We build and operate the heavy-metal GPU infrastructure that AI runs on.
Bare-metal NVIDIA DGX, engineered
NVIDIA DGX systems are the reference platform for AI compute — but they are only as good as the infrastructure around them. We handle:
- Deployment & lifecycle: OS, drivers, firmware, and BMC configured for AI workloads from day one.
- GPU Operators on Kubernetes: NVIDIA GPU Operator for drivers, container runtime, device plugin, DCGM exporter, and MIG management.
- High-throughput storage: Parallel filesystems (WEKA, VAST, BeeGFS, Lustre) and NVMe-oF caching tuned for distributed training checkpointing and inference KV-cache.
- Networking: RoCE v2 / InfiniBand fabrics, congestion control, and adaptive routing for multi-node training at scale.
- Observability: GPU utilisation, memory, thermals, NVLink/NVSwitch topology, and queue depth via the LGTM stack (Loki, Grafana, Tempo, Mimir) + DCGM dashboards.
Why bare-metal for AI?
- Performance: No hypervisor overhead; full GPU memory bandwidth and NVLink interconnect.
- Cost: At sustained utilisation (>40%), owned GPUs beat cloud instances on TCO within 12-18 months.
- Sovereignty: Data and model weights stay in your datacenter — GDPR, EU AI Act, and Swedish regulatory compliance.
- Determinism: No noisy neighbors; predictable tail latency for inference SLAs.
Day-2 operations we provide
- Capacity planning: Workload profiling, GPU utilisation trending, and procurement forecasting.
- Firmware & driver lifecycle: Tested upgrade paths, rollback procedures, and compatibility matrices.
- Failure domain design: Rack-level power, cooling, and network redundancy for DGX SuperPOD topologies.
- Security hardening: Immutable OS images, signed firmware, TPM-backed attestation, and CIS benchmark compliance.
Considering DGX or GPU infrastructure?
We advise on procurement, sizing, and operations — from single DGX to SuperPOD. Talk to an engineer.