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created_at: '2022-06-13T04:54:38Z' | ||
hidden: false | ||
position: 6 | ||
tags: [] | ||
tags: | | ||
gpu | ||
title: Available GPUs on NeSI | ||
vote_count: 2 | ||
vote_sum: 2 | ||
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NeSI has a range of Graphical Processing Units (GPUs) to accelerate compute-intensive research and support more analysis at scale. | ||
Depending on the type of GPU, you can access them in different ways, such as via batch scheduler (Slurm), interactively (using [Jupyter on | ||
NeSI](../Interactive_computing_using_Jupyter/Jupyter_on_NeSI.md)), | ||
or Virtual Machines (VMs). | ||
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!!! warning | ||
This page has been automatically migrated and may contain formatting errors. | ||
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The table below outlines the different types of GPUs, | ||
who can access them and how, and whether they are currently available or on the future roadmap. | ||
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NeSI has a range of Graphical Processing Units (GPUs) to accelerate | ||
compute-intensive research and support more analysis at scale. Depending | ||
on the type of GPU, you can access them in different ways, such as via | ||
batch scheduler (Slurm), interactively (using [Jupyter on | ||
NeSI](../../Scientific_Computing/Interactive_computing_using_Jupyter/Jupyter_on_NeSI.md)), | ||
or Virtual Machines (VMs). | ||
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||
The table below outlines the different types of GPUs, who can access | ||
them and how, and whether they are currently available or on the future | ||
roadmap. | ||
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||
If you have any questions about GPUs on NeSI or the status of anything | ||
listed in the table, [contact | ||
Support](https://support.nesi.org.nz/hc/en-gb/requests/new). | ||
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||
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If you have any questions about GPUs on NeSI or the status of anything listed in the table, [contact | ||
Support](mailto:[email protected]). | ||
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| GPGPU | Purpose | Location | Access mode | Who can access | Status | | ||
|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------|------------------------------------------------|----------------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------------------|----------------------------------------------------------------|--------------------------------------------------------------------------------------------------------------| | ||
| 9 NVIDIA Tesla P100 PCIe 12GB cards (1 node with 1 GPU, 4 nodes with 2 GPUs) | | [Mahuika](../../Scientific_Computing/The_NeSI_High_Performance_Computers/Mahuika.md) | Slurm and [Jupyter](../../Scientific_Computing/Interactive_computing_using_Jupyter/Jupyter_on_NeSI.md) | NeSI users | Currently available | | ||
| 7 NVIDIA A100 PCIe 40GB cards (4 nodes with 1 GPU, 2 nodes with 2 GPUs) | Machine Learning (ML) applications | [Mahuika](../../Scientific_Computing/The_NeSI_High_Performance_Computers/Mahuika.md) | Slurm | NeSI users | Currently available | | ||
| 7 A100-1g.5gb instances (1 NVIDIA A100 PCIe 40GB card divided into [7 MIG GPU slices](https://www.nvidia.com/en-us/technologies/multi-instance-gpu/) with 5GB memory each) | Development and debugging | [Mahuika](../../Scientific_Computing/The_NeSI_High_Performance_Computers/Mahuika.md) | Slurm and [Jupyter](../../Scientific_Computing/Interactive_computing_using_Jupyter/Jupyter_on_NeSI.md) | NeSI users | Currently available | | ||
| 5 NVIDIA Tesla P100 PCIe 12GB (5 nodes with 1 GPU) | Post-processing | [Māui Ancil](https://support.nesi.org.nz/hc/en-gb/articles/360000203776-M%C4%81ui-Ancillary-Nodes) | Slurm | NeSI users | Currently available | | ||
| 4 NVIDIA HGX A100 (4 GPUs per board with 80GB memory each, 16 A100 GPUs in total) | Large-scale Machine Learning (ML) applications | [Mahuika](../../Scientific_Computing/The_NeSI_High_Performance_Computers/Mahuika.md) | Slurm | NeSI users | Available as part of the [Milan Compute Nodes](https://support.nesi.org.nz/knowledge/articles/6367209795471) | | ||
| 4 NVIDIA A40 with 48GB memory each (2 nodes with 2 GPUs, but capacity for 6 additional GPUs already in place) | Teaching / training | Flexible HPC | [Jupyter](../../Scientific_Computing/Interactive_computing_using_Jupyter/Jupyter_on_NeSI.md), VM, or bare metal tenancy possible (flexible) | Open to conversations with groups who could benefit from these | In development. | | ||
| 9 NVIDIA Tesla P100 PCIe 12GB cards (1 node with 1 GPU, 4 nodes with 2 GPUs) | | [Mahuika](../The_NeSI_High_Performance_Computers/Mahuika.md) | Slurm and [Jupyter](../Interactive_computing_using_Jupyter/Jupyter_on_NeSI.md) | NeSI users | Currently available | | ||
| 7 NVIDIA A100 PCIe 40GB cards (4 nodes with 1 GPU, 2 nodes with 2 GPUs) | Machine Learning (ML) applications | [Mahuika](../The_NeSI_High_Performance_Computers/Mahuika.md) | Slurm | NeSI users | Currently available | | ||
| 7 A100-1g.5gb instances (1 NVIDIA A100 PCIe 40GB card divided into [7 MIG GPU slices](https://www.nvidia.com/en-us/technologies/multi-instance-gpu/) with 5GB memory each) | Development and debugging | [Mahuika](Mahuika.md) | Slurm and [Jupyter](../Interactive_computing_using_Jupyter/Jupyter_on_NeSI.md) | NeSI users | Currently available | | ||
| 5 NVIDIA Tesla P100 PCIe 12GB (5 nodes with 1 GPU) | Post-processing | [Māui Ancil](Maui_Ancillary.md) | Slurm | NeSI users | Currently available | | ||
| 4 NVIDIA HGX A100 (4 GPUs per board with 80GB memory each, 16 A100 GPUs in total) | Large-scale Machine Learning (ML) applications | [Mahuika](Mahuika.md) | Slurm | NeSI users | Available as part of the [Milan Compute Nodes](https://support.nesi.org.nz/knowledge/articles/6367209795471) | | ||
| 4 NVIDIA A40 with 48GB memory each (2 nodes with 2 GPUs, but capacity for 6 additional GPUs already in place) | Teaching / training | Flexible HPC | [Jupyter](../Interactive_computing_using_Jupyter/Jupyter_on_NeSI.md), VM, or bare metal tenancy possible (flexible) | Open to conversations with groups who could benefit from these | In development. | |
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