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MIG, MPS and Time-Slicing

MIG, 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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RELATED CONCEPTS

Kubernetes GPU Scheduling Multi-Tenancy, Quotas and Fair Share GPU Execution Model GPU Memory Hierarchy Inference Autoscaling and Cold Starts Capacity Planning and Utilization

LESSONS THAT TEACH THIS

Fleet and ReliabilityAllocated, active and placeable are different capacity measurements14 min→Foundations of AI InfrastructureDiagnose placement, startup and device health18 min→Fleet and ReliabilitySpecify isolation by resource and validate the end-to-end promise30 min→

PRACTICE THIS IN REAL QUESTIONS

Kubernetes, Slurm & GPU SchedulingMIG, time-slicing and MPS all let several jobs share one GPU. What is the difference, and when would you pick each?→GPU & Accelerator ArchitectureMIG versus MPS: what isolation does each give you when sharing a GPU, and which would you pick for a multi-tenant inference node?→Kubernetes, Slurm & GPU SchedulingDesign a notebook platform for 300 researchers on 64 GPUs. How do you share, reclaim and account for the GPUs?→Kubernetes, Slurm & GPU SchedulingDesign a GPU-aware scheduler that supports fractional GPUs: what isolation does each fraction get, and where does it break?→Kubernetes, Slurm & GPU SchedulingKubernetes device plugins versus Dynamic Resource Allocation: what changes for GPU scheduling, and what would you adopt in 2026?→Kubernetes, Slurm & GPU SchedulingDesign the scheduling and isolation for a multi-tenant fine-tuning service: hundreds of customers, a few base models, shared GPUs.→