The largest scale for sovereign AI
The AI Data Center is the flagship of MICH's turnkey private AI infrastructure. It pre-integrates AI servers, rack power, precision cooling, and high-speed networking into a standard shipping container, delivered to your site and ready to run.
You do not need a new machine room, complex electrical fit-out, or separate procurement of servers, switches, UPS, and air conditioning. MICH packages all of this into a single deliverable, maintainable unit. Your data stays entirely inside your perimeter, invisible and unreachable by any cloud vendor.
Why containerized
Traditional data-center builds fail on speed, not just cost. Electrical work, HVAC installation, and rack integration typically take 6–18 months to get the first GPU online. The containerized AI DC compresses that to a matter of months, putting compute to work faster.
- Fast deployment: Any area on your site with a power feed becomes a compute facility, whether a parking area, outdoor lot, or warehouse yard.
- Density and efficiency: Liquid cooling makes per-square-meter compute density far exceed a conventional air-cooled room.
- Flexible scaling: Start with one container; add more as demand grows without touching existing IT.
- Sovereignty guaranteed: No public-cloud dependency; data never leaves the facility, meeting the strictest data-residency requirements.
Typical use cases
Multi-site private training cluster: Consolidate data from multiple plants into a controlled central training environment; distribute trained models back to each site's AI Server. The entire pipeline stays inside the corporate network.
Pharma / biomedical R&D compute: Large-scale molecular simulation, genomics analysis, and medical imaging all demand high-density GPU compute, and research data must never reach the cloud.
Government / financial secure compute: Classified document analysis, large-scale RAG knowledge bases, and AI audit systems all require a private compute environment with a well-defined perimeter.
One OS, everywhere
Every server inside the AI DC runs LowsiteOS: the same OS stack, same model format, and same management interface as the edge-side AI Server M1/M2. Models trained in the AI DC can be pushed to any AI Server node seamlessly, with no re-packaging or re-integration required.