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Buy an inference cluster only after you can describe the workload it must serve, the service level it must meet, and the site in which it will run. Accelerator count is not a specification: model memory behavior, CPU and host memory, PCIe topology, network paths, storage, software versions, power, cooling, and support all determine whether the system delivers usable throughput at the required latency and quality. Use a written bill of materials, a site-readiness review, and an acceptance test on your own model and traffic before committing.
1. Define the serving workload and service level
Start with an inventory that a supplier can turn into a test plan. NVIDIA’s configuration guidance says application workload, datasets, models, and use case affect hardware selection, and advises buyers to discuss specific needs with a provider. Its exact wording is: “Please discuss specific workload requirements with your provider to ensure your solution will meet your business needs.” NVIDIA-Certified Systems Configuration Guide
Record the model behavior
- Model name, release, parameter size, architecture, tokenizer, and any retrieval, ranking, vision, audio, or tool-calling components.
- Precision and quantization options you will permit, together with the minimum quality or accuracy that must be retained.
- Maximum and typical input (prompt) length, output length, context-window requirements, and whether requests have predictable or highly variable sizes.
- Weights, tokenizer files, adapters, retrieval indexes, and other data that must remain in accelerator memory, host memory, or local storage.
- Whether a warm key-value cache, model cache, or persistent worker state is expected. A cold start and a warm steady state can have very different latency and memory behavior.
Write down the service level
| Requirement | What to specify | Why it changes the cluster |
|---|---|---|
| Concurrency | Normal, peak, and burst request counts; queueing policy; batch limits | Determines how many requests share memory and compute and whether more replicas are preferable to larger nodes. |
| Latency | Time-to-first-token, per-token latency, end-to-end latency, and the tail percentile you will enforce | A system that posts high average throughput can still violate a customer-facing tail-latency target. |
| Throughput | Tokens per second, requests per second, images or documents per second, and the measurement window | Allows proposals to be compared at the same load instead of at an isolated peak. |
| Quality | Accuracy, pass rate, human-evaluation threshold, or task-specific acceptance test | Prevents a faster quantized or partitioned model from being accepted when it no longer meets the product requirement. |
| Availability | Maintenance window, failure tolerance, recovery time, and data-retention constraints | May require replicas, spare capacity, redundant network paths, or a different deployment shape. |
| Growth | Expected users, requests, context length, model changes, and expansion date | Determines whether you need a larger initial fabric, spare rack capacity, or a modular scale-out plan. |
Give every supplier the same workload sheet. Otherwise, each proposal may optimize a different prompt length, batch size, cache state, or quality setting and the resulting numbers will not be comparable.
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Do not infer deployment size from model parameter count alone. Test the actual model, serving software, prompt/output mix, and concurrency. Some inference workloads fit on CPUs; others benefit from one accelerator-equipped server; larger models, higher request volume, or tighter latency targets can justify multiple servers. AMD’s educational guidance presents this CPU-versus-accelerator framing but labels its results as configuration-dependent, so use it as a benchmark question rather than a universal model-size rule. AMD EPYC inference infographic
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CPU-only baseline
Run a CPU baseline when the model and target concurrency plausibly fit in host memory and the latency objective is flexible. Measure the same quality and traffic conditions you will use for accelerators. A CPU result gives you a fallback and reveals whether the proposed accelerator is solving a real bottleneck or merely increasing peak capacity you will not consume.
Single-node acceleration
A single server is often simpler to operate: fewer network hops, less distributed-serving complexity, and a smaller failure domain. Confirm that the complete model, runtime workspace, cache, and concurrency headroom fit in the available accelerator and host memory. If a model is split across devices, measure the communication overhead rather than assuming that adding devices produces linear gains.
Multi-node serving
Scale out when one node cannot provide the required memory, throughput, or availability, or when independent replicas are the cleanest way to meet concurrency. Multi-node designs make east-west GPU traffic, fabric topology, storage paths, failure handling, and software compatibility first-class requirements. Ask the supplier to show where model partitions, collectives, request routing, and storage transfers travel.
3. Audit the complete server bill of materials and topology
Accelerator quantity is only one line in the design. CPU sockets, cores, memory channels, PCIe lanes, root ports, NIC placement, local storage, firmware, and management interfaces can starve an otherwise powerful device. The following values are a concrete reference checklist from NVIDIA’s inference-server example, not vendor-neutral minimums. Verify them against the exact accelerator, server, runtime, and workload proposed. NVIDIA configuration guide
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| Area | NVIDIA example recommendation | Questions for the supplier |
|---|---|---|
| Accelerators | 2, 4, or 8 GPUs per server, balanced across CPU sockets and PCIe root ports | Are devices evenly attached to the intended sockets? What topology does the runtime report? |
| CPU | PCIe Gen5-capable CPUs (or later when available), at least two sockets, at least 2.0 GHz base clock, and at least six physical CPU cores per GPU | Which cores are reserved for request handling, preprocessing, networking, and system services? |
| Host memory | At least twice aggregate GPU memory, distributed across sockets and memory channels | Is the memory capacity and bandwidth sufficient for the model, cache, preprocessing, and failover headroom? |
| PCIe | GPU slots must match the specific GPU’s required PCIe speed and lane width | Which root port owns each GPU and NIC? Are any devices sharing a constrained switch or slot? |
| Networking | At least 200 Gbps for multi-node inference and up to 400 Gbps per GPU in the cited guidance | Are these link rates required by measured traffic, and are NICs, switches, optics, and cables included? |
| Local storage | One NVMe drive per CPU socket, minimum 1 TB | What data is local, what is remote, and is the proposed drive server-qualified for the workload and endurance? |
| Management and security | Redfish 1.0 or greater for remote management and TPM 2.0 for secure boot | Can your tools provision, monitor, update, attest, and recover every node without physical access? |
Check memory headroom, not just capacity
Calculate memory for weights, runtime workspace, activations, key-value cache, batching, communication buffers, operating-system services, and a safety margin. Ask the vendor to demonstrate the largest supported context and the target concurrency with the required quality setting. A configuration that barely fits the weights can fail when prompts lengthen or a second model is loaded.
Map devices to sockets and root ports
Request a topology diagram and the output of the platform’s device-discovery tools. Confirm that GPU-to-GPU paths, GPU-to-NIC paths, and CPU-to-storage paths use the intended links. A nominally identical server can behave differently when a NIC is placed behind a congested switch or a device is attached to the remote CPU socket.
4. Design networks by traffic type
One “network speed” number hides several separate flows. NVIDIA’s reference-architecture guidance distinguishes east-west communication between compute nodes, north-south customer and storage connectivity, storage data movement, customer uplinks, and management or support services. NVIDIA reference-architecture design tenets
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|---|---|---|
| East-west | GPU collectives, model partitioning, replication, and node-to-node serving traffic | Measure bandwidth, latency, congestion behavior, and oversubscription at the intended scale. |
| Customer or north-south | Ingress, egress, API traffic, and service-to-service calls | Separate customer bursts from accelerator synchronization where possible; document load-balancer behavior. |
| Storage | Model loading, checkpoints, datasets, logs, and cache movement | Show that storage transfers cannot leave accelerators idle. NVIDIA explicitly says storage networking should be sized for the workload. |
| Management | Provisioning, telemetry, firmware, security, and out-of-band recovery | Keep management available during data-plane incidents and specify isolation controls. |
The cited NVIDIA guide gives 200 Gbps as a minimum NIC recommendation for multi-node inference and up to 400 Gbps per GPU. Treat those as NVIDIA configuration examples, not universal requirements. Require a traffic model showing why the proposed rates, switch radix, topology, and oversubscription meet your measured workload.
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5. Validate the serving software and model settings
Hardware cannot compensate for an incompatible or poorly tuned software stack. Put these items in the proposal and acceptance test:
- Operating-system, driver, accelerator runtime, communication-library, container, orchestration, and serving-backend versions.
- Supported model format, tokenizer version, quantization method, batching and scheduling policy, and maximum context length.
- Use of graph compilation, kernel selection, tensor or pipeline parallelism, and CPU pinning. Require the supplier to state which settings are enabled rather than reporting an unexplained “optimized” result.
- Cold-start time, model-load path, image and weight-cache location, and behavior after a node or process restart.
- Observability: per-request latency, queue time, device utilization, memory use, network counters, storage throughput, errors, and throttling or thermal events.
Lock versions in the test record. A benchmark result without backend and software versions cannot be reproduced after an upgrade or compared fairly with another proposal.
6. Confirm facility readiness before ordering hardware
High-density inference equipment can require electrical, mechanical, and cooling work before it can be installed. NVIDIA’s data-center resource calls out power, cooling, architecture, rack layout, storage, and networking; its configuration guide notes that temperature, airflow, and hardware choices affect component temperature and workload performance. GPU-ready data-center resource NVIDIA configuration guide
Electrical and thermal checks
- Available utility and rack power, circuit voltage, phase balance, breaker capacity, UPS or generator limits, and measured peak as well as steady-state demand.
- Cooling method, supply-air temperature, return-air path, rack airflow direction, containment, and allowable inlet temperature. Obtain the server’s environmental limits for the exact configuration.
- Whether added racks require power distribution, chilled-water, rear-door heat exchangers, containment, or structural work, and who pays for and schedules that work.
Rack, access, and environment
- Rack units, weight, rail compatibility, door and elevator clearances, cable paths, and space for expansion.
- Humidity, dust, altitude, vibration, and other mechanical or environmental conditions specified by the OEM.
- Delivery route, staging area, installation responsibility, spare parts, and on-site response procedures.
A predefined scalable unit in a reference architecture is a planning pattern for topology, networking, power, cooling, rack layout, and expansion. It is not evidence that your site should begin at that size. Have the supplier map every assumption to your facility survey.
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7. Run a representative acceptance benchmark
Make purchase approval conditional on a test using your model and realistic traffic. A vendor peak score is useful only when its configuration and conditions match your service requirement. MLPerf’s foundational methodology paper explains why hardware and software variety make architecture-neutral, representative, reproducible comparison difficult and describes rules intended to support comparable results. MLPerf Inference Benchmark paper
- Freeze the workload. Supply representative prompts or inputs, output-length distribution, context lengths, concurrency levels, request mix, cache state, and quality checks. Include peak and failure-recovery scenarios.
- Define pass/fail gates. Set throughput, latency distribution, availability, error rate, and accuracy or quality thresholds before the test. State whether measurements include queueing, preprocessing, network transfer, and response delivery.
- Record provenance. Capture hardware part numbers, accelerator count, topology, firmware, operating system, drivers, backend and serving versions, model build, quantization, network mode, storage path, and all tuning parameters.
- Test several operating points. Run low, target, and peak concurrency; cold and warm cache; normal and maximum context; and at least one degraded or restart condition. Record how performance changes rather than selecting only the best point.
- Verify quality at speed. Compare outputs with the agreed accuracy or evaluation set. Reject a faster configuration that misses the quality gate.
- Repeat and sign the report. Have both parties retain raw results, scripts, logs, and timestamps. Tie the accepted configuration to the bill of materials and software versions in the contract.
8. Compare proposals on the same decision axes
| Axis | Evidence to request |
|---|---|
| Model fit | Memory map, context and quantization settings, quality results, and documented headroom. |
| Tail performance | Latency distribution and throughput at your target concurrency, not an isolated peak score. |
| End-to-end balance | CPU and memory utilization, PCIe and GPU topology, fabric counters, storage throughput, and serving configuration. |
| Site feasibility | Power, cooling, rack, airflow, environmental limits, installation work, and expansion space. |
| Operations | Monitoring, remote management, security controls, firmware process, warranty, replacement times, and support coverage. |
| Compatibility | Supported operating system, drivers, frameworks, model formats, orchestration, and security tooling. |
| Expansion | How additional nodes, switches, storage, power, and cooling are added without redesigning the service. |
| Commercial terms | Dated quotation, delivery date, installation scope, acceptance criteria, recurring support, and assumptions that could create extra site cost. |
No broadly applicable, vendor-neutral price, power-draw, total-cost, or cost-per-token figure is established for this purchase. Obtain dated, configuration-specific quotes and test results. Treat every benchmark score as scoped to the reported hardware, software, workload, and measurement conditions.
9. Use a supplier due-diligence checklist
- Has the supplier run your model, tokenizer, prompt/output mix, concurrency, cache state, and quality test?
- Does the written bill of materials identify every accelerator, CPU, DIMM, NIC, switch, optic, cable, NVMe drive, rail, firmware level, and support item?
- Does the topology diagram show CPU sockets, PCIe root ports, GPU placement, NIC placement, storage paths, and east-west links?
- Are the proposed network rates justified by measured traffic, with storage and management paths separated from customer traffic where appropriate?
- Are power, cooling, airflow, rack, environmental, delivery, and installation assumptions signed by the facility owner?
- Are Redfish remote management, TPM-based secure boot, logging, access control, and firmware-update procedures supported by your operations team?
- Does the acceptance clause define workload, quality, latency, throughput, availability, test conditions, and remedies if the system fails?
- Is the expansion plan explicit about rack space, power, cooling, switch ports, addressing, storage, and software licenses?
10. Common buying mistakes and how to correct them
Buying by accelerator count
Symptom: The specification names eight devices but omits memory topology, CPU capacity, PCIe placement, and network design.
Correction: Require the complete bill of materials and topology, then validate end-to-end performance.
Accepting a peak benchmark
Symptom: A proposal quotes maximum tokens per second with a short prompt, favorable batching, or an unstated cache state.
Correction: Re-run the same representative mix at target and peak concurrency, including tail latency and quality.
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Ignoring storage and startup paths
Symptom: Devices sit idle while models, indexes, or checkpoints load over an undersized path.
Correction: Measure load and recovery traffic, specify local NVMe and remote-storage roles, and size storage networking to the workload.
Leaving the facility survey until delivery
Symptom: The rack cannot supply the required power or remove the heat, delaying installation or forcing an expensive redesign.
Correction: Complete a signed power, cooling, airflow, rack, environmental, and delivery assessment before placing the order.
Comparing unlike software stacks
Symptom: Two vendors report different runtimes, quantization, drivers, or model builds, making their results appear contradictory.
Correction: Freeze versions and settings, retain raw logs, and compare only runs with identical workload and quality gates.
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Make the purchase conditional on proof
The defensible specification is the smallest system that passes your quality, latency, throughput, availability, and facility tests with documented headroom and a credible expansion path. Use vendor guidance such as NVIDIA’s CPU, memory, PCIe, NIC, storage, and management examples as a checklist, not as universal minima; verify every value against the exact accelerator and software stack. Do not sign off on accelerator count alone. Require a reproducible, workload-specific acceptance report and a written record of the power, cooling, network, storage, support, and growth assumptions that make the cluster viable.
Quick Recap
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