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GPU sharing / MIG for inference
Partition GPUs (MIG or time-slicing) so multiple inference workloads share accelerators safely.
AssessFinOpsNew
- Why this ring
- Assess after attribution exists - sharing without quotas creates noisy-neighbor outages.
- Production risk if ignored
- Contention spikes P99 latency and causes cascading inference timeouts.
- Typical effort
- months
- High FinOps impact
Use cases
- Multi-model GPU pool
- Cost reduction on idle GPUs
Adoption steps
- Baseline utilization
- Pilot MIG on non-critical
- Enforce quotas
- Watch P99 vs cost
Related tools
In your assessment
GPU packing efficiency + SLO impact review