Compute Hardware Overview - Revision history

Honfi001 at 07:40, 21 July 2026

2026-07-21T07:40:33Z

← Older revision		Revision as of 07:40, 21 July 2026
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	!GPU Maker		!GPU Maker
	!GPU Type		!GPU Type
	!~~Number of~~ GPUs		!GPUs per Node
	!VRAM (GB)		!VRAM (GB)
	!VRAM Type		!VRAM Type

Honfi001 at 13:23, 20 July 2026

2026-07-20T13:23:46Z

Show changes

Haars001: Updated specs

2026-07-09T12:24:59Z

Updated specs

← Older revision		Revision as of 12:24, 9 July 2026
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	<!-- TODO: the per-node specifications below (core counts, memory, CPU/GPU models, number of nodes) need to be confirmed against the current cluster and filled in. The structure is in place; the exact figures are placeholders marked TODO. -->

	Anunna's compute nodes are grouped into [[Partitions / Queues \| partitions]] by the kind of hardware they provide. This page summarises what each node type offers; for how to request a particular node or feature in a job, see [[Choosing a node (constraints)]].		Anunna's compute nodes are grouped into [[Partitions / Queues \| partitions]] by the kind of hardware they provide. This page summarises what each node type offers; for how to request a particular node or feature in a job, see [[Choosing a node (constraints)]].

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	Standard compute nodes for jobs that do not need a GPU. These make up the <code>main</code> partition and handle the bulk of the cluster's work.		Standard compute nodes for jobs that do not need a GPU. These make up the <code>main</code> partition and handle the bulk of the cluster's work.

	<!-- TODO: confirm and fill in — number of CPU nodes, cores per node, memory per node, CPU model. The cluster is heterogeneous, so there may be more than one CPU node type (e.g. standard and high-memory "fat" nodes). -->
	We have 3 generations in use right now:		We have 3 generations in use right now:

	* gen2 : 29 nodes with Intel CPUs, 32 cores and 375G RAM. 2 nodes with Intel CPUs, 64 cores and 1.5T RAM		* gen2 : 29 nodes with Intel CPUs, 32 cores and 375G RAM. 2 nodes with Intel CPUs, 64 cores and 1.5T RAM
	* gen3: 73 nodes with AMD CPUs, 128 cores and 1T RAM. 2 nodes with AMD CPUs, 96 cores and 4T RAM		* gen3: 73 nodes with AMD CPUs, 128 cores and 1T RAM. 2 nodes with AMD CPUs, 96 cores and 4T RAM
	* gen4: 3 nodes with AMD CPUSs, 192 cores and 2.2T RAM		* gen4: 3 nodes with AMD CPUSs, 192 cores and 2.25T RAM

	== GPU nodes ==		== GPU nodes ==

	Nodes equipped with GPUs, for accelerated workloads such as deep learning. Request a GPU with <code>--gres=gpu:<n></code> and the appropriate partition (see [[Choosing a node (constraints)]]).		Nodes equipped with GPUs, for accelerated workloads such as deep learning. Request a GPU with <code>--gres=gpu:<n></code> and the appropriate partition (see [[Choosing a node (constraints)]]).

			These also come in different flavours:

	=== NVIDIA GPUs ===		=== NVIDIA GPUs ===
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	Available in the <code>gpu</code> partition. The cluster has several NVIDIA GPU models — A100, A6000, and V100. The scheduler hands out A100s first, then A6000s, then V100s; the price per GPU-hour is the same for all of them. As a rough guide, the A100 (80 GB) is about twice as fast as the A6000 (48 GB) or V100 (16 GB), depending on whether your workload can use the extra memory. Constrain a job to a specific model with, for example, <code>--constraint='nvidia&A100'</code>.		Available in the <code>gpu</code> partition. The cluster has several NVIDIA GPU models — A100, A6000, and V100. The scheduler hands out A100s first, then A6000s, then V100s; the price per GPU-hour is the same for all of them. As a rough guide, the A100 (80 GB) is about twice as fast as the A6000 (48 GB) or V100 (16 GB), depending on whether your workload can use the extra memory. Constrain a job to a specific model with, for example, <code>--constraint='nvidia&A100'</code>.

	~~<!-- TODO~~: ~~confirm number of NVIDIA GPU nodes~~, GPUs ~~per node~~, ~~and host CPU/memory per node~~. ~~-->~~		* gpu100: 32 cores, 375G RAM, 4 Nvidia V100 GPUs with 16G VRAM
			* gpun20{0,1,2,3): 32 cores, 500G RAM, each has 4 Nvidia A100 with 80G VRAM
			* gpuxn200: 64 cores, 500G RAM, 2 Nvidia RTX A6000 with 48G VRAM
			* node301: 192 cores, 2.25T RAM, 4 Nvidia L40S with 46G VRAM

	=== AMD GPUs ===		=== AMD GPUs ===
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	Available in the <code>gpu_amd</code> partition. Requested the same way as NVIDIA GPUs, using the <code>gpu_amd</code> partition.		Available in the <code>gpu_amd</code> partition. Requested the same way as NVIDIA GPUs, using the <code>gpu_amd</code> partition.

	~~<!-- TODO~~: ~~confirm number of AMD GPU nodes~~, ~~GPU model~~, ~~GPUs per node, and host CPU~~/~~memory per node. --~~>		* gpua201: 32 cores, 375G RAM, 4 AMD MI210 with 64G VRAM<br />

	== See also ==		== See also ==

Haars001 at 12:14, 9 July 2026

2026-07-09T12:14:07Z

← Older revision		Revision as of 12:14, 9 July 2026
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	<!-- TODO: confirm and fill in — number of CPU nodes, cores per node, memory per node, CPU model. The cluster is heterogeneous, so there may be more than one CPU node type (e.g. standard and high-memory "fat" nodes). -->		<!-- TODO: confirm and fill in — number of CPU nodes, cores per node, memory per node, CPU model. The cluster is heterogeneous, so there may be more than one CPU node type (e.g. standard and high-memory "fat" nodes). -->
			We have 3 generations in use right now:

			* gen2 : 29 nodes with Intel CPUs, 32 cores and 375G RAM. 2 nodes with Intel CPUs, 64 cores and 1.5T RAM
			* gen3: 73 nodes with AMD CPUs, 128 cores and 1T RAM. 2 nodes with AMD CPUs, 96 cores and 4T RAM
			* gen4: 3 nodes with AMD CPUSs, 192 cores and 2.2T RAM

	== GPU nodes ==		== GPU nodes ==

Haars001: Phase 1 § 1 P1.1.3 / Phase 2 P2.1: new page — compute node types by partition, with TODO markers for current per-node specs (via create-page on MediaWiki MCP Server)

2026-06-18T10:17:21Z

Phase 1 § 1 P1.1.3 / Phase 2 P2.1: new page — compute node types by partition, with TODO markers for current per-node specs (via create-page on MediaWiki MCP Server)

New page

Anunna's compute nodes are grouped into [[Partitions / Queues | partitions]] by the kind of hardware they provide. This page summarises what each node type offers; for how to request a particular node or feature in a job, see [[Choosing a node (constraints)]].

== CPU nodes ==

Standard compute nodes for jobs that do not need a GPU. These make up the <code>main</code> partition and handle the bulk of the cluster's work.



== GPU nodes ==

Nodes equipped with GPUs, for accelerated workloads such as deep learning. Request a GPU with <code>--gres=gpu:<n></code> and the appropriate partition (see [[Choosing a node (constraints)]]).

=== NVIDIA GPUs ===

Available in the <code>gpu</code> partition. The cluster has several NVIDIA GPU models — A100, A6000, and V100. The scheduler hands out A100s first, then A6000s, then V100s; the price per GPU-hour is the same for all of them. As a rough guide, the A100 (80 GB) is about twice as fast as the A6000 (48 GB) or V100 (16 GB), depending on whether your workload can use the extra memory. Constrain a job to a specific model with, for example, <code>--constraint='nvidia&A100'</code>.



=== AMD GPUs ===

Available in the <code>gpu_amd</code> partition. Requested the same way as NVIDIA GPUs, using the <code>gpu_amd</code> partition.



== See also ==

* [[Cluster Architecture Overview]]
* [[Choosing a node (constraints)]]
* [[Partitions / Queues]]