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AI infrastructure can no longer be planned as separate GPU, network, building and utility projects. Open Compute Project (OCP) describes the new reality as “one large chip to grid design, deployment and operations problem.” The reason is physical: accelerator generations and workloads are changing faster, rack power is rising by an order of magnitude, and cooling, electrical distribution and grid interconnection now determine whether a cluster can operate at its advertised performance.
Co-design means fixing those dependencies together, using repeatable rack and site blocks that can be procured, commissioned and upgraded. It does not mean every enterprise should copy a hyperscale campus. It means selecting a complete, serviceable system before buying isolated components.
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Why separate roadmaps are breaking down
A conventional data-center plan might reserve floor space, specify an electrical service, then purchase servers when a budget is approved. That sequence is too slow and too loosely coupled for dense AI. A facility may be operational in two to three years, while transmission upgrades, substations and interconnection studies can require longer planning and construction cycles. Meanwhile, a new accelerator generation can change the rack power, network bandwidth and cooling requirement before the building is finished.
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- A GPU cluster selected without its coolant-distribution and heat-rejection loop can require an expensive retrofit.
- Compute capacity reserved without firm grid capacity can leave paid equipment idle or force operation below its design power.
- A network topology chosen without cable routes, power paths and service clearances can create physical bottlenecks.
- A building sized for today’s rack density may strand space when a later generation needs far more power per cabinet.
OCP’s Open Cluster Designs for AI responds by publishing procurement-oriented guidance for topology, cabling, air and liquid cooling, power distribution and airflow—areas that were often treated as one-off engineering work.
The scale of the change
OCP’s strategic-initiative roadmap points to an order-of-magnitude increase in rack power density, including 1 MW racks, gigawatt-scale facilities and multi-gigawatt campuses. A 1 MW rack is not a universal specification; it is a planning threshold that makes ordinary branch circuits, cooling loops, floor loading, busways and maintenance procedures inadequate unless they are redesigned as one system.
NVIDIA’s DSX reference pattern illustrates the range already being addressed with repeatable blocks. It lists cabinet thermal design power (TDP) from 198 kW for MGX Gen 1.1 to 330 kW for Vera Rubin NVL72. The pattern pairs liquid-to-liquid thermal systems with N+1 coolant-distribution-unit (CDU) redundancy and coordinates space, power, cooling and connectivity in a Scalable Unit. Those figures describe that reference design, not a universal AI-rack requirement.
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The energy consequence is system-wide. The International Energy Agency (IEA) estimates that data centers used 415 TWh of electricity in 2024 and projects about 945 TWh in 2030 in its base case. Cooling alone ranges from about 7% of electricity use in efficient hyperscale facilities to more than 30% in less-efficient enterprise facilities. Efficiency therefore depends on the thermal architecture and operating conditions, not just on the accelerator’s rated performance.
The UK’s 2025 Compute Roadmap forecasts at least 6 GW of AI-capable data-center capacity by 2030. That target links clusters to land, grid upgrades, capital and industrial policy—evidence that site planning is now part of compute strategy.
What a co-designed AI system includes
Chip, software and workload
OCP’s AI Hardware/Software Co-Design workstream targets utilization, energy efficiency, flexible configuration, heterogeneous resources and scaling from chips to clusters. Its Polymorphic Architecture definition was formalized in 2024 and used in 2025 to specify AI fabrics, topologies and data services. The practical implication is to measure the workload’s communication pattern and utilization, not merely buy the highest theoretical accelerator throughput. A model that leaves expensive devices waiting on memory or network transfers can waste both power and scarce grid capacity.
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Cluster and network
Open Cluster Designs for AI separates scale-up, scale-out, front-end, storage and management networks while addressing physical and logical topology and cable management. Network choices affect switch count, optical budgets, cable distance, rack placement, airflow and service access. The topology must therefore be drawn together with cabinet rows, overhead or underfloor pathways and the electrical distribution plan.
Rack, cooling and heat rejection
Air cooling remains viable for lower-density systems and some enterprise deployments, but high-density designs increasingly use direct-to-chip liquid cooling and liquid-to-liquid heat transfer. Liquid cooling is not automatically mandatory for every AI server; it becomes a capacity and reliability decision as sustained rack power rises. A design must specify coolant chemistry, leak detection, CDU redundancy, facility-water temperatures, heat-rejection equipment, isolation procedures and how a failed component is serviced without taking down the cluster.
Power conversion and the grid
OCP is working on 800 VDC distribution, a path toward 1,500 VDC, solid-state transformers, energy storage, telemetry and disturbance performance between data centers and utilities. These technologies can reduce conversion stages or improve observability, but they also introduce protection, standards, maintenance and utility-coordination requirements. The electrical single-line diagram, harmonic behavior, ride-through strategy and demand profile belong in the same review as the server bill of materials.
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Site, permitting and policy
IEA analysis finds that data-center integration policies increasingly seek visibility into demand by scale, timing and location. A site with nominal megawatts on paper may still fail if the interconnection schedule, substation work, water availability, land-use approvals or construction workforce cannot meet the cluster’s deployment date.
What does a 1 MW AI rack require?
It requires an engineered power-and-thermal block, not simply more servers. Designers must validate the rack’s peak and sustained load, busway or busbar ratings, protection coordination, UPS and generator behavior, floor loading, liquid connections, heat-rejection capacity, network cabling and safe maintenance clearances. They also need a plan for partial loading: a rack that reaches 1 MW only during short bursts has different utility and storage implications from one that sustains that draw continuously.
Because published reference designs span 198 kW to 330 kW per cabinet, a “1 MW rack” may represent several coordinated cabinets or a future high-density block rather than one standardized enclosure. Treat the number as a site-planning envelope and document exactly which equipment, cooling overhead and redundancy it includes.
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How to replace independent roadmaps with a co-design process
- Define the workload envelope. Record model sizes, training and inference phases, utilization targets, memory needs, communication patterns, acceptable latency and expected accelerator refresh dates.
- Set power and thermal cases. Calculate both peak and sustained rack demand, include networking and cooling overhead, and model degraded operation with a pump, CDU, power module or chiller unavailable.
- Select a reusable physical block. Specify cabinet dimensions, liquid interfaces, busway, cable paths, service clearances, fire protection, monitoring and the smallest unit that can be deployed and expanded.
- Validate the network and building together. Map scale-up and scale-out links, optical reach, switch locations, airflow or coolant routes, floor loading and replacement paths before fixing row layouts.
- Secure grid capacity on the real schedule. Obtain an interconnection study, milestone dates, available fault level, power-quality requirements and any transmission or substation work; do not treat a utility reservation as energization.
- Phase procurement and commissioning. Use the same acceptance tests for electrical quality, coolant flow, leak detection, telemetry, network performance and failover at factory, integrated-system and live-cluster stages.
Is liquid cooling mandatory for AI servers?
No. It is a design choice governed by sustained density, inlet conditions, acoustic limits, available facility water and service capability. Air can be simpler for modest-density racks, but its fans and room-level heat removal consume space and energy. Direct-to-chip liquid cooling can support higher heat flux in a compact footprint, while liquid-to-liquid systems transfer heat through a CDU to the facility loop. The trade-off is added plumbing, controls, water-quality management and technician training.
Should an enterprise copy a hyperscaler design?
Copy the method, not the scale. An enterprise should reuse validated interfaces, telemetry and failure testing where they fit, then right-size redundancy, network fabrics, cooling and utility service to its workload and site. A small cluster may sensibly use air cooling and conventional distribution; adopting a gigawatt-campus architecture can add cost and operational complexity without improving utilization.
Which choices are hardest to change later?
- Grid location and interconnection: moving a site or increasing its contracted capacity can take longer than replacing servers.
- Cooling distribution: embedded piping, CDU rooms, water treatment and heat rejection are expensive to retrofit.
- Structural and spatial allowances: floor loading, ceiling height, risers, service aisles and cable pathways constrain later cabinet density.
- Network topology: changing from a scale-up fabric or altering cable distances can require new switches, optics and physical routes.
- Controls and telemetry: retrofitting unified power, thermal and workload data is harder than specifying it at the start.
A practical comparison framework
Evaluate candidate architectures against the same questions rather than comparing accelerator prices alone.
| Dimension | Questions to answer |
|---|---|
| Performance and energy | What performance per watt is sustained on the target workload, and what utilization is required? |
| Power profile | What are peak, sustained and degraded-mode rack loads, including cooling and network overhead? |
| Thermal design | Is the system air-cooled, direct-to-chip or liquid-to-liquid, and what redundancy and service steps are required? |
| Network and cabling | Which scale-up, scale-out, storage and management fabrics are needed, at what distances and with what growth margin? |
| Grid and schedule | When will firm capacity and interconnection equipment be available, and what happens if milestones slip? |
| Portability and modularity | Can the rack, CDU, busway and software stack accept the next accelerator generation? |
| Build strategy | Which elements are feasible in an existing room, and which require greenfield construction? |
| Operations and cost | Can technicians isolate failures safely, and what are the embodied energy, operating energy and total cost over the deployment life? |
The bottom line for planners
AI has made infrastructure dependencies too tight for sequential roadmaps. Start with workload and grid realities, then choose a rack, thermal loop, network fabric and site block that can be delivered and operated together. Reference designs from OCP and NVIDIA show how to standardize those interfaces; IEA and national planning figures show why the resulting decisions now belong in energy and industrial strategy as well as IT.
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