The AI Lab
Liquid-cooled NVIDIA H100 clusters and agentic AIOps, engineered for the two hardest problems in modern infrastructure: training at scale and keeping production self-healing.
An NVIDIA H100 cluster built for scale
Dense, liquid-cooled GPU nodes wired with a non-blocking InfiniBand fabric — the substrate for both customer AI workloads and our own AIOps models.
80 GB HBM3 each — 640 GB pooled GPU memory per node.
All-to-all GPU bandwidth for tensor-parallel training.
Non-blocking RoCEv2 fabric across the cluster spine.
Host compute for data pipelines and orchestration.
Efficiency measured, not marketed
Power Usage Effectiveness is the ratio of total facility energy to the energy that actually reaches IT equipment. A perfect 1.0 means every watt does useful work; the industry average sits near 1.5.
At 1.12 PUE, ~89% of all energy drawn reaches compute — versus ~67% at the industry-average 1.5.
The agentic remediation loop
When telemetry signals trouble, an agent works the problem end to end — detecting, healing, and verifying — with human-in-the-loop guardrails at every step.
- Step 1Log Analysis
- Step 2Anomaly Detection
- Step 3Self-Healing Script
- Step 4Node Replication
- Step 5Session Transfer
- Step 6Debug
- Step 7Resolution
Technical specifications
| Domain | Specification | Key metric |
|---|---|---|
| Fluid cooling | Direct-to-chip liquid cooling, 40°C coolant, rear-door heat exchangers | 1.12 PUE |
| Network routing | 400G InfiniBand NDR spine-leaf, RoCEv2, non-blocking topology | < 2 µs hop |
| Storage architecture | NVMe-oF all-flash parallel filesystem (WEKA), GPUDirect Storage | 2 TB/s read |
Pilot AIOps on a slice of your estate
We'll connect to a non-production environment and show measurable noise reduction within weeks.