Design the scheduling and isolation for a multi-tenant fine-tuning service: hundreds of customers, a few base models, shared GPUs.

Choose the tenant boundary before planning base-model reuse. Account for frozen weights, adapter state and activations separately, then schedule by compatibility, fairness and the real cost of restarting training.

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

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The concepts behind this question

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

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📐 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.

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.

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🧮 Open Weights & Serving Engines🔒 Premium

Pretraining, Fine-Tuning and DistillationUnderstand how a base model becomes a useful application model. Separate training objectives from LoRA and quantization, compare distillation with retrieval, and choose the smallest intervention supported by your evaluation.

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.

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UP NEXT ON YOUR JOURNEY

Next in this trackThe cluster dashboard says 90% allocated and 30% utilized. What is happening, how do you prove it, and what policy fixes it?Next in this trackWe have GPUs in two clouds. Can we run one training job across both, and what would you do instead?Next in this trackDesign a notebook platform for 300 researchers on 64 GPUs. How do you share, reclaim and account for the GPUs?

FEDITOR'S NOTE

Do not assume separate processes or devices share a frozen base in HBM. Distinguish adapter exports from resumable checkpoints, and use scoped memory and compute arithmetic.

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