AI Infrastructure Design & Build — GPU Clusters, Liquid Cooling & InfiniBand Fabric
AI & Compute
AI Infrastructure
Purpose-built data centers and compute halls for AI training and inference — engineered for 130+ kW rack densities, liquid cooling, and InfiniBand GPU fabrics.
Why DCS Global
The AI Infrastructure Specialists
Unlike general-purpose data center integrators, DCS Global focuses exclusively on high-density AI and HPC infrastructure. Our engineers have deployed GPU clusters from 8 to 10,000+ GPUs across 40+ countries — bringing purpose-built expertise to every engagement.
130+ kW
Max rack density
40+
Countries deployed
10,000+
GPUs integrated
Why DCS Global for AI Infrastructure
AI-Optimized Design
Every design decision — power density, cooling topology, network fabric, storage architecture — is optimized for AI training and inference workloads, not repurposed from general-purpose data center specs.
Extreme Power Density
We design for 30–130+ kW per rack, with 480V power distribution, high-efficiency PDUs, and 2N redundancy to support the most demanding GPU clusters.
Liquid Cooling Expertise
Direct liquid cooling (DLC), rear-door heat exchangers, and single-phase immersion cooling — we design and install the full thermal infrastructure for high-density AI compute.
High-Speed Fabric
InfiniBand NDR (400 Gb/s) and RoCE v2 network fabric design for GPU-to-GPU communication with sub-microsecond latency and non-blocking topology.
Security & Compliance
Physical security, network segmentation, data encryption, and compliance documentation for AI workloads subject to HIPAA, FedRAMP, or export control requirements.
Performance Validation
Pre-production benchmarking with NVIDIA MLPerf and custom workload tests confirms the infrastructure meets performance targets before production cutover.
Delivery Process
AI Infrastructure Build Phases
Workload Analysis
GPU model selection, training vs. inference ratio, storage I/O requirements, network bandwidth modeling, and power envelope calculation.
Facility Design
Power infrastructure, cooling topology, structural loading, network entry, and physical security design for the AI compute hall.
Network Fabric Design
InfiniBand or Ethernet fabric topology, switch selection, cabling design, and routing architecture for GPU cluster communication.
Cooling System Installation
Liquid cooling distribution units (CDUs), manifold installation, rear-door heat exchangers, or immersion tank deployment and commissioning.
Compute Integration
GPU server rack integration, power cabling, network cabling, firmware configuration, and cluster software stack deployment.
Benchmarking & Handover
MLPerf benchmarking, thermal validation, power measurement, and documented handover with as-built drawings and O&M documentation.
Infrastructure Specifications
Frequently Asked Questions
What power densities can DCS Global support for AI clusters?
We design for 30–130+ kW per rack depending on GPU platform and cooling approach. NVIDIA H100 DGX systems typically require 10–11 kW per server; full racks of H200 systems can exceed 120 kW. We engineer the power and cooling infrastructure to match the specific GPU platform.
What cooling approach do you recommend for AI infrastructure?
For densities above 30 kW per rack, we recommend direct liquid cooling (DLC) or rear-door heat exchangers. For densities above 80 kW per rack, single-phase immersion cooling is the most efficient option. We evaluate the tradeoffs for each project based on density, facility constraints, and operational preferences.
Can you retrofit an existing data center for AI workloads?
Yes, but it requires careful assessment. Most existing data centers are designed for 5–15 kW per rack and cannot support AI densities without significant power and cooling upgrades. We perform a detailed feasibility study before recommending a retrofit vs. new build approach.
What networking is required for GPU clusters?
GPU-to-GPU communication for distributed training requires either InfiniBand NDR (400 Gb/s) or high-speed Ethernet (RoCE v2 at 200–400 GbE). The network fabric must be non-blocking with sub-microsecond latency. We design the full fabric from GPU NIC to spine switch.
Do you support both training and inference infrastructure?
Yes. Training infrastructure requires maximum GPU density, high-bandwidth networking, and large parallel storage. Inference infrastructure prioritizes latency, throughput per watt, and scalability. We design both, often in the same facility with separate power and cooling zones.
Why Organizations Act
Business Challenges We Solve
Power Density Beyond Data Center Limits
AI server racks draw 30–100 kW each. Most enterprise data centers are designed for 5–10 kW per rack and cannot support AI workloads without significant power and cooling infrastructure upgrades.
GPU Procurement and Allocation
H100 and H200 GPU allocations require 6–18 month lead times. Without vendor relationships and procurement expertise, AI projects stall before infrastructure is deployed.
Thermal Management at Scale
Air cooling fails above 30–40 kW per rack. AI facilities require direct liquid cooling (DLC), rear-door heat exchangers, or immersion cooling — technologies most facilities have never deployed.
Network Fabric for Distributed Training
Multi-node AI training requires 400Gb/s InfiniBand or RoCEv2 with rail-optimized topology. Standard enterprise networking introduces latency that collapses distributed training efficiency.
Storage Throughput for Training Pipelines
AI training requires 100–500 GB/s of sustained storage throughput. Inadequate storage architecture causes GPU idle time that inflates training costs by 30–50% and extends project timelines.
Operational Expertise Gap
AI infrastructure requires specialized skills across GPU platforms, CUDA, MLOps, and distributed systems. Most IT organizations lack the expertise to design, deploy, and operate purpose-built AI facilities.
Vendor-Neutral Expertise
Technology Ecosystem
DCS Global is vendor-neutral and works with the leading platforms in the industry. We recommend the right technology for your requirements — not the vendor with the best margin.
AI Server Platform
AI Networking
Thermal Management
AI Storage
AI Orchestration
Power & DCIM
Vendor-Neutral Advisory
DCS Global holds no exclusive reseller agreements that would bias our recommendations. Our engineers are certified across multiple platforms and will specify the solution that best fits your technical requirements, budget, and long-term roadmap.
Trusted Advisor Framework
AI Infrastructure Buyer\'s Guide
Use this framework to evaluate your requirements before engaging vendors. Organizations that complete this analysis make faster decisions and achieve better outcomes.
What AI workloads are you targeting — training, fine-tuning, or inference?
Training large foundation models requires maximum GPU density and cluster networking. Fine-tuning can use smaller GPU counts. Inference prioritizes throughput per dollar and often uses different GPU SKUs.
What is your power budget and existing facility capacity?
AI infrastructure requires 10–100 kW per rack. Facilities must be assessed for available power capacity, UPS headroom, and cooling capability before AI infrastructure design begins.
Do you require on-premises, colocation, or hybrid AI infrastructure?
On-premises AI delivers maximum performance and data sovereignty. Colocation provides faster deployment and shared infrastructure costs. Hybrid architectures serve both training and inference patterns.
What are your data sovereignty and compliance requirements?
Healthcare, defense, and financial services AI workloads carry HIPAA, ITAR, or SOX requirements that mandate specific data handling, security controls, and audit logging.
What is your GPU platform preference and procurement timeline?
NVIDIA H100/H200 offer the broadest software ecosystem. AMD MI300X offers competitive memory bandwidth. Both have 6–18 month procurement lead times that must be factored into project planning.
What is your target time-to-production for AI workloads?
Purpose-built AI facilities require 6–18 months from design to commissioning. Colocation deployments can be operational in 60–90 days. Timeline requirements drive the infrastructure strategy.
Not sure where to start? Our solutions advisors can walk you through this framework in a 30-minute discovery call.
Schedule an Infrastructure AssessmentDecision Framework
On-Premises AI Infrastructure vs. Cloud AI
Use this framework to evaluate whether building a purpose-built AI facility or using cloud AI services is the right decision for your organization.
| Criterion | Purpose-Built AI Facility | Cloud AI (AWS, Azure, GCP) | Best For |
|---|---|---|---|
| GPU Performance | Bare-metal — full NVLink bandwidth, no noisy neighbor | Variable — shared infrastructure, limited NVSwitch access | Purpose-Built AI Facility |
| Data Sovereignty | Complete — training data never leaves your facility | Depends on provider controls and region selection | Purpose-Built AI Facility |
| Upfront Capital | High — $10M+ for a production AI facility | Zero — pure OpEx model | Cloud AI (AWS, Azure, GCP) |
| 3-Year TCO (High Utilization) | Lower — on-prem wins at sustained, high-utilization workloads | Higher — cloud GPU costs 3–5x on-prem at sustained use | Purpose-Built AI Facility |
| GPU Availability | Guaranteed once deployed | Constrained — H100 availability limited across all regions | Purpose-Built AI Facility |
| Deployment Speed | 6–18 months from design to commissioning | Hours to days for new GPU capacity | Cloud AI (AWS, Azure, GCP) |
| Elasticity | Fixed capacity — scale requires procurement lead time | Elastic — scale up or down in minutes | Cloud AI (AWS, Azure, GCP) |
| Operational Control | Full control over hardware, software, and security stack | Limited by provider abstractions and managed services | Purpose-Built AI Facility |
This comparison is a general framework. The right choice depends on your specific requirements, existing environment, and business objectives. DCS Global can help you evaluate the options for your situation.
Continue Learning
Resource Center
Continue your research with these curated resources from the DCS Global knowledge base.
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Related resources
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Technical guides
Physical Infrastructure Foundation
Preparing Mission-Critical Infrastructure for AI
AI readiness is not only a question of which computing technology to deploy. It is equally a question of whether the physical infrastructure supporting that technology — power, cooling, connectivity, and commissioning — is prepared to sustain it reliably. DCS Global's 20+ years of mission-critical infrastructure experience provides a verified foundation for helping organizations evaluate and address the physical layer requirements that AI workloads impose.
Verified Existing Capability
DCS Global has 20+ years of verified experience in critical power systems, UPS installation and maintenance, battery systems, power distribution, commissioning and load testing, fiber and telecom infrastructure, and data center design. These capabilities are directly relevant to the physical infrastructure requirements of AI deployments — and represent the foundation of what DCS Global brings to AI infrastructure discussions.
Emerging and Developing Specialization
AI-specific infrastructure disciplines — including GPU cluster integration, high-density liquid cooling deployment, InfiniBand fabric design, and AI workload performance validation — represent areas where DCS Global's physical infrastructure foundation is relevant, but where AI-specific specialization continues to develop. We are transparent about this distinction so customers can make informed decisions about the scope of engagement.
Reliable Critical Power
Verified DCS Global Capability
DCS Global designs, installs, and maintains critical power systems for facilities where power interruption is not acceptable. This foundation is directly applicable to AI environments, where a single unplanned power event can corrupt training runs representing days of compute time.
Relevance to AI Infrastructure
AI training workloads run continuously for hours or days. Power reliability at the facility level — not just the UPS level — determines whether those runs complete successfully.
UPS Systems
Verified DCS Global Capability
UPS system design, installation, and long-term preventive maintenance are among DCS Global's core service areas. We work across major UPS platforms and have maintained UPS infrastructure in data centers, hospitals, financial institutions, and utilities.
Relevance to AI Infrastructure
AI server racks draw significantly more power than conventional IT equipment. UPS systems serving AI infrastructure must be sized, maintained, and tested to handle the actual load — not the load the facility was originally designed for.
Battery Systems
Verified DCS Global Capability
Battery maintenance, testing, and replacement are verified DCS Global capabilities. We assess battery health, perform scheduled maintenance, and manage replacement programs for UPS systems across a range of facility types.
Relevance to AI Infrastructure
Battery systems are the last line of defense before generator transfer. In AI environments, degraded battery capacity that goes undetected can result in a gap between utility loss and generator pickup — causing exactly the kind of unplanned outage that destroys in-progress training jobs.
Power Distribution
Verified DCS Global Capability
DCS Global installs and maintains power distribution infrastructure — PDUs, switchgear, automatic transfer switches, and branch circuit systems — in mission-critical facilities. We assess existing distribution capacity and design upgrades when facilities need to support higher loads.
Relevance to AI Infrastructure
AI server racks require significantly higher per-rack power delivery than conventional IT. Existing PDUs, branch circuits, and distribution paths must be assessed before AI equipment is deployed — and upgraded if they cannot support the load.
Higher Rack Density
Verified DCS Global Capability
DCS Global's data center design and power distribution work includes facilities with elevated rack densities. We assess whether existing power and cooling infrastructure can support increased density before recommending deployment.
Relevance to AI Infrastructure
AI infrastructure operates at rack densities that most enterprise data centers were not designed to support. A density assessment — covering power capacity, cooling adequacy, and structural loading — is a necessary first step before any AI deployment in an existing facility.
Cooling Requirements
Verified DCS Global Capability
DCS Global installs and maintains precision cooling systems in mission-critical facilities. Our data center design work includes cooling architecture for facilities with varying density requirements. We assess whether existing cooling infrastructure can support proposed load increases.
Relevance to AI Infrastructure
AI workloads generate substantially more heat per rack than conventional IT. Cooling infrastructure that is adequate for a standard data center may be inadequate for AI densities. An honest cooling assessment — before equipment arrives — prevents costly retrofits and operational failures.
Fiber Connectivity
Verified DCS Global Capability
Fiber optic installation, splicing, testing, and structured cabling are verified DCS Global capabilities. We design and install inside plant and outside plant fiber infrastructure in data centers and enterprise facilities.
Relevance to AI Infrastructure
AI infrastructure requires high-bandwidth, low-latency connectivity between compute nodes, storage systems, and the broader network. Fiber infrastructure quality — including cable type, connector quality, and pathway design — directly affects the performance and reliability of AI workloads.
Network Reliability
Verified DCS Global Capability
DCS Global's network engineering and fiber infrastructure capabilities support the physical and structured cabling layer of network infrastructure. We design and install the physical connectivity that network equipment depends on.
Relevance to AI Infrastructure
Distributed AI training is highly sensitive to network interruptions. A single dropped packet in a multi-node training job can stall the entire cluster. Physical network infrastructure — cable quality, pathway redundancy, and connection integrity — is the foundation that network reliability is built on.
Commissioning
Verified DCS Global Capability
Commissioning and integrated systems testing are verified DCS Global capabilities. We perform acceptance testing, load bank testing, and functional performance testing for critical power and infrastructure systems before facilities go live.
Relevance to AI Infrastructure
AI infrastructure that has not been properly commissioned is infrastructure that has not been verified to perform under load. Commissioning — testing power systems, cooling systems, and transfer sequences at actual operating conditions — is how you confirm the facility will support AI workloads before they are deployed.
Load Testing
Verified DCS Global Capability
Load bank testing for generators and UPS systems is a standard part of DCS Global's commissioning and maintenance programs. We test systems at rated capacity to verify performance before and during facility operation.
Relevance to AI Infrastructure
AI workloads impose sustained, high-utilization loads that are different from the intermittent loads most data center equipment was tested against. Load testing at AI-representative power levels — before AI equipment is deployed — reveals capacity gaps and equipment limitations that would otherwise surface as operational failures.
Equipment Maintenance
Verified DCS Global Capability
Preventive maintenance programs for UPS systems, batteries, generators, and precision cooling are among DCS Global's core service areas. We maintain critical infrastructure on scheduled programs designed to prevent failures before they occur.
Relevance to AI Infrastructure
AI infrastructure runs at high utilization continuously. Equipment that is not maintained on a rigorous schedule degrades faster and fails more often. A maintenance program designed for the actual operating conditions of AI infrastructure — not the lighter loads of conventional IT — is essential for sustained reliability.
Operational Readiness
Verified DCS Global Capability
DCS Global's commissioning, start-up, and maintenance capabilities are oriented toward verifying that infrastructure is operationally ready — not just installed. We document as-built conditions, test performance under load, and confirm that systems respond correctly to failure scenarios.
Relevance to AI Infrastructure
Operational readiness for AI infrastructure means the facility has been tested at AI-representative loads, failure scenarios have been exercised, maintenance procedures are in place, and the team responsible for the facility understands how it behaves under the conditions AI workloads create.
Infrastructure Lifecycle Planning
Verified DCS Global Capability
DCS Global supports the full infrastructure lifecycle — from initial design and installation through long-term maintenance and eventual decommissioning. Our lifecycle approach is grounded in 20+ years of maintaining critical infrastructure across a range of facility types.
Relevance to AI Infrastructure
AI infrastructure is not a one-time deployment. GPU platforms evolve, power and cooling requirements change, and facilities must be planned to accommodate future capacity additions. Infrastructure lifecycle planning — accounting for how AI requirements will evolve — is how organizations avoid building themselves into a corner.
Assessment-Oriented Engagement
Request an AI Infrastructure Readiness Discussion
Before committing to an AI infrastructure deployment, it is worth understanding whether your physical infrastructure — power, cooling, connectivity, and commissioning — is prepared to support it reliably. DCS Global can conduct a structured readiness discussion to help you identify gaps, prioritize improvements, and understand what the physical layer requirements of your AI deployment actually are.
Infrastructure Readiness
Power, Commissioning & Start-Up for AI Facilities
AI infrastructure fails at the physical layer before it fails at the software layer. DCS Global verifies power availability, commissions every critical system, and validates performance before any GPU cluster goes into production.
Power Availability Assessment
A single NVIDIA H100 SXM5 server draws 10.2 kW. An 8-GPU cluster requires 110–120 kW including overhead. Most existing data centers cannot support this density without upgrades.
DCS Global conducts a power availability assessment before any AI deployment — verifying utility capacity, UPS headroom, PDU ratings, and branch circuit availability. If your facility cannot support AI workloads, we design the upgrades.
A documented power readiness report with specific upgrade recommendations, cost estimates, and a deployment timeline that accounts for electrical work.
Critical Systems Commissioning
AI clusters cannot tolerate unplanned power interruptions or cooling failures. Every critical system must be verified to specification before production workloads are loaded.
DCS Global performs NETA-certified commissioning of UPS systems, PDUs, cooling infrastructure, and transfer switches before AI cluster installation — including load bank testing at full rated capacity and integrated systems testing under simulated failure scenarios.
A commissioned facility with documented NETA test results, verified transfer times, and confirmed cooling performance at AI rack densities — before a single GPU is installed.
Installation, Start-Up & Validation
GPU cluster installation requires precise sequencing — power, cooling, networking, and compute must be installed and verified in the correct order to avoid damage and delays.
DCS Global manages turnkey AI cluster installation — rack and stack, power and cooling connections, network fabric cabling, firmware configuration, and pre-production performance validation using NVIDIA MLPerf benchmarks.
A production-ready AI cluster with documented installation records, verified network fabric performance, and benchmark results confirming the infrastructure meets design targets.
Next Step
Build Your AI Facility
Share your GPU platform, power budget, and timeline. We will deliver a purpose-built AI infrastructure design within two weeks.
Start Your Project
Build Your AI Facility
Share your GPU platform, power budget, and timeline. We will deliver a purpose-built AI infrastructure design within two weeks.
AI Infrastructure — Frequently Asked Questions
Technical questions from IT Directors and infrastructure architects evaluating enterprise AI deployments.