Technology
Engineered for Density, Efficiency, and Control
Three layers define the Platform Data architecture: single-phase immersion cooling, Nexus Center orchestration, and a resource-efficient approach to energy and sustainability.
Layer 01
Immersion Cooling
Platform Data uses single-phase liquid immersion cooling to support high-density AI compute with reduced cooling complexity and improved efficiency compared with air-cooled designs. Servers operate submerged in a dielectric fluid that transfers heat to a closed loop, and dry coolers reject that heat without evaporative process water in normal operation.
Designed for Density
Designed for high-density AI compute beyond the practical limits of conventional air cooling.
Low Cooling Overhead
Targets low cooling overhead, with an internal design target of approximately 1.03 PUE where validated and where site conditions, commissioning, and operating profile support it.
Thermal Stability
Supports improved thermal stability across sustained AI workloads.
Compact Deployment
Supports a compact deployment footprint per unit of compute.
Reduced Mechanical Complexity
Fewer mechanical cooling components than conventional air-cooled designs.
Waterless Heat Rejection
Supports non-adiabatic dry-cooler heat rejection; no evaporative process water required in normal operation.
Layer 02
Nexus Center
Nexus Center is the orchestration and management layer that connects Data Nodes into a managed service environment. It gives customers a single interface to capacity that may span multiple regions and compliance contexts.
- Workload placement aligned with regional policy controls.
- Metering and billing support for consumption and reserved capacity.
- SLA monitoring with customer-facing visibility.
- Compliance evidence for audit and governance requirements.
- Operational visibility across every connected Data Node.
Layer 03
Energy & Sustainability
Power, water, land, permitting, and community acceptance now shape where and how AI infrastructure deploys. Platform Data embeds its sustainability strategy in the physical architecture of the Data Node rather than layering claims on top of a conventional design.
Platform Data's Data Node architecture is designed to reduce the physical resource intensity of AI infrastructure. By combining single-phase immersion cooling, closed-loop dry-cooler heat rejection, compact modular deployment, and prepared industrial-site reuse, Platform Data supports a more efficient path for scaling managed AI compute capacity.
Resource Efficiency by Attribute
| Attribute | Approach |
|---|---|
| Water | Closed-loop liquid cooling with non-adiabatic dry-cooler heat rejection; no evaporative process water required for heat rejection in normal operation. |
| Energy Efficiency | Immersion-cooled architecture targets low cooling overhead, with an internal design target of approximately 1.03 PUE where validated and where site conditions, commissioning, and operating profile support it. |
| Space Efficiency | High-density immersion cooling supports compact Data Node deployment and reduced physical footprint per unit of compute. |
| Acoustic Discipline | Reduced mechanical cooling complexity can support lower acoustic impact, subject to site-specific acoustic engineering and local requirements. |
| Industrial Reuse | Qualified industrial sites can support adaptive reuse, faster deployment, and reduced dependence on large greenfield campus development. |
| Heat Reuse | Where feasible, Data Node thermal output may support downstream heat-reuse applications such as industrial process heat, district heating, greenhouse heating, aquaculture, or domestic hot-water preheating. |
| Governance & Compliance | Local operating structures, compliance-aware workflows, audit evidence, and Nexus Center visibility support responsible deployment for enterprise, regulated, and sovereign workloads. |
Platform Data frames ESG performance around measurable infrastructure efficiency. It publishes validated metrics only when formal validation supports them.
See the architecture applied to your workload.
The same Data Node design serves training, inference, dedicated clusters, and sovereign deployments.