IDC Facility Delivery
Support tier-oriented data room planning, modular rollout, and integrated monitoring for scalable environments.
The compute center capability map is organized into five modules so management and technical teams can align quickly.
We provide end-to-end AI infrastructure planning and execution, integrating facilities, compute, cooling, servers, and operational governance into one delivery approach.
Support tier-oriented data room planning, modular rollout, and integrated monitoring for scalable environments.
Design intelligent compute infrastructure for AI and HPCC workloads with heterogeneous resources.
Coordinate UPS, HVDC, load balancing, and liquid cooling strategies.
Integrate GPU clusters, schedulers, and distributed storage for training and inference.
Provide immersion and cold-plate options with leak detection, thermal simulation, and AI thermal management.
Cover architecture, optimization, energy management, and lifecycle operations.
View service value through the outcomes enterprises care about most when planning infrastructure investments.
Coordinate design, equipment, construction, and validation milestones to reduce execution delays.
Plan capacity, orchestration, and thermal control together to reduce bottlenecks in dense environments.
Bring PUE, visibility, compliance, and lifecycle planning into the same operating model.
The compute center capability map is organized into five modules so management and technical teams can align quickly.
Capacity modeling, modular facility planning, and liquid-cooling-ready architecture.
Integrate efficient power systems and liquid cooling support.
Shape GPU clusters, orchestration, and high-throughput storage layers.
Improve low-latency interconnect and reinforce security.
Use AIOps and predictive maintenance for visibility and expansion planning.
From new builds to upgrade programs, each service can be aligned with your operational goals and site conditions.
Coordinates facilities, servers, networking, and cooling as one program for new data-center and compute initiatives.
Supports technical upgrade paths for environments under pressure from density, latency, and energy targets.
Covers architecture guidance, phased adjustment, and long-term optimization under a more sustainable operating model.
The AI server lifecycle is presented in three stages to support design, optimization, and ongoing operations.
Map CPU, GPU, FPGA, and ASIC mixes for training or inference systems.
Cover model optimization, power management, and cooling compatibility.
Track utilization, memory pressure, and hardware aging for future planning.
The most common service requests usually begin from one of the following project situations.
Coordinates site conditions, power, cooling, and equipment planning as one program for new builds.
Introduces liquid cooling, dense servers, and network upgrades into operating environments with less disruption.
Provides a matching optimization path for efficiency, observability, and scale-planning priorities.
For compute centers, liquid cooling, AI servers, and infrastructure upgrade priorities, we can help shape the next discussion.