GPUs are only the accelerators. A usable AI data center also needs host CPUs and system memory, high-speed scale-up and scale-out networking, local and shared storage, rack infrastructure, dense electrical distribution, liquid or air heat-removal equipment, and management and security systems. The correct mix depends on whether the site trains models or serves inference, how many nodes must communicate, storage bandwidth, rack power density, resilience targets, and the expansion plan.
Start with the host system: CPUs, memory and boot devices
Every GPU server still requires general-purpose computing. Host CPUs boot the operating system, run drivers and orchestration agents, prepare and validate data, handle input/output, and provide services that do not belong on an accelerator. System memory holds the operating system, framework runtime, staging buffers, metadata and caches. If CPU cores or RAM are undersized, expensive GPUs can sit idle while the host waits on preprocessing, storage or control tasks.
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As a concrete rack-scale reference, NVIDIA’s current GB200/GB300 compute-tray specification uses two Grace ARM CPUs with up to 1 TB of LPDDR5 memory. That is a design example, not a universal requirement for every AI server: a smaller inference node may need far less memory, while a training node must size host RAM for dataset staging, checkpoint handling and the number of accelerators it feeds.
What the host layer must handle
- Operating-system startup, GPU drivers, container runtimes and cluster agents.
- Data decoding, tokenization, augmentation and batching before work reaches the GPUs.
- Network and storage I/O, including checkpoint writes and dataset reads.
- Control-plane services such as scheduling, health checks and remote provisioning.
- Temporary buffers and caches that prevent repeated reads from shared storage.
Boot media and local data media are separate considerations. A server needs a reliable boot device for the operating system and firmware tools, plus enough local capacity for caches and temporary files.
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Networking has two jobs: scale up inside the rack and scale out across it
Distributed AI workloads exchange far more than ordinary application traffic. Gradients, activations, parameters, checkpoints and service requests all compete for links. The design therefore separates low-latency communication among accelerators from traffic between nodes, racks and storage.
Scale-up fabric
Scale-up links connect GPUs and switch trays within one server or rack. They are optimized for high bandwidth and predictable latency so a model can be split across accelerators without spending most of its time waiting for synchronization. In a rack-scale system, the fabric switches, cables and software topology are as important as the GPU count; a broken or miswired link can strand part of the accelerator pool.
Scale-out network
Scale-out links connect compute nodes to other nodes, racks and storage systems. Ethernet or InfiniBand can carry this traffic, depending on the architecture and the required congestion control, latency and management model. NVIDIA’s GB200 example lists two 400 GbE tenant-access links and four 400 Gb east/west links per tray. The east/west links can be configured for InfiniBand or Ethernet. Those figures describe that tray design, not a minimum for all deployments.
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NVIDIA’s enterprise architecture identifies Spectrum-X Ethernet switches and BlueField-3 SuperNICs as components that move traffic among GPUs, CPUs and storage. In practice, plan separate bandwidth and failure domains for:
- GPU-to-GPU or GPU-to-switch traffic inside a node or rack.
- Node-to-node traffic for distributed training and parallel inference.
- North-south tenant or user traffic entering the service.
- Storage traffic to file systems, object stores and backup targets.
- Out-of-band management, which should remain reachable when the production fabric is degraded.
Network adapters, switch ports, optics or copper assemblies, transceivers, cabling paths and fabric-management software must be specified together. A high-speed switch without matching adapters and correctly qualified cables cannot deliver its rated path.
Storage is a hierarchy, not a single array
AI systems use different media for boot, scratch work, shared datasets and durable checkpoints. Treating all of those jobs as one storage tier usually creates either unnecessary cost or a bottleneck that leaves GPUs waiting.
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| Storage tier | Primary role | Design implication |
|---|---|---|
| Local NVMe | Boot, cache, temporary data and fast node-local access | Place capacity and endurance in each compute node; local data is fast but not automatically shared or durable. |
| Shared file storage | Datasets and checkpoints consumed by many nodes | Size throughput and metadata performance for concurrent readers and writers, not just raw capacity. |
| Object storage | Durable data lake or archive | Use for long-lived datasets, completed checkpoints and retention workflows; account for network and retrieval latency. |
The GB200/GB300 design allows up to eight NVMe data drives plus one NVMe boot drive per compute tray. Endurance, form factor, firmware, thermal limits and server qualification matter more than choosing a generic consumer SSD. Drives used for repeated checkpoint writes may need a different endurance class from drives used mainly for read caches.
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1Scan for outdated or missing drivers - takes under a minute2Repair Windows errors before they cause bigger problems3Fix the driver behind crashes, sound loss and screen glitchesNVIDIA notes that storage bandwidth per GPU varies with workload, model and performance requirements. A language-model training run that streams large shards and writes frequent checkpoints has a different profile from an inference service that keeps a working set in memory and reads only occasional updates. Shared systems from vendors such as WEKA, VAST and DDN are examples of AI-oriented file or object-storage approaches; the appropriate choice still depends on measured concurrency, failure recovery and data-governance needs.
Rack mechanics turn components into a serviceable system
High-density accelerators cannot simply be placed in a conventional rack and connected with ad-hoc wiring. Rack-scale systems require a mechanical and interconnect plan that preserves airflow or liquid-cooling paths, maintains cable bend radii and allows failed parts to be replaced without dismantling the entire cluster.
Typical infrastructure includes standardized racks, compute trays, fabric or network switch trays, power shelves, structural stiffeners, high-speed copper or optical cables, and coolant-distribution connections. NVIDIA’s GB200/GB300 rack pattern contains 18 compute trays and nine NVLink switch trays, along with power shelves, stiffeners and other infrastructure components. That pattern illustrates the ratio of compute to fabric in one design; it is not a universal rack layout.
Mechanical checks before delivery
- Verify rack depth, rail kits, tray weight limits and floor loading.
- Reserve front and rear clearance for service and cable routing.
- Document every copper, optical and management connection, including spare paths.
- Coordinate coolant manifolds and drip-free quick disconnects with tray removal procedures.
- Confirm that power shelves, busbars and cable paths do not block airflow or service access.
Electrical infrastructure must support dense, continuous loads
AI racks draw concentrated power, so the electrical design extends far beyond a rack power strip. A facility may need medium-voltage service, switchgear, transformers or power-conversion equipment, rack power shelves, distribution panels, monitoring and backup generation. Protection settings, grounding, phase balance and selective coordination must be engineered for the actual rack and building design.
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NVIDIA’s 800 VDC roadmap says direct 800 V input can remove rack-level AC/DC conversion and is intended to support 1 MW IT racks and beyond, with the transition starting in 2027. This is a future architecture target, not a requirement that every present-day data center install an 800 V bus. Existing sites should select voltage, rectifiers, busbars and protection equipment that their utility service and local codes support.
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Backup-power resilience
Power quality and continuity targets determine how much redundancy is justified. NVIDIA’s DSX facilities reference specifies central standby generators in an N+2 configuration at the 34.5 kV switchgear level. N+2 means the site can lose two generator units and still meet the defined load; it does not by itself guarantee uninterrupted operation. UPS systems, transfer equipment, fuel autonomy, maintenance procedures and a controlled workload-shutdown plan remain separate design decisions.
Cooling removes the heat that the electrical system delivers
Nearly all power consumed by compute becomes heat. Cooling hardware may include cold plates attached to processors, coolant-distribution units, pumps, heat exchangers, chillers, dry coolers or cooling towers, valves, sensors and control software. The right combination follows rack heat density, allowable component temperatures, water availability, humidity and outdoor climate.
Air, hybrid and liquid approaches
- Air cooling: Works for lower-density racks when room airflow, containment and fan power can remove the load. It becomes difficult as accelerator heat rises.
- Hybrid cooling: Uses liquid cold plates for the hottest devices while air handles memory, drives and power electronics.
- Liquid cooling: Moves heat directly from processors to a coolant loop, allowing higher rack density but adding pumps, manifolds, leak detection, treatment and maintenance requirements.
Cooling has its own electrical architecture. NVIDIA’s facilities reference places chillers on a decoupled 4.16 kV bus while pumps, coolers and controls use 480 V. The exact voltages will vary by facility, but the principle is important: cooling loads need dedicated distribution, monitoring and backup decisions rather than being treated as an afterthought to IT power.
GPU-ready guidance links power, cooling, rack layout, network architecture and storage. Changing one layer can invalidate assumptions in the others: raising rack power may require a different coolant loop, larger feeders, new switchgear and a revised floor layout.
Management, security and control keep the cluster operable
A large accelerator pool needs independent ways to provision, observe and recover it. Out-of-band management switches and controllers provide access to baseboard management functions, console redirection, power cycling and firmware updates even when the operating system or production network is unavailable. In-band networking carries normal cluster traffic and application telemetry.
Control-plane hardware and software
- Out-of-band switches, management controllers and serial or console access.
- Telemetry collectors for temperature, power, link health, drive wear and coolant conditions.
- Remote provisioning and imaging systems for operating systems, drivers and containers.
- Firmware lifecycle tools that coordinate updates across servers, adapters, switches and power equipment.
- Cluster schedulers and orchestration services that allocate GPUs, CPUs, memory and storage together.
- Identity, access-control and audit systems protecting management interfaces and model data.
NVIDIA describes both in-band and out-of-band management for provisioning, monitoring and managing AI infrastructure. Its DSX documentation connects compute, network, energy, power and cooling signals through an IT/OT integration and communications hub. That integration lets operators correlate a throttled GPU with a failing pump, overloaded feeder or congested fabric instead of treating each alarm as an isolated incident.
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Choose the architecture for the workload
Training clusters
Training normally stresses scale-up and scale-out communication, sustained storage reads, checkpoint writes and synchronized scheduling. Prioritize a balanced fabric, enough host memory to stage data, high-throughput shared storage and failure handling that can resume or roll back a distributed job. Checkpoint placement should account for both local NVMe speed and durable shared or object storage.
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Inference may favor predictable latency, rapid model loading, tenant isolation and efficient serving over the largest possible cross-rack fabric. Local NVMe can accelerate model images and caches, while shared storage remains important for version distribution and durable logs. Network capacity must be sized for client traffic as well as GPU-to-GPU communication when a model is partitioned across devices.
Small, growing and large deployments
A single-rack installation can consolidate switching, storage and management, but it still needs independent management access and a cooling design matched to its power density. A multi-rack cluster should establish repeatable rack units, reserved network and electrical capacity, and a storage namespace that can expand without disruptive migration. At larger scale, separate failure domains for racks, power paths, cooling loops and fabric tiers become essential.
A practical planning sequence
- Define the workload: Record model size, training or inference mode, concurrency, checkpoint frequency, target latency and expected growth.
- Set the accelerator topology: Decide how many GPUs share a node or rack and what communication must remain on the scale-up fabric.
- Size hosts: Select CPU cores, system memory, boot media and local NVMe for preprocessing, staging, caches and service tasks.
- Engineer the fabrics: Allocate scale-up, scale-out, storage and management networks, then validate adapters, switch ports and cabling as one system.
- Model storage traffic: Measure dataset reads, metadata operations, checkpoint writes and archive retrievals before selecting local, file and object tiers.
- Calculate rack and facility loads: Include compute, switches, storage, power conversion, pumps, chillers and controls; do not count only GPU board power.
- Select heat rejection: Choose air, hybrid or liquid cooling from rack density and climate, then design coolant, leak detection and service procedures.
- Set resilience targets: Specify redundant power paths, generator and UPS behavior, spare parts, maintenance windows and workload recovery objectives.
- Integrate operations: Connect IT and facility telemetry, secure management access, automate provisioning and test failure scenarios before production.
Common mistakes and how to avoid them
- Buying GPUs first: A GPU count without host, fabric, storage and cooling budgets produces stranded capacity. Approve a complete rack bill of materials instead.
- Underestimating storage: Capacity alone does not reveal throughput or metadata limits. Test the number of concurrent readers and checkpoint writers the cluster will create.
- Mixing incompatible links: Rated switch bandwidth is irrelevant if adapters, optics, cables or firmware cannot operate together. Qualify the full path.
- Ignoring service access: Dense trays, manifolds and cable bundles must be replaceable without taking down unrelated racks. Include spares and documented procedures.
- Assuming backup generators solve every outage: Generator redundancy does not replace UPS ride-through, transfer coordination, fuel planning or an application recovery strategy.
- Treating cooling as a room problem: Pumps, chillers, controls and heat exchangers consume power and need monitoring. Give them dedicated electrical and maintenance plans.
- Putting management on the production fabric: An in-band outage should not remove the ability to power-cycle, reimage or diagnose a node. Keep an independent out-of-band path.
What a complete non-GPU bill of materials contains
At minimum, list host CPUs and system memory; boot and local NVMe drives; scale-up switches and links; scale-out adapters, switches and cabling; shared file and object-storage capacity; racks, rails, power shelves and stiffeners; electrical service, conversion, distribution and monitoring; cooling loops and heat-rejection equipment; out-of-band controllers and switches; telemetry, provisioning, firmware and orchestration systems; and the spares and test equipment needed to operate them.
The exact quantities should come from workload measurements and facility limits. The GB200/GB300 figures—two Grace CPUs with up to 1 TB LPDDR5 per compute tray, up to eight NVMe data drives plus one boot drive, and a rack pattern of 18 compute trays with nine NVLink switch trays—show how tightly these layers are integrated. They are reference architecture values, not a shopping list for every site.
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