Dividing NVIDIA A30 GPUs and Conquering Multiple Workloads – NVIDIA Technical Blog News and tutorials for developers, data scientists, and IT admins 2025-07-25T18:15:10Z http://www.open-lab.net/blog/feed/ Maggie Zhang <![CDATA[Dividing NVIDIA A30 GPUs and Conquering Multiple Workloads]]> http://www.open-lab.net/blog/?p=50380 2023-04-04T16:58:51Z 2022-08-30T19:00:35Z Multi-Instance GPU (MIG) is an important feature of NVIDIA H100, A100, and A30 Tensor Core GPUs, as it can partition a GPU into multiple instances. Each...]]> Multi-Instance GPU (MIG) is an important feature of NVIDIA H100, A100, and A30 Tensor Core GPUs, as it can partition a GPU into multiple instances. Each...

Multi-Instance GPU (MIG) is an important feature of NVIDIA H100, A100, and A30 Tensor Core GPUs, as it can partition a GPU into multiple instances. Each instance has its own compute cores, high-bandwidth memory, L2 cache, DRAM bandwidth, and media engines such as decoders. This enables multiple workloads or multiple users to run workloads simultaneously on one GPU to maximize the GPU��

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