Dedicated GPU
infrastructure for
serious AI.
Corcelium designs, deploys, and operates private NVIDIA clusters for teams training and serving large models — bare-metal performance, a non-blocking fabric, and engineers who run it alongside you.
Built for the realities
of training at scale.
No virtualization tax
Bare-metal access end to end — no hypervisor overhead, no noisy neighbors, no abstraction between your code and the silicon.
One fabric, linear scaling
Rail-optimized InfiniBand and GPUDirect RDMA keep the interconnect off your critical path, so adding nodes adds throughput.
Capacity on demand
Pre-staged clusters provision in seconds. Slurm and Kubernetes ready out of the box, with images and environments you control.
Private & sovereign
Single-tenant isolation, private networking, and region-specific deployments for teams with data-residency requirements.
Engineered storage
High-throughput parallel filesystems plus local NVMe feed thousands of GPUs without leaving them waiting on I/O.
Engineers, not tickets
A team that has run large clusters before — reachable in minutes, helping you tune the run, not closing a support queue.
From request to first
training step in days.
Tell us your workload
Share your model size, framework, scale, and timeline. An engineer reviews it — no generic sales funnel.
We architect the cluster
We size GPUs, fabric, and storage to your job, then stage a dedicated, bare-metal cluster wired for your workload.
You scale, we operate
Spin up via API, Slurm, or Kubernetes. We run the infrastructure and stay on call while you focus on the model.
From a single node
to a full cluster.
Corcelium clusters are wired as one coherent fabric. Rail-optimized InfiniBand, GPUDirect RDMA, and parallel storage mean scaling out is a configuration change — not a re-architecture.
Hyperscaler power.
Without the hyperscaler tax.
Built for everything
- Virtualized instances with hypervisor overhead
- Shared tenancy and unpredictable neighbors
- Egress fees and opaque bundled pricing
- Support tickets and tiered response times
- Best-effort networking between nodes
Built for AI at scale
- Bare-metal GPUs with zero virtualization tax
- Dedicated, single-tenant, isolated clusters
- Straightforward commitments, no egress traps
- Direct line to engineers who run clusters
- Non-blocking InfiniBand engineered for training
The latest NVIDIA accelerators.
Things teams ask us.
NVIDIA L40S, H100, H200, and Blackwell B200 — from a single node to large multi-node clusters wired on a non-blocking InfiniBand fabric. Tell us your target scale and we'll architect it.
Pre-staged capacity provisions in seconds; dedicated reserved clusters are typically stood up in days, depending on configuration and scale. We'll give you a concrete timeline when we scope the workload.
Bare-metal. You get direct access to every GPU, NIC, and NVLink lane — no hypervisor overhead and no noisy neighbors sharing your hardware.
Managed Slurm and Kubernetes out of the box, with a REST API and Terraform for infrastructure-as-code. Bring your own containers, or start from our CUDA, PyTorch, and JAX images.
Yes. We offer single-tenant isolation, private networking, and region-specific deployments, with a roadmap toward owned, region-specific data centers for full data-residency control.
A 24/7 NOC plus solutions engineers who understand distributed training — reachable in minutes, helping you tune the run rather than closing a ticket queue.
Tell us what you're
building.
Share your model size, timeline, and the scale you need. We'll architect the cluster and follow up directly.