https://wiki.anunna.wur.nl/index.php?action=history&feed=atom&title=Compute_Hardware_Overview 2026-06-20T00:36:02Z Revision history for this page on the wiki MediaWiki 1.45.3 https://wiki.anunna.wur.nl/index.php?title=Compute_Hardware_Overview&diff=2792&oldid=prev 2026-06-18T10:17:21Z

<p>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)</p> <p><b>New page</b></p><div>&lt;!-- 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. --&gt;<br /> <br /> Anunna&#039;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)]].<br /> <br /> == CPU nodes ==<br /> <br /> Standard compute nodes for jobs that do not need a GPU. These make up the &lt;code&gt;main&lt;/code&gt; partition and handle the bulk of the cluster&#039;s work.<br /> <br /> &lt;!-- 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 &quot;fat&quot; nodes). --&gt;<br /> <br /> == GPU nodes ==<br /> <br /> Nodes equipped with GPUs, for accelerated workloads such as deep learning. Request a GPU with &lt;code&gt;--gres=gpu:&lt;n&gt;&lt;/code&gt; and the appropriate partition (see [[Choosing a node (constraints)]]).<br /> <br /> === NVIDIA GPUs ===<br /> <br /> Available in the &lt;code&gt;gpu&lt;/code&gt; 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, &lt;code&gt;--constraint=&#039;nvidia&amp;A100&#039;&lt;/code&gt;.<br /> <br /> &lt;!-- TODO: confirm number of NVIDIA GPU nodes, GPUs per node, and host CPU/memory per node. --&gt;<br /> <br /> === AMD GPUs ===<br /> <br /> Available in the &lt;code&gt;gpu_amd&lt;/code&gt; partition. Requested the same way as NVIDIA GPUs, using the &lt;code&gt;gpu_amd&lt;/code&gt; partition.<br /> <br /> &lt;!-- TODO: confirm number of AMD GPU nodes, GPU model, GPUs per node, and host CPU/memory per node. --&gt;<br /> <br /> == See also ==<br /> <br /> * [[Cluster Architecture Overview]]<br /> * [[Choosing a node (constraints)]]<br /> * [[Partitions / Queues]]</div>

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