AI Systems Design🔒 Premium

Multi-Tenant Fine-Tuning ServiceA fine-tuning service turns a tenant’s dataset into an evaluated model or adapter. Its pipeline must protect data while scheduling training and serving the result efficiently.

📐 AI Systems Design🔒 Premium

Serverless GPU PlatformsA serverless GPU platform hides allocation and startup from its users. The design balances cold-start latency, tenant isolation and the cost of idle capacity.

🗂️ Scheduling & Orchestration🔒 Premium

Workload Identity and SecretsA running model service needs permission to read weights and emit telemetry. Separate the workload identity from its credentials, scope access to specific operations, and test expiry, revocation and cross-tenant denial.

🗂️ Scheduling & Orchestration🔒 Premium

GPU Tenant Isolation BoundariesGPU allocation, virtual machines and network policies protect different boundaries. Trace each tenant's device, network and storage paths, then test both permitted work and forbidden access before declaring a shared GPU service isolated.

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UP NEXT ON YOUR JOURNEY

Next in this trackWalk me through cabling a rail-optimized cluster. What goes where, and what goes wrong?Next in this trackThe cluster passed every acceptance test and production throughput is 20 percent below plan. Where do you look?Next in this trackRoll out a driver and firmware upgrade across a live 2,048-GPU fleet without losing a training run.

FEDITOR'S NOTE

Look for explicit tenant privileges, enforced boundaries, verified reassignment and correct request-level capacity arithmetic.

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