Evaluate the ecosystem from the workload and site outward, not from a server quote inward. A suitable design connects accelerators, CPUs, scale-up and scale-out networking, storage, AI software, facilities, deployment, and operations under one set of assumptions. Vendor certifications and reference architectures are useful evidence about a defined configuration; they are not proof that the same design fits your models, facility, latency target, sovereignty rules, or operating team.
What an AI infrastructure ecosystem includes
A custom AI data center is a system of interdependent suppliers rather than a collection of isolated products. The ecosystem may include:
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- Server and accelerator suppliers for GPU or other AI compute, CPUs, memory, local storage, and rack integration.
- Network suppliers for the fabric between accelerators, storage, management systems, and external users.
- Storage suppliers for training data, checkpoints, feature data, logs, backup, and archival retention.
- AI software suppliers for drivers, containers, orchestration, model platforms, security, and developer tooling.
- Facilities engineers and equipment suppliers for utility power, electrical distribution, backup generation, cooling, heat rejection, controls, connectivity, racks, and fire and safety systems.
- A system integrator or prime contractor that turns the separate designs into a commissioned cluster.
- Operations and support providers responsible for monitoring, incident response, security updates, capacity expansion, and lifecycle replacement.
NVIDIA describes its reference architectures as “clear, consolidated cluster design guidance for NVIDIA global system partners and enterprise customers building Enterprise AI Factories.” That is NVIDIA’s description of its own program, not an independent market ranking. Read the architecture material at NVIDIA Reference Architectures and the accompanying program overview as a way to structure questions, then validate every interface against your project.
1. Write the workload and ownership brief first
Give every prospective partner the same written brief. Without it, each bidder optimizes a different problem and their prices, performance claims, and facility assumptions cannot be compared.
Classify the work
- Training: emphasizes accelerator-to-accelerator bandwidth, synchronized job performance, checkpoint throughput, and predictable scheduling.
- Inference: emphasizes latency, throughput per request, model-serving reliability, batching behavior, and isolation between services.
- Analytics or HPC: may require a different balance of CPU, memory, accelerator, storage, and interconnect resources.
- Mixed tenancy: needs placement, quotas, isolation, and service-level policies so one workload cannot consume the entire fabric.
State whether the first deployment is dedicated to one model, shared by teams, or intended to change over time. A design for occasional fine-tuning has different utilization and scheduling requirements from a continuously busy inference service.
Specify scale and performance targets
Document the initial and planned number of nodes, accelerators per node, expected utilization, model sizes, data-set sizes, checkpoint frequency, job duration, concurrent users, and target growth. For inference, provide a latency percentile and throughput target rather than only an average response time. For training, define the useful metric—such as time to a target quality or tokens processed per second—and the measurement conditions.
Ask bidders to state whether a result is measured on your model and data shape, a representative benchmark, or a theoretical specification. Require the same workload, precision, batch size, software versions, and success criteria for every proposal.
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- List data-residency, sovereignty, retention, encryption, and cross-border transfer requirements.
- Identify latency limits between users, the cluster, storage, and any external services.
- Describe identity, directory, ticketing, secrets, logging, security-monitoring, and backup systems that must integrate with the cluster.
- Record tenancy rules, maintenance windows, change-control requirements, and any workloads that cannot be interrupted.
NVIDIA’s planning material distinguishes designs by workload and deployment need and notes practical constraints such as space, power, and cooling. Use that principle even when the final stack includes products from several ecosystems; the brief is the common contract for comparison.
Assign the operating model
Decide what your team will own on day one: hardware replacement, driver and firmware updates, cluster scheduling, security response, model serving, data protection, and 24-hour incident coverage. A partner’s proposal should identify the skills, staffing, runbooks, and access it assumes. If the design requires a capability your team does not have, price the managed service or training explicitly instead of leaving it as an unpriced risk.
2. Evaluate the complete technical design
Request a bill of materials, topology diagrams, software bill of materials, dependency matrix, and deployment procedure—not just a list of server models. NVIDIA’s reference-architecture materials cover compute, networking, storage, software, and deployment guidance; use that same whole-system scope when reviewing a non-NVIDIA proposal.
Compute and node pattern
Verify the accelerator type and quantity, CPU model and count, host memory, local NVMe capacity, PCIe or equivalent interconnect layout, power draw, and serviceability. Ask how GPUs are connected within a node and how a failed accelerator, host, power supply, or fabric adapter is isolated without taking down unrelated jobs.
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Scale-up and scale-out networking
Separate the high-bandwidth fabric used for collective accelerator work from the management, storage, and user networks. The proposal should show link speeds, port counts, oversubscription, topology, routing, congestion controls, redundancy, and switch placement. Ask how the design behaves when a link, switch, transceiver, or rail fails, and whether maintenance can be performed without a full-cluster outage.
Have the partner demonstrate the communication pattern that matters to you—such as distributed training collectives or east-west inference traffic—rather than citing a switch’s line rate alone. Require telemetry for link errors, congestion, packet drops, and fabric health, with retention long enough to diagnose intermittent failures.
Storage and data movement
Map each data class to a storage tier: hot training data, scratch space, checkpoint storage, model registry, logs, backup, and archive. Specify usable capacity, read and write throughput, metadata performance, latency, durability, replication, encryption, and recovery objectives. Include the path from storage to each node; a fast array cannot compensate for insufficient host links or a congested fabric.
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Software, control plane, and security
List the operating system, accelerator drivers, firmware, container runtime, orchestration layer, schedulers, AI frameworks, model-serving components, identity integration, vulnerability scanning, secrets handling, and license terms. Require a version-compatibility matrix showing which combinations were validated together and who owns updates when one component changes.
Define separate management and tenant privileges. The design should cover secure boot or equivalent platform controls, network segmentation, key management, audit logs, image signing, vulnerability response, and secure decommissioning. Ask whether telemetry leaves the facility and how data is protected in transit and at rest.
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
Provisioning, observability, and deployment
Prefer repeatable infrastructure-as-code or an equivalent documented process over manual node-by-node configuration. The delivery package should include golden images, firmware procedures, cluster registration, scheduler configuration, health checks, rollback steps, and acceptance tests.
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Observability must span hardware and software: temperature, power, accelerator utilization and memory errors, CPU and memory pressure, storage latency, fabric counters, job health, scheduler queues, and service-level metrics. Establish alert thresholds, escalation paths, and log ownership before acceptance. A dashboard without an on-call process is not operational coverage.
3. Treat facility readiness as a first-order design input
High-density AI equipment can invalidate an otherwise sound IT design if power delivery, cooling, heat rejection, or space is assumed rather than engineered. Plan the facility and compute together. NVIDIA’s DSX Facilities Infrastructure Reference Design Overview explicitly addresses co-design of power, cooling, controls, connectivity, and compute, including phased expansion. The NVIDIA DSX documentation provides the broader facilities context.
Power and electrical distribution
- Document utility capacity, service voltage, transformer and switchgear limits, rack power density, branch-circuit design, power-quality requirements, and metering.
- Specify uninterruptible-power and generator coverage, fuel autonomy, transfer behavior, maintenance bypass, and the sequence for controlled shutdown.
- Reserve capacity for network, storage, cooling, controls, lighting, and future racks; do not size only for the first server shipment.
- Define who supplies short-circuit, coordination, arc-flash, grounding, and code studies and who signs off the installation.
Cooling and heat rejection
Identify whether the design uses air, direct liquid, rear-door heat exchangers, or a hybrid approach. Record supply and return temperatures, flow, water quality, leak detection, redundancy, cooling-tower or dry-cooler limits, and the effect of seasonal conditions. Match the cooling design to the actual rack heat load and expansion stages; a generic “high-density ready” statement is not an engineering calculation.
Require a control sequence for startup, failure, maintenance, and emergency shutdown. Clarify the boundary between the facilities controls system and IT monitoring, including alarm ownership and time synchronization.
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Racks, connectivity, and physical layout
Check rack dimensions, weight loading, cable pathways, overhead or underfloor distribution, hot- and cold-aisle strategy, fire suppression, service clearances, delivery routes, staging space, and spare-parts storage. Confirm that network fiber and power paths are physically diverse where the availability target requires it.
Expansion and commissioning
Divide the build into explicit phases with reserved utility, cooling, floor, network, and control capacity. Require factory tests, site acceptance tests, burn-in, thermal and electrical validation, fabric tests, storage tests, failure-injection scenarios, and documented acceptance criteria. A reference design can inform the plan, but qualified providers must produce project-specific studies, drawings, permits, and responsibility matrices.
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).
4. Interpret certifications and reference architectures precisely
A certification or design-review result is evidence with a scope. It becomes useful only when the published configuration, software revision, test method, and scale match your proposal.
Design-review evidence
NVIDIA describes a Design Review Board process and publishes partner designs that pass applicable reviews, with configuration and scale information. Check the exact document revision, node count, accelerator configuration, network topology, storage assumptions, software versions, and date. Ask what was reviewed and what was outside the review: facility engineering, your data, your security controls, operations, or future expansion.
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NVIDIA’s Certified Systems page says certified systems undergo functional, security, and performance testing, including single-GPU, multi-GPU, and multi-node tests. Treat that as evidence about the exact certified product and configuration. It does not establish performance on your model, compatibility with every storage or orchestration choice, site readiness, lifecycle cost, or service quality.
Questions to ask about every badge
- What exact product, firmware, driver, accelerator count, and topology does the certification cover?
- Which tests were run, under what workload and duration, and what constitutes a pass?
- Does the proposed bill of materials match the certified configuration, or are there substitutions?
- Which support organization owns a failure that crosses server, fabric, storage, and facilities boundaries?
- What changes void the certification, and how are updates revalidated?
5. Map partner roles and support boundaries
Build a responsibility matrix before signing. NVIDIA’s Ecosystem Architecture describes partners contributing infrastructure options, services, software integrations, deployment expertise, and industry solutions. Your contract must turn those categories into named owners.
| Workstream | Evidence to request | Boundary that must be explicit |
|---|---|---|
| Compute and servers | Bill of materials, topology, thermal and power data, spare strategy, firmware policy | Who replaces failed parts and coordinates downtime with the scheduler? |
| Network | Fabric diagram, capacity model, redundancy, telemetry, validation results | Who diagnoses an issue spanning adapters, switches, drivers, and applications? |
| Storage | Usable capacity, performance tests, replication, backup and recovery procedures | Who owns data integrity and recovery-time commitments? |
| Software | Version matrix, security process, image and driver lifecycle, licenses | Who tests and approves upgrades across the complete stack? |
| Facilities | Electrical, cooling, controls, connectivity, rack and permit documents | Where does facilities responsibility end and IT responsibility begin? |
| Integration and commissioning | Runbooks, acceptance tests, training, as-built documentation | Who is the prime when a cross-domain test fails? |
| Operations | Service levels, on-call roster, monitoring, escalation and reporting | Who owns the incident clock and customer communications? |
Also ask which party can access telemetry, how support engineers authenticate, where logs are stored, and whether remote access can be disabled for sovereignty or security reasons.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.6. Compare proposals with common assumptions
Do not rank bids by accelerator count or purchase price. Issue one assumptions sheet and require every bidder to return deviations in a marked-up copy.
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| Comparison area | Common input | Evidence to compare |
|---|---|---|
| Workload | Same model, data shape, precision, batch or concurrency, and success metric | Measured throughput, latency percentiles, utilization, and test logs |
| Scale | Same initial cluster and growth scenario | Expansion steps, lead times, reserved capacity, and revalidation plan |
| Facility | Same available power, cooling method, rack envelope, and site conditions | Load calculations, heat rejection, utility studies, and commissioning scope |
| Network and storage | Same topology assumptions, data set, checkpoint rate, and recovery objectives | Fabric and storage test results, failure behavior, and telemetry coverage |
| Software and security | Same versions, identity controls, isolation, and update policy | Compatibility matrix, vulnerability process, audit evidence, and rollback plan |
| Support | Same coverage hours, response targets, parts model, and escalation rules | Named owners, service-level terms, staffing, and cross-vendor coordination |
| Lifecycle cost | Same support period, energy assumptions, refresh and expansion horizon | Itemized capital, facilities, software, labor, energy, spares, and exit costs |
The available NVIDIA materials do not provide an independent, apples-to-apples ranking of vendors for performance, energy, lifecycle cost, or service quality. Therefore, publish no universal “best partner” list. Obtain comparable evidence for your workload and site, and preserve the raw test conditions with the proposal.
7. Use a staged evaluation and acceptance process
- Issue the requirements brief. Include workload, scale, latency, data location, tenancy, integrations, site envelope, operating model, growth, and acceptance metrics.
- Run a design clarification round. Require topology diagrams, a complete bill of materials, software versions, facility assumptions, responsibility matrix, and deviations from the brief.
- Test a representative workload. Use the same model, data, precision, concurrency, and success criteria. Capture utilization, latency, throughput, power, thermal behavior, failures, and recovery.
- Review facility engineering. Obtain project-specific electrical, cooling, controls, connectivity, structural, code, and permit deliverables from qualified providers.
- Validate operations. Walk through provisioning, monitoring, patching, incident escalation, spare replacement, backup restoration, security response, and expansion.
- Contract the interfaces. Make acceptance tests, documentation, training, service levels, software support, change control, data handling, and cross-vendor escalation contractual obligations.
- Commission in phases. Accept factory, site, cluster, workload, and resilience tests separately so a passing server inspection cannot hide a failing facility or software integration.
8. Common failure modes and how to prevent them
Choosing the hardware before defining the job
Symptom: a large accelerator count but low utilization, queue contention, or missed latency targets. Prevention: make the workload brief and measured acceptance test prerequisites to the purchase decision.
Treating a reference architecture as a site design
Symptom: insufficient utility capacity, cooling shortfall, unplanned controls work, or a late rack-layout change. Prevention: commission site-specific studies and assign every interface between utility, facilities, servers, network, and software owners.
Assuming certification covers substitutions
Symptom: a proposal cites a certified system but changes the accelerator count, adapter, firmware, storage, or software revision. Prevention: reconcile the proposed bill of materials line by line with the certified configuration and demand revalidation for deviations.
Leaving the control plane and observability until the end
Symptom: the cluster runs a demo but operators cannot identify a bad link, failing accelerator, storage stall, or software regression. Prevention: make telemetry, alert ownership, log retention, runbooks, and rollback tests part of acceptance.
Creating a support gap between vendors
Symptom: server, network, storage, and facilities suppliers each declare their component healthy while the workload remains down. Prevention: name a prime incident coordinator, define response clocks, provide shared telemetry, and require joint failure exercises.
Ignoring the growth path
Symptom: the first phase works but later racks require new switch tiers, transformers, cooling equipment, or software architecture. Prevention: reserve physical and utility capacity, document expansion steps, and price the validation and outage implications of each phase.
9. A decision rubric for the final shortlist
Score each proposal only against the same evidence package. A practical rubric gives the greatest weight to workload fit and operational risk, then checks facility and lifecycle feasibility.
- Workload fit: measured results on the target model or a defensible representative workload.
- System completeness: coherent compute, fabric, storage, software, deployment, and observability design.
- Facility feasibility: verified power, cooling, controls, connectivity, rack, safety, and expansion deliverables.
- Operational ownership: named prime, escalation path, staffing, telemetry access, update policy, and spares.
- Evidence quality: exact configurations, test conditions, revisions, acceptance criteria, and documented deviations.
- Lifecycle economics: transparent capital, energy, support, labor, refresh, expansion, and exit assumptions.
Record unresolved assumptions as commercial and technical risks with an owner and due date. Do not convert a missing value into a favorable score; mark it as not established and require evidence before award.
What a strong final proposal should contain
Before selecting a partner ecosystem, insist on one integrated package containing:
- A requirements traceability matrix linking every stated need to a design element and an acceptance test.
- A complete bill of materials with exact part numbers, revisions, quantities, spares, licenses, and support terms.
- Compute, network, storage, security, software, facilities, rack, cabling, and controls diagrams.
- Measured workload results with conditions, raw metrics, utilization, power, thermal behavior, and failure outcomes.
- Project-specific engineering studies, permits, commissioning plan, phased expansion plan, and as-built documentation.
- A responsibility and escalation matrix covering every cross-vendor interface.
- Operations runbooks for provisioning, monitoring, patching, backup, restore, incident response, component replacement, and rollback.
- A lifecycle cost model and a change-control process for substitutions, upgrades, and capacity additions.
The best ecosystem is therefore not the one with the most impressive badge or the largest nominal cluster. It is the one that demonstrates, under your workload and facility assumptions, how the entire system will perform, be powered and cooled, be secured and observed, be commissioned, and be supported when a component or software layer fails.
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