Bare Metal Deployment Guide
Bare Metal Deployment Guide (0.1.0)

Advanced GPU configuration (Optional)

NVIDIA AI Enterprise 2.0 or later

Compute workloads can benefit from using separate GPU partitions. The flexibility of GPU partitioning allows a single GPU to be shared and used by small, medium, and large-sized workloads. GPU partitions can be a valid option for executing Deep Learning workloads. An example is Deep Learning training and inferencing workflows, which utilize smaller datasets but are highly dependent on the size of the data/model, and users may need to decrease batch sizes.

The following graphic illustrates a GPU partitioning use case where multi-tenant, multiple users are sharing a single A100 (40GB). In this use case, a single A100 can be used for multiple workloads such as Deep Learning training, fine-tuning, inference, Jupiter Notebook, debugging, etc.

dg-gpu-part-01.png

GPUs are partitioned using Multi-Instance GPU (MIG) spatial partitioning. More details on MIG can be found in the NVIDIA Multi-Instance GPU User Guide.

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