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](https://docscontent.nvidia.com/dims4/default/d82c497/2147483647/strip/true/crop/666x267+0+0/resize/666x267!/quality/90/?url=https%3A%2F%2Fk3-prod-nvidia-docs.s3.us-west-2.amazonaws.com%2Fbrightspot%2Fsphinx%2F0000018e-a47d-dedb-a79e-aefd1a130000%2Fai-enterprise%2Fdeployment-guide-bare-metal%2F0.1.0%2F_images%2Fdg-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.