GPU infrastructure for Ray workloads.
Ray has become a common layer for distributed Python and reinforcement learning workloads, and it tolerates heterogeneity better than most schedulers. That changes what you should buy.
What decides this requirement.
The CPU matters more here.
Ray workloads frequently mix CPU-heavy stages with GPU-heavy ones inside the same pipeline. Data preparation, environment simulation in reinforcement learning and orchestration logic all consume CPU, and a node configured to maximise accelerators while starving the host processor will bottleneck in exactly those stages.
That is a different node specification from a pure training cluster. We size CPU cores, host memory and local storage against the actual pipeline rather than defaulting to the GPU-maximised shape that suits large-scale training.
Ray also tolerates heterogeneous resources better than Slurm, which means a mixed fleet is more viable here. That flexibility can be worth real money when growing a cluster over time.
Specify the whole node.
We ask about the pipeline shape before the accelerator: which stages are CPU-bound, how much host memory the data path needs, whether local scratch matters. Those answers change the node configuration significantly.
Where the workload spans CPU-only and GPU nodes, we source both rather than forcing everything onto accelerated hardware, which is a common and expensive default.
The wider configuration logic is on the cluster sizing page, and hardware options on the hardware sourcing desk.
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.
Not special, but differently balanced. Ray pipelines are frequently CPU-bound in parts, so nodes that maximise accelerators while starving the host processor bottleneck in those stages.
More comfortably than with Slurm, yes. Ray tolerates heterogeneous resources well, which makes growing a cluster incrementally more viable.
Often not. Where parts of the pipeline are CPU-only, dedicated CPU nodes are far cheaper than running that work on accelerated hardware.
Yes. CPU nodes, GPU nodes, storage and networking come through the same desk, specified against the pipeline rather than a template.
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.
Your enquiry has landed with the desk. Acknowledged within one hour.