Available Resources

Compute Nodes

Your jobs will land on appropriately sized nodes automatically based on your CPU to memory ratio. For example in the genoa partition:

A job which requests ≤ 2 GB/core will run on the 44 Genoa nodes which have 2 GB/core, or if those are full, the 4 GB/core nodes.
A job which requests ≤ 4 GB/core will run on the 4 Genoa nodes which have 4 GB/core, or if those are full, the 8 GB/core nodes.
A job which requests > 4 GB/core will run on the 16 Genoa nodes which have 8 GB/core.

Architecture	Cores	Memory	GPGPU	Nodes
2 x AMD Milan 7713 CPU └ 8 x Chiplets     └ 8 x Cores	126	512GB (2GB / Core)	-	48
			1 x NVIDIA A100	4
			2 x NVIDIA A100	2
			4 x NVIDIA HGX A100	4
		1024GB (4GB / Core)	-	8
2 x AMD Genoa 9634 CPU └ 12 x Chiplets     └ 7 x Cores	166	358GB (1GB / Core)	-	28
			2 x NVIDIA H100	4
			4 x NVIDIA L4	4
		1024B (2GB / Core)	-	4
		1432GB (4GB / Core)	-	16

GPGPUs

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), or Virtual Machines (VMs).

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.

If you have any questions about GPUs on NeSI or the status of anything listed in the table, Contact our Support Team.

Architecture	Purpose/Note	VRAM		SLURM
NVIDIA A100 PCIe cards	Machine Learning (ML) applications	40GB	--gpus-p A100:1--gpus-per-node A100:1	4
			Milan, 2 x A100	--gpus-per-node A100:2	2
NVIDIA HGX A100	Large-scale Machine Learning applications	80GB	Milan, 4 x HGX A100	--gpus-per-node A100:4	4
NVIDIA H100	Large-scale Machine Learning (ML) applications	96GB	Genoa, 2 x H100	--gpus-per-node H100:2	8
NVIDIA L4	no fp64 double precision		Genoa, 4 x L4	--gpus-per-node L4:4	16
NVIDIA A40	Teaching / training	48GB	Flexible HPC	Not accessable by Slurm	4