GPU clusters built for Slurm.
Slurm remains the default scheduler for serious training and HPC, and it makes assumptions about the machine underneath it. Buying hardware without those assumptions in mind produces a cluster that works but underdelivers.
What decides this requirement.
Homogeneity and a fast fabric.
Slurm allocates whole nodes or defined resources within them, and scheduling is far cleaner when nodes within a partition are identical. Mixed node types inside one partition create allocation inefficiency and operational irritation that compounds as the cluster grows.
That matters at purchase time. Buying twenty nodes now and twenty slightly different nodes next year, then putting them in one partition, is a decision people regret. We plan the partition structure alongside the procurement rather than after it.
Shared storage that every node can reach at speed is the other assumption. Training jobs read datasets and write checkpoints from wherever they land, so a storage layer sized for a single node's throughput will disappoint immediately.
The hardware follows the topology.
We size the fabric against how jobs will actually be distributed, because a Slurm partition spanning nodes with poor interconnect delivers far less than its GPU count implies. The reasoning is on the networking desk.
Node configuration follows too: CPU and memory ratios that suit your job mix, local scratch where the pipeline needs it, and consistency across the partition so allocation stays clean.
Bring the job profile and we will size the cluster for it. For research and public-sector buyers the procurement discipline on the HPC page applies alongside.
Start from what is verified.
Current verified lines with quantities, lead times and indicative pricing are public on the live inventory. Anything not listed becomes a sourcing requirement with a first pass inside one business day.
Asked first, answered straight.
It runs on most things, but it schedules far better on homogeneous nodes with a fast interconnect and shared storage. Hardware bought without those assumptions works while delivering less than its specification suggests.
You can, and it is usually better to separate them into different partitions rather than mixing within one. Mixed nodes inside a partition create allocation inefficiency that grows with the cluster.
Shared, and fast enough to feed every node simultaneously rather than one at a time. Under-sized storage is a common cause of clusters underperforming their GPU count.
We source the hardware, fabric, storage and facility. Scheduler deployment and tuning is typically your team or an integrator, and we size the machine so their job is straightforward.
Working through this on a real requirement?
Twenty minutes with the desk, no pitch and no quote at the end of it. Tell us roughly what you need and we will come back within one business day.
Sent. We are on it.
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