One tenant's traffic is pushing everyone else past their latency objective. What do you do?

One tenant can consume a shared deployment's capacity without exceeding any limit you set, because the limits are usually on requests and the cost is in tokens. What actually needs limiting, the three isolation levels by strength and price, and the fix that works in minutes.

Updated Sep 2026 · Grounded in real AI infrastructure interview loops and written to a senior-engineer editorial bar, with every number worked and every diagram hand-built.

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The concepts behind this question

Ranked by how closely each one overlaps this question's topic, so the first card is the thing to read if the answer above moved too fast.

Advanced

🗂️ Scheduling & Orchestration🔒 Premium

Multi-Tenancy, Quotas and Fair ShareA 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.

Core

🗂️ Scheduling & OrchestrationSign in

MIG, MPS and Time-SlicingA whole H100 is far more than a notebook, a small inference service or a CI job needs, and giving each of them a card leaves most of the fleet idle. Three mechanisms share a GPU, and they differ in what they isolate: MIG partitions the hardware into up to seven slices with their own memory and compute, MPS lets several processes share one GPU's SMs concurrently with no memory isolation, and time-slicing context-switches between processes with no isolation at all. The choice is the isolation the workload needs against the utilization the platform wants.

Foundational

📐 AI Systems Design

Multi-Region Serving and FailoverRunning inference in more than one region buys latency for distant users and survival when a region fails, and it costs a second fleet that must be capable of absorbing the first one's traffic. The design turns on three decisions: whether regions are active-active or active-passive, what state has to cross regions and what deliberately does not, and how much headroom each region carries so a failover does not simply move the outage.

Core

📐 AI Systems DesignSign in

GPU Job Scheduler DesignDesign a scheduler for a shared GPU cluster is the most common design prompt in AI infrastructure interviews, because it touches everything: queues and priorities, gang placement, topology, fairness across teams, preemption and the checkpoints that make it survivable, and the failure handling that keeps a 512-GPU job alive. This page builds the design in layers, states the data model and the scheduling loop, derives the numbers (how long a job waits, how much preemption costs, how much fragmentation wastes), and lists the trade-offs the interviewer will push on.

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UP NEXT ON YOUR JOURNEY

Next in this trackThree open-weights models could serve your product. How do you choose?Next in this trackThe engine logs say CUDA graphs are disabled for your deployment. Does it matter?Next in this trackFifty teams have fine-tuned the same base model. How do you serve all of them?

FEDITOR'S NOTE

Scored on limiting the resource that is actually consumed, on the three isolation levels with their costs, and on an immediate mitigation separate from the durable fix.

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