Underspecifying the Network Fabric
Cost: Very HighThe network fabric is the most commonly underspecified component in AI infrastructure. Organizations that select 100 GbE Ethernet for a large training cluster discover that GPUs spend 40–60% of their time waiting for data: wasting the most expensive hardware in the stack. The fix after deployment requires replacing switches, cables, and NICs, a project that can cost as much as the original network infrastructure.
How to Prevent It
Size the network fabric for the full cluster bandwidth requirement, not the average. Use InfiniBand NDR or 400 GbE RoCEv2 for clusters larger than 64 GPUs. Do not compromise on network bandwidth to reduce cost, the GPU hardware is far more expensive than the network.
Skipping the Site Readiness Assessment
Cost: Very HighOrganizations that skip the site readiness assessment consistently discover constraints after procurement is complete. A data center that cannot support 30 kW racks requires power infrastructure upgrades that take 6–12 months and cost hundreds of thousands of dollars, after the GPU hardware has already been purchased and is sitting in a warehouse.
How to Prevent It
Conduct a site readiness assessment before procurement. Assess power capacity, cooling headroom, structural load, and network connectivity. If the existing facility cannot support the planned deployment, identify the upgrades required and include them in the project scope and budget.
Selecting Components Independently
Cost: HighAI infrastructure is a tightly coupled system. Organizations that select GPUs from one vendor, network switches from another, storage from a third, and cooling from a fourth: without a system integrator who understands how the components interact: consistently produce systems that underperform. The interactions between components (GPU TDP vs. cooling capacity, network bandwidth vs. GPU utilization, storage throughput vs. training speed) must be engineered together.
How to Prevent It
Engage a system integrator who designs the full stack: compute, network, storage, power, cooling, as an integrated system. Require the integrator to validate that all components are compatible and that the system will perform as designed before procurement.
Ignoring GPU Hardware Lead Times
Cost: HighGPU hardware is in high demand and short supply. Organizations that plan their AI infrastructure project on a 6-month timeline and begin procurement after design is complete discover that the hardware will not arrive for 12 months. The project timeline must account for hardware lead times, which means procurement must begin before design is complete.
How to Prevent It
Initiate GPU hardware procurement as early as possible, ideally before design is complete. Current lead times for NVIDIA H100 and H200 GPUs are 6–12 months. Projects that begin procurement after design is finalized consistently miss their deployment timelines.
Underestimating Operational Complexity
Cost: HighAI infrastructure requires specialized operational expertise that most enterprise IT teams do not have. GPU cluster management, InfiniBand fabric administration, DCIM for high-density power and cooling, and the operational procedures for training job management are all specialized skills. Organizations that deploy AI infrastructure without planning for operations consistently experience poor utilization, frequent outages, and high operational costs.
How to Prevent It
Define the operational model before design begins. If the organization does not have GPU cluster management expertise, InfiniBand fabric administration skills, and liquid cooling maintenance capability, the operational model must include a managed services component. Budget for operations from day one.
Not Defining Acceptance Criteria
Cost: Medium-HighOrganizations that do not define acceptance criteria before the project starts have no basis for holding vendors accountable for performance. A system that "works" but delivers 60% of the expected GPU utilization is not a successful project: but without defined acceptance criteria, the vendor has no contractual obligation to address the performance gap.
How to Prevent It
Define specific, measurable acceptance criteria before the project starts. Include GPU utilization benchmarks, network throughput requirements, storage performance requirements, and power and cooling performance targets. Require the vendor to demonstrate that the system meets these criteria before final payment.
Choosing the Wrong Cooling Approach
Cost: Medium-HighOrganizations that deploy AI infrastructure in air-cooled facilities discover that GPU servers throttle under sustained training load: reducing performance by 20–40%. The fix requires retrofitting liquid cooling infrastructure, which is expensive and requires taking the facility offline.
How to Prevent It
Select the cooling approach based on the planned rack density, not the current facility capability. If the planned deployment requires 30 kW racks, design for liquid cooling from the start, retrofitting liquid cooling into an air-cooled facility is expensive and disruptive.
Inadequate Storage Architecture
Cost: MediumStorage is the second most commonly underspecified component in AI infrastructure. Organizations that use traditional NAS or SAN storage for training workloads discover that storage throughput limits GPU utilization: GPUs spend time waiting for data rather than computing. The fix requires replacing the storage system, which is expensive and disruptive.
How to Prevent It
Size the storage system for the peak throughput requirement of the full cluster, not the average. Use parallel file systems (GPFS, Lustre, WEKA, VAST) for training workloads. Do not use NAS or SAN storage as the primary training data store.
No Checkpoint Strategy
Cost: MediumTraining large models takes days to weeks. Without a checkpoint strategy, a hardware failure or software crash requires restarting training from the beginning, losing days of compute time. A well-designed checkpoint strategy limits the maximum loss to a few hours of training.
How to Prevent It
Design a checkpoint strategy before the project starts. Define checkpoint frequency, checkpoint storage location, and the recovery procedure for failed training jobs. Ensure the storage system can support the checkpoint write throughput required.
Single-Vendor Dependency
Cost: MediumOrganizations that build AI infrastructure with deep dependency on a single vendor: for hardware, software, and operations: find it difficult to change vendors when pricing increases, support quality declines, or the vendor is acquired. Design for vendor optionality from the start.
How to Prevent It
Design the infrastructure to avoid single-vendor dependency where possible. Use open standards (RoCEv2 over proprietary InfiniBand where performance allows, open-source storage software where appropriate). Ensure that the operational model does not require the original vendor for routine maintenance.