Trace Kubernetes pod placement and Ray bundle reservation as separate layers, including logical GPU limits and autoscaling delay.
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← Kubernetes, Slurm & GPU Scheduling / 15
mediumAnyscaleDatabricks
What is Ray on Kubernetes good for, and where do its scheduler and the Kubernetes scheduler fight each other?
Trace Kubernetes pod placement and Ray bundle reservation as separate layers, including logical GPU limits and autoscaling delay.
Updated Sep 2026 · Learn how AI infrastructure works through explanations, worked examples and diagrams, then practise applying it to interview questions.
The concepts behind this question
Use these concept explanations to review the ideas behind this question.
Advanced
Ray on KubernetesRay schedules Python tasks and actors across workers; KubeRay manages those workers on Kubernetes. The two scheduling layers need compatible resource and placement rules.🗂️ Scheduling & Orchestration🔒 Premium
Advanced
Inference Autoscaling and Cold StartsInference autoscaling adds or removes model replicas as demand changes. Queue pressure, latency and startup time help determine how much ready capacity to keep.🚀 Inference & Serving🔒 Premium
Core
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.🗂️ Scheduling & OrchestrationSign in
Foundational
Kubernetes GPU SchedulingKubernetes coordinates GPU discovery, scheduling and container access through several components. Understanding their separate roles makes allocation failures easier to diagnose.🗂️ Scheduling & Orchestration
UP NEXT ON YOUR JOURNEY
Next in this trackOur GPU pods take four minutes to start on a fresh node and sometimes fail with 'no CUDA-capable device'. Walk me through both.Next in this trackWe rent GPUs. When should we buy committed capacity instead of paying on demand, and what do we do with the rest of the demand?Next in this trackOur cluster is 85% allocated but 64-GPU jobs wait for hours. Explain the fragmentation and what a scheduler should do about it.
FEDITOR'S NOTE
Trace a Ray request through logical bundles, worker pods and physical hosts. Explain fractional GPU limits and check whether autoscaling delays actually occur in sequence.
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