platform
AI infra interview questions tagged platform, across every topic.
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Concepts behind "platform"
The curriculum that explains the ideas these questions test.
Foundational
Kubernetes GPU SchedulingKubernetes knows nothing about GPUs until something tells it. The NVIDIA device plugin advertises each node's GPUs as a countable resource, the scheduler matches a pod's request to a node with enough of them, and the container runtime wires the device in. That model is enough for one job per GPU and breaks the moment you need sharing, topology or multi-node placement, which is where Dynamic Resource Allocation, the GPU Operator and the batch schedulers come in. Knowing which layer does what is the platform interview's opening question.🗂️ Scheduling & Orchestration
Foundational
Slurm vs KubernetesEvery GPU platform team has this argument, and the two schedulers were built for different jobs: Slurm for long, large, all-or-nothing training on bare metal; Kubernetes for many services that scale up and down. Training fleets run Slurm because gang scheduling, topology and MPI-style launch are native there; serving fleets run Kubernetes because autoscaling and rolling deploys are native there. A platform that does both picks a hybrid: Slurm on Kubernetes (Slinky, Soperator) or a batch scheduler on Kubernetes (Kueue, Volcano, KAI). The interview question is which, for which workload, and why.🗂️ Scheduling & Orchestration
Foundational
Control Plane and API Design for GPU PlatformsEvery GPU platform has a control plane, and its API is what the rest of the organization experiences as the platform. Three semantics decide whether it survives contact with a network: idempotent creation so a retried request does not launch a second job on sixty-four GPUs, cancellation modelled as intent because only the node agent can stop a running process, and cursor pagination that does not skip rows when work is created during a listing.📐 AI Systems Design
Advanced
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.🗂️ Scheduling & Orchestration🔒 Premium