MIG vs time-slicing: partitioning GPUs for inference
When to slice an A100 into seven, and when not to.
CR
Carlos Ruiz
Jun 26, 2026·1 min read·225
A single A100 is overkill for a 1B parameter model. The question is how to share it safely between workloads.
Multi-Instance GPU (MIG)
MIG gives you hardware-isolated partitions with dedicated memory and compute. Predictable, but rigid — profiles are fixed sizes.
Time-slicing
Time-slicing oversubscribes a GPU by context-switching between pods. Higher density, zero isolation. One noisy neighbor can wreck your p99.
Hard multi-tenant boundary? Use MIG.
Bursty internal workloads that tolerate jitter? Time-slice.
Latency-critical single tenant? Don't partition at all.
nvidia-smi mig -cgi 19,19,19 -C # three 1g.10gb instancesCR
Written by
Carlos RuizGPU performance nerd
Squeezing every last token/sec out of accelerators. CUDA, MIG, and kernels.
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