Racks Powered on Are Not a Cluster. We Make the GPUs You Bought Behave Like a Cloud.
Burn-in, fabric tuning, GPUDirect storage, scheduler and observability stack — the software and validation work that turns installed hardware into a platform your team operates with confidence from day one.
Who This Is For
- Teams taking delivery of a new cluster who want it validated before the first real job
- Organisations whose GPUs are installed but under-performing on multi-node training
- Platform teams that want Kubernetes, Slurm or both set up the way the large operators run them
- Anyone who has rented from a neocloud and wants the same operational experience on their own floor
What's Included
Burn-In and Acceptance
GPU, memory, NVLink and node stress testing; infant-mortality screening; serial-level acceptance records.
Fabric Validation
InfiniBand or Spectrum-X configuration, adaptive routing, congestion control, NCCL all-reduce benchmarks across the full cluster.
Storage Integration
Parallel filesystem mount and tuning, GPUDirect Storage, checkpoint throughput tests against your model sizes.
Scheduler and Orchestration
Kubernetes with GPU operator and network operator, Slurm with topology-aware scheduling, or both side by side.
Observability
NVIDIA Mission Control, DCGM, Prometheus and Grafana wired to node, fabric and facility telemetry with alerting.
Operator Enablement
Runbooks, failure-mode playbooks, and training sessions so your team runs the platform without us.
Reference Specifications
Starting points. Every engagement is engineered to the workload, site and budget in front of us.
| Platforms | NVIDIA GB300 NVL72, HGX B300/B200, H200; AMD MI355X with ROCm |
|---|---|
| Fabric | Quantum-X800 InfiniBand, Spectrum-X Ethernet; NCCL and RCCL tuning; rail-optimised topologies |
| Storage | VAST, WEKA, DDN; GPUDirect Storage; NVMe-oF |
| Orchestration | Kubernetes (GPU Operator, Network Operator, Run:ai), Slurm, NVIDIA Base Command Manager, Mission Control |
| Observability | DCGM exporter, Prometheus, Grafana, Loki; integration to DCIM and BMS |
| Validation | NCCL tests, MLPerf-style training runs, HPL, storage throughput, failure injection |
How We Deliver
- 1
Burn-In
Weeks 1Node-level stress testing and acceptance; faulty components identified and RMA'd.
- 2
Fabric and Storage
Weeks 1–2Interconnect configuration and benchmarking, filesystem tuning, GPUDirect validation.
- 3
Platform
Weeks 2–3Scheduler, orchestration and observability deployed and integrated; reference jobs run end to end.
- 4
Handover
Weeks 3–4Runbooks, training, acceptance test against agreed performance criteria.
Questions We Get Asked
Our cluster is already installed by someone else. Can you still help?
Yes. Fabric and storage tuning on existing clusters is a common engagement; most under-performing multi-node training traces back to interconnect configuration or storage bottlenecks we can measure and fix.
Kubernetes or Slurm?
Research and training teams usually want Slurm; product and inference teams want Kubernetes. Many clusters run both, with Slurm for training partitions and Kubernetes for inference and services. We set up whichever matches how your teams actually work.
What does acceptance look like?
Agreed, measurable criteria: NCCL bandwidth per node pair, a reference training run at target throughput, storage checkpoint time, and zero open hardware faults.
Request a Quotation
Pre-tagged as Cluster bring-up & software. A solutions engineer responds the same business day.