SageMaker Inference now supports Multi-Instance GPU to maximize GPU utilization for model endpoints
TL;DR
Amazon SageMaker Inference now supports GPU partitioning with NVIDIA Multi-Instance GPU (MIG), which divides one GPU into hardware-isolated partitions so you can run a different model in each or take a right-sized fraction of a newer accelerator. This post shows how two optional API fields place one model per partition, and what we measured against a whole older GPU.
Nauti's Take
For teams running several small or unevenly sized models, MIG is worth testing as a way to improve GPU utilization. First compare per-model latency, throughput, and cost with a full GPU, then repeat the test under real concurrency.
Hardware isolation can reduce interference, but it does not remove the need to measure burst traffic before changing endpoint architecture.
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