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Amazon SageMaker AI adds GPU partitioning for real-time endpoints

Amazon SageMaker AI now supports GPU partitioning via NVIDIA MIG technology to run multiple models on a single accelerator.

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Summary

Amazon SageMaker AI inference now supports GPU partitioning, letting users run multiple models on a single GPU instead of dedicating a full accelerator to each model. Using NVIDIA Multi-Instance GPU technology, SageMaker divides a GPU into up to seven isolated partitions with dedicated memory and compute. Users can configure partitioning when creating an endpoint hosting inference components without changing application code.

What changed

  • Amazon SageMaker AI inference now supports GPU partitioning to run several models on a single GPU.
  • It uses NVIDIA Multi-Instance GPU technology to divide a GPU into as many as seven isolated partitions.
  • Users can configure GPU partitioning when creating an endpoint that hosts inference components.

Availability

Available now

“GPU partitioning is available on P4d, P5, G7, and G7e instances in US East (N. Virginia), US East (Ohio), US West (Oregon), Asia Pacific (Singapore), Asia Pacific (Tokyo), Europe (Frankfurt), and Europe (Ireland).”

Founder takeaway

Consider using GPU partitioning on supported instances if you serve smaller fine-tuned models, embeddings, or classifiers and want to avoid paying for unused GPU capacity.

Source

Researched and drafted with AI from the official sources above, then checked automatically before publishing. Spotted a mistake? Tell us.

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