QubitPage Technical Dossier
QubitPage Foundation
QubitPage Foundation — the GPU cloud platform powering SENTINEL AI training, fleet management, and investor datacenter credits. BYOK AI, VS Code integration, CLI/API, and NVIDIA DGX A100 infrastructure.
PlatformQubitPage®
GPU FleetDGX A100
Credit Ratio1:1 EUR
AccessInvestor Priority
1 — What is QubitPage Foundation?
QubitPage Foundation is the cloud control plane and GPU compute infrastructure that powers SENTINEL's AI training pipeline, fleet OTA updates, and investor-facing datacenter services. It is both the operational backbone of SENTINEL manufacturing and a standalone revenue-generating platform.
2 — Architecture
- BYOK AI: Bring your own model — deploy, train, and serve custom AI workloads on NVIDIA A100/H100 hardware.
- Resource Catalogue: On-demand GPU hours, storage, and networking — billed in credits.
- VS Code Integration: Native extension for remote GPU sessions, Jupyter notebooks, and model monitoring.
- CLI / API: Programmatic access for CI/CD pipelines, batch training jobs, and fleet inference deployment.
- OTA Staging: All SENTINEL firmware and SENTINEL-OS updates are staged, validated, and deployed through QubitPage Foundation infrastructure.
3 — Investor Credits
Every euro invested in SENTINEL earns 1:1 datacenter credits on QubitPage Foundation. Credits can be used for:
- GPU compute hours for AI model training and inference
- Storage and data pipeline services
- Priority queue access to NVIDIA DGX A100 / H100 nodes
- Transfer within the QubitPage Foundation ecosystem
Credits never expire and are tracked in your Investor Portal.
4 — GPU Datacenter Specs
| Component | Specification |
|---|---|
| Compute Nodes | NVIDIA DGX A100 — 8x A100 80 GB per node |
| Interconnect | NVLink 600 GB/s + InfiniBand HDR 200 Gb/s |
| Storage | NetApp AFF A800 — NVMe all-flash, 100 TB usable |
| Network | Dual 100 GbE uplink, BGP peering |
| Power | 200 kWp solar + grid backup (net-zero target) |
| Location | On-site at SENTINEL factory, Ciurila, Cluj, Romania |
5 — Use Cases
- SENTINEL R&D: Training perception models, reinforcement-learning locomotion policies, and natural language interfaces for SENTINEL-OS.
- Investor AI workloads: Investors with credits can deploy their own ML training jobs — LLM fine-tuning, computer vision, scientific computing.
- Fleet Management: Real-time telemetry ingestion from deployed SENTINEL units, predictive maintenance models, and OTA rollout orchestration.