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The right AI data-center partner ecosystem is the one that can prove a complete, operable design for your workloads and site—not the one with the longest partner list or the most impressive certification logo. Start with workload, scale, latency, data location, tenancy and ownership requirements. Then require every proposer to show how compute, networking, storage, software, facilities, deployment and support work together under the same assumptions.
Use reference architectures and certifications as evidence with a defined scope. They can show that a configuration was reviewed or tested, but they do not prove that it fits your building, utility limits, operating model or growth plan. A defensible decision comes from comparable workload results, site-specific engineering, explicit interface ownership and a support model that remains clear after commissioning.
1. Define the workload before you name partners
Write a short workload and ownership brief before inviting proposals. NVIDIA’s reference-architecture documentation separates designs by workload and deployment need and notes that real deployments may combine configurations for different requirements. That principle applies regardless of which suppliers you eventually select.
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State whether the facility will train foundation or fine-tuned models, serve real-time or batch inference, run analytics, support traditional HPC, or host a mixture. Training generally stresses sustained accelerator utilization, high-bandwidth scale-out communication and checkpoint storage. Inference adds latency, quality-of-service and tenant-isolation requirements. Analytics and HPC may need different CPU, memory, filesystem and scheduling characteristics. A mixed environment may need separate node pools or policies rather than one compromise configuration.
Scale, latency and utilization
Specify the initial accelerator count, expected job sizes, target response times, concurrency, utilization range and growth horizon. Distinguish a single-node proof of concept from a production cluster that must schedule multi-node jobs. Ask proposers to state the measurement method, software versions, precision, batch size and dataset used for every performance result. A throughput number without those conditions cannot be compared fairly.
Data location, sovereignty and tenancy
Document where training data, model artifacts, logs and backups may reside. Include jurisdiction, encryption, retention and cross-border-transfer constraints. Explain whether departments, customers or external tenants share the cluster. Shared tenancy affects network segmentation, identity integration, scheduler policy, noisy-neighbor controls and chargeback telemetry. If regulated workloads require dedicated hardware or physically separate storage, make that a design input rather than a later contract amendment.
Integration and ownership requirements
List identity providers, data platforms, orchestration systems, security tools, existing networks, facilities-management systems and ticketing processes that the cluster must integrate with. Decide what your team will operate: firmware, drivers, Kubernetes or another scheduler, storage, network fabric, monitoring, backups, security response and capacity planning. The partner must price and staff the remaining responsibilities; “supported” is not the same as “operated for you.”
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Request a bill of materials (BOM), topology diagrams, dependency list and validated deployment procedure. NVIDIA describes reference architectures as covering compute, networking, storage, software and deployment guidance; use that breadth as the minimum scope for proposal reviews. A server quote alone is not an AI-factory design.
Compute and node pattern
Check accelerator model and count per node, CPU and memory balance, local NVMe, PCIe or equivalent interconnects, boot devices, redundant power supplies, management controllers and rack-unit allocation. Ask whether the design is optimized for scale-up within a node, scale-out across nodes, or both. Confirm firmware, driver and accelerator-software compatibility for the exact configuration. If a supplier proposes a certified data-center GPU server, treat it as one physical component and verify its fit with your network, storage, power, cooling, facility and support requirements.
Networking topology
Separate the management, storage, tenant and high-performance fabric roles. Require a diagram showing leaf-spine or other topology, uplink speeds, oversubscription, path redundancy, congestion control, collective-communication support, optics and cable lengths. Identify where scale-up links terminate and which switches, adapters and firmware are validated together. Ask for failure behavior: what happens when a link, switch, rail or host fails, and how quickly can jobs resume?
Storage and data paths
Map source data ingress, training reads, checkpoint writes, model-registry access, inference serving and backup or archive flows. Specify capacity, usable versus raw space, throughput, IOPS, metadata performance, latency, durability, encryption and expansion method. Confirm protocol and client compatibility with the selected operating system, orchestrator and accelerator software. Require a recovery test that demonstrates how a failed device, controller or namespace affects running jobs.
Software, control plane and management
Request the exact operating-system images, accelerator drivers, container runtime, libraries, schedulers, orchestration components, registries, identity integration and policy controls. Include version pinning and an upgrade matrix. The control plane should cover provisioning, inventory, firmware, configuration drift, secrets, certificates, quotas and tenancy. Clarify which components are open source, which require subscriptions and who owns vulnerability remediation.
Deployment and observability
Ask for an automated build or infrastructure-as-code workflow, preflight checks, acceptance tests and rollback steps. Observability should expose accelerator utilization and errors, memory health, temperatures, power, network counters, storage latency, scheduler queues, job health and facility telemetry. Define retention, alert routing and access permissions. A dashboard that cannot correlate a failed job with a switch port, storage path or cooling alarm is incomplete.
Support boundaries
Put the escalation chain in writing. Identify the prime contractor, first-line help desk, hardware and software specialists, facilities contacts and the party coordinating a cross-vendor incident. Define severity levels, response and restoration targets, parts logistics, on-site coverage, maintenance windows and end-of-life notice periods. Ask who is accountable when a driver, firmware, switch and storage combination is not explicitly listed in a support matrix.
| Design layer | Evidence to request | Questions that expose gaps |
|---|---|---|
| Compute | Exact BOM, node diagram, firmware and driver matrix, workload results | Which configuration produced the result, and how does it scale when nodes or tenants are added? |
| Network | Physical and logical topology, bandwidth and redundancy calculations, failure tests | Are scale-up, scale-out, storage and management paths separated and monitored? |
| Storage | Capacity and performance model, protocol compatibility, recovery procedure | What are checkpoint and metadata latencies at peak concurrency? |
| Software | Version-locked stack, security controls, upgrade and rollback plan | Who validates upgrades across the full dependency chain? |
| Deployment | Automation, acceptance tests, runbooks and observability dashboards | Can another engineer rebuild a node or replace a switch without undocumented steps? |
| Support | RACI, service levels, escalation tree and lifecycle policy | Who owns a fault that crosses server, fabric, storage and facilities boundaries? |
3. Interpret certifications and reference architectures precisely
NVIDIA describes its program this way: “NVIDIA Reference Architectures provide clear, consolidated cluster design guidance for NVIDIA global system partners and enterprise customers building Enterprise AI Factories.” That is a description of the program’s purpose, not a guarantee that any published design fits your project. Read the scope, revision, components and deployment assumptions at NVIDIA Reference Architectures and Introducing NVIDIA Reference Architectures.
What a design review can establish
NVIDIA’s Design Review Board process publishes passed partner designs with configuration and scale information. A passed review is useful evidence that the qualifying design follows applicable NVIDIA patterns at the stated scope. Verify the exact hardware, software revisions, node count and topology in the published design. A partner may have several reviewed configurations, and your proposed configuration may combine elements that were not reviewed together.
Rank #3
- Number of Processor Cores: 512
- Processor Core Clock: 1.3GHz
- Memory Clock: 1.8GHz
- Memory Size: 6GB
- Peak single precision floating point performance: 1331 Gigaflops
What a certified system can establish
The NVIDIA Certified Systems program describes functional, security and performance testing, including single-GPU, multi-GPU and multi-node tests. Ask for the certification entry for the exact product and configuration, the test categories covered, the software version and any constraints. Certification indicates that defined tests passed; it does not establish your application’s throughput, energy efficiency, facility compatibility or service quality.
Questions to ask about every badge
- What exact model, accelerator count, network adapters, switches, storage and firmware were tested or reviewed?
- Which software versions, drivers, datasets, batch sizes and job patterns were used?
- Is the result for a single node, a fixed cluster size or a scalable architecture?
- What is excluded—facilities, external storage, tenant isolation, security operations or managed support?
- Has the published design been revised, retired or superseded?
4. Treat the facility as part of the AI system
Power, cooling, heat rejection, controls, connectivity, rack arrangements and expansion plans must be designed with the compute, not after it. NVIDIA’s DSX Facilities Infrastructure Reference Design Overview explicitly addresses this co-design and frames the site for phased expansion. The broader NVIDIA DSX documentation is a useful reference, but neither document substitutes for project-specific engineering.
Power and electrical distribution
Obtain a utility-capacity study covering incoming service, transformers, switchgear, distribution voltage, rack power density, power-quality limits, grounding, redundancy and maintenance bypass. Include backup generation, uninterruptible power, fuel autonomy, transfer behavior and generator testing. Model both steady-state and transient demand; accelerator clusters can create sharp load changes during job starts and failures. Confirm who supplies and certifies each electrical component.
Cooling and heat rejection
Determine whether the servers use air, direct liquid, rear-door heat exchangers or another method. Translate the cluster’s thermal design power into rack-level heat, room airflow, coolant flow, supply and return temperatures, water quality, leak detection and heat-rejection capacity. Check whether the building can support the selected technology and whether maintenance can occur without taking the whole cluster offline.
Controls, connectivity and rack layout
Define interfaces among building-management systems, computerized maintenance systems, environmental sensors, network demarcation points and security controls. Reserve space for cable trays, patch panels, manifolds, leak sensors, service clearances and spares. Validate cable lengths and bend radii against the proposed rack positions. Connectivity planning must include carrier diversity, demarcation, out-of-band management and physical security.
Phased expansion
Design the first phase so later racks, power feeds, cooling loops, network pods and storage shelves can be added without redesigning the live cluster. Identify trigger points for utility upgrades, additional chillers or heat-rejection equipment. Require a sequence-of-operations document for each expansion phase and a plan for maintaining production service during construction.
Rank #4
- Memory Size: 16 GB GDDR6 ECC.
- Memory Bus Width: 128-bit.
- Memory Bandwidth: 200 GB/s.
- CUDA Cores: 1280.
- Peak Single Precision floating point performance: 18 Tflops (GPU Boost Clocks).
Assign every interface
Do not turn a reference design into a site-specific engineering promise. Obtain studies and stamped designs from qualified providers and create an interface register.
| Interface | Owner to name in the contract | Acceptance evidence |
|---|---|---|
| Utility to facility | Electrical engineer or facilities contractor | Capacity, protection, grounding and energization tests |
| Facility to rack | Facilities integrator and system integrator jointly | Power, cooling, leak, airflow and environmental readings at design load |
| Rack to server | System supplier | Install, burn-in, firmware and thermal acceptance reports |
| Server to network | Network integrator | Link, latency, congestion and failover tests |
| Cluster to storage | Storage and AI integrators | Throughput, metadata, checkpoint and recovery tests |
| All systems to operations | Prime contractor | Unified monitoring, alarms, runbooks and escalation exercise |
5. Map the ecosystem and operating boundaries
Build a role map before comparing brands. NVIDIA’s Ecosystem Architecture describes partners contributing infrastructure options, services, software integrations, deployment expertise and industry solutions. Your map should show which party supplies, integrates, commissions and supports each layer.
| Role | Deliverables | Boundary to clarify |
|---|---|---|
| Accelerator and server supplier | Nodes, firmware, parts, burn-in and hardware support | Does support include the selected network, storage and software versions? |
| Network supplier | Adapters, switches, optics, topology and fabric operations | Who diagnoses a collective-communication fault spanning servers and switches? |
| Storage supplier | Data platform, performance tuning, replication and recovery | Who guarantees checkpoint behavior under cluster load? |
| AI software provider | Drivers, libraries, orchestration, security and model-serving components | Who validates upgrades and handles vulnerabilities? |
| Facilities provider | Power, cooling, controls, racks, connectivity and compliance | Who owns alarms and outages that throttle or shut down compute? |
| System integrator | Architecture, installation, automation, testing and documentation | Is it the prime contractor with authority to coordinate others? |
| Operations and managed services | Monitoring, incident response, patching, capacity and lifecycle work | Which tasks remain with your staff, and during what hours? |
Ask for one cross-vendor incident exercise before acceptance. For example, simulate a host losing network connectivity while a storage controller reports elevated latency and a rack cooling alarm appears. The exercise should demonstrate detection, ownership, escalation, mitigation, communications and post-incident analysis.
6. Compare proposals on common assumptions
Give every bidder the same workload definition, cluster scale, facility envelope, network and storage assumptions, support period and growth scenario. Require a response template so a lower price cannot hide missing scope.
Use an evidence-based scorecard
| Criterion | Comparable evidence | Decision question |
|---|---|---|
| Workload performance | Reproducible benchmark logs and application results | Does the design meet latency, throughput and utilization targets at the stated scale? |
| Delivery risk | Lead times, dependencies, site readiness and contingency plan | What can delay energization, installation or acceptance? |
| Power and cooling | Rack load, thermal model, utility and heat-rejection requirements | Can the existing or planned facility sustain the cluster and its expansion? |
| Operability | Automation, telemetry, runbooks, staffing and maintenance model | Can your team detect, isolate and recover faults? |
| Security and compliance | Identity, segmentation, encryption, logging and audit evidence | Does the design satisfy data-location and tenant obligations? |
| Lifecycle cost | Acquisition, facilities, software, support, energy, parts and refresh assumptions | What will the complete five-year operating model cost under stated utilization? |
| Support quality | Named specialists, service levels, escalation and references for similar scope | Who is accountable after the integrator leaves? |
The reviewed NVIDIA materials do not provide an independent, apples-to-apples ranking of vendors for performance, lifecycle cost, energy or service quality. Do not publish or accept a universal league table. Obtain comparable evidence for your own workload and site, and record assumptions beside every score.
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- Write the requirements brief. Record workload types, accelerator scale, latency, utilization, data sovereignty, tenancy, integrations, availability targets, growth and internal operating responsibilities.
- Freeze the site envelope. Document utility capacity, rack space, allowable power density, cooling and heat rejection, connectivity, security constraints, commissioning dates and expansion limits.
- Issue a common request. Require a full BOM, topology, software matrix, facilities design, deployment plan, acceptance tests, support RACI and lifecycle-cost model.
- Validate credentials. For each certification or reference architecture, capture the exact product, configuration, revision, scale and test or review scope. Use the reference-architecture catalog and the Certified Systems listings as primary evidence where applicable.
- Run a design workshop. Put the server, network, storage, software, facilities and operations leads in one session. Trace a training job, an inference request, a checkpoint and a failure through every layer.
- Test representative workloads. Use your data or a data set with equivalent characteristics. Capture throughput, latency, utilization, error rates, power, thermal behavior and recovery time, with software versions recorded.
- Perform site and interface reviews. Check electrical studies, cooling calculations, rack drawings, cable plans, controls integration and phased-expansion sequence. Assign an owner and acceptance test to each interface.
- Negotiate the operating contract. Attach the final BOM and version matrix, define service levels and escalation, require change control, specify spare parts and end-of-life notice, and state who coordinates cross-vendor incidents.
- Accept in stages. Gate utility and cooling readiness, hardware burn-in, fabric tests, storage tests, software validation, security checks, workload acceptance and operations handover. Do not declare production readiness from a server power-on alone.
8. Common failure modes and how to correct them
Choosing by certification alone
Symptom: A proposal leads with a badge but cannot show your workload, site assumptions or support boundaries. Correction: Record the badge’s exact scope, then require project-specific performance, facilities and operational evidence.
Buying nodes before planning the building
Symptom: Servers arrive before utility upgrades, cooling loops, racks or network demarcation are ready. Correction: Make facilities design, procurement dependencies and commissioning gates part of the same program plan.
Benchmarking an isolated component
Symptom: A GPU or storage result looks strong, but end-to-end jobs miss targets. Correction: Test the complete path—data ingest, network fabric, storage, scheduler, accelerators, checkpointing and monitoring—at representative concurrency.
Leaving the prime contractor undefined
Symptom: Each supplier redirects a multi-layer incident to another party. Correction: Name one coordinating party, publish the escalation tree and rehearse a cross-vendor failure before acceptance.
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Ignoring version drift
Symptom: An upgrade changes driver, firmware or library behavior and invalidates previous results. Correction: Maintain a locked compatibility matrix, a staging cluster or maintenance window, regression tests and rollback images.
Underestimating expansion
Symptom: The first phase works, but additional racks require new switch pods, feeders, chillers or construction outages. Correction: Model the next growth phases now and reserve physical, electrical, thermal and network capacity with trigger dates.
9. What a defensible decision looks like
Select the ecosystem that can connect its claims to your stated workload and site. The decision record should include the common assumptions, complete BOM, architecture revision, benchmark conditions, facilities studies, interface register, acceptance gates, support RACI, software matrix and expansion plan. It should also list exclusions and unresolved risks rather than hiding them in marketing language.
A partner’s certification or reference-architecture approval is valuable when it narrows technical uncertainty. It becomes decision-grade only when the exact configuration is tied to reproducible tests, site-specific engineering and an operating contract. If two proposals cannot be compared under the same assumptions, the next step is not a ranking—it is to close the evidence gaps.
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