GPU infrastructure under Kubernetes.
Kubernetes has become the default control plane for AI platforms, particularly where capacity is shared across teams or sold to customers. It runs best on bare metal it fully controls.
What decides this requirement.
Orchestration wants the whole node.
Kubernetes with GPU device plugins schedules accelerators as resources, and it does that most cleanly when it controls the full node rather than sitting inside someone else's virtualisation layer. Nested abstraction adds overhead and removes visibility exactly where you need it.
For neoclouds and platforms selling capacity onward, that control is not optional. Partitioning, isolation and accounting all depend on owning the layer beneath the orchestrator.
This is why so many AI platform businesses run Kubernetes on reserved bare metal rather than on virtualised cloud instances, and why we source the metal accordingly.
Predictability beats maximum specification.
Kubernetes schedules better against predictable node shapes. Consistent GPU count, memory and local storage per node makes bin-packing efficient; a fleet of one-off configurations does not.
Storage and networking underneath deserve the same attention as the compute. Persistent volumes that cannot keep up with the pods scheduled against them are a common and frustrating bottleneck.
We source the whole layer beneath your control plane: nodes, fabric, storage and facility. For platforms reselling capacity, the commercial structures are on the neocloud 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.
It works virtualised, and it works considerably better with full node control. Nested abstraction adds overhead and removes the visibility that scheduling and accounting depend on.
Yes, and that is one of the strongest reasons to run Kubernetes over GPU infrastructure. The isolation and accounting depend on controlling the layer beneath the orchestrator.
Consistent ones. Predictable GPU count, memory and local storage per node makes scheduling efficient. A fleet of one-off configurations schedules badly regardless of total capacity.
We source and land the infrastructure beneath it. Cluster deployment is typically your team or an integrator, and we make sure the hardware suits what they are building.
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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