Explain how a Slurm job actually gets its GPUs: GRES, partitions, cgroups, and the sbatch that lands 8 GPUs on one node.

Follow a GPU request from node inventory through allocation, device confinement and task launch. A single-node, eight-task example shows how resource shape and per-task device numbering fit together.

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

Follow a GPU request from node inventory through allocation, device confinement and task launch. A single-node, eight-task example shows how resource shape and per-task device numbering fit together.

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The concepts behind this question

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

Core

🗂️ Scheduling & OrchestrationSign in

Slurm for AI ClustersSlurm allocates resources to batch jobs and launches their tasks. GPU requests, task placement and queue policy work together to determine when a training job runs.

Foundational

🗂️ Scheduling & Orchestration

Node Lifecycle: Drain, Upgrade and ReturnFleet maintenance moves nodes through drain, repair and validation before returning them to service. Deadlines and concurrency limits keep that work within the available capacity.

Foundational

🗂️ Scheduling & Orchestration

Slurm vs KubernetesSlurm and Kubernetes provide different foundations for running GPU workloads. Choose the operating model and supporting components that fit the users and jobs.

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 trackWe want to run LLM inference on Kubernetes. What does the deployment actually need beyond a Deployment with a GPU limit?Next in this trackDesign the scheduling and isolation for a multi-tenant fine-tuning service: hundreds of customers, a few base models, shared GPUs.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?

FEDITOR'S NOTE

Check that the script requests one node and eight GPUs, makes task binding explicit, and distinguishes CUDA visibility from cgroup enforcement. Explain signal handling without promising automatic checkpoints.

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