The Tool Desk
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Use the process below to turn a workload into a written specification, test CPU and accelerator options, challenge each supplier’s bill of materials, and accept a system only when representative results are documented under your conditions.
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1. Define the inference service before choosing hardware
The first purchase document should describe the workload, not a preferred server or GPU. Record every model that will be served, its tokenizer, precision or quantization, maximum context, expected prompt and output lengths, and the serving stack. Include routing, batching, speculative decoding, retrieval, safety filters and any other component that consumes CPU, memory, storage or network capacity.
Write measurable service objectives
Specify both performance and quality targets. A useful workload sheet includes:
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- Models and versions: exact checkpoints, adapters, embedding models, rerankers and preprocessing components.
- Request shape: prompt-token and output-token distributions, maximum context, streaming requirements and whether requests arrive as a steady stream or in bursts.
- Concurrency: average, planned peak and the queue depth at which requests must be rejected or throttled.
- Latency: time to first token, inter-token latency, complete-response latency and tail targets such as p95 or p99.
- Throughput: requests per second, input tokens per second and output tokens per second at the target concurrency.
- Quality: accuracy, retrieval recall, safety outcomes or other acceptance tests at the exact serving settings.
- Cache state: cold start, warm model, prefix-cache hit rate, key-value-cache size and the eviction policy.
- Growth: expected model changes, traffic growth, new tenants and the date by which additional capacity is needed.
NVIDIA’s NVIDIA-Certified Systems Configuration Guide says application workload, datasets, models and use case affect hardware selection. It also advises, “Please discuss specific workload requirements with your provider to ensure your solution will meet your business needs.” Treat that as a prompt for a workload-specific proposal, not as permission to accept a generic reference design.
Separate capacity from resilience
Calculate the capacity needed to serve normal and peak traffic, then decide how much spare capacity is required for maintenance, failures and rolling upgrades. A cluster that exactly meets a benchmark at 100% utilization may fail its service objective when one node is drained or traffic shifts between tenants. State whether the target assumes all nodes are healthy, whether a node failure must be tolerated, and how much headroom is reserved for bursts.
2. Decide whether you need CPUs, one accelerator server or multiple nodes
Do not use model parameter count as a universal sizing rule. The right shape depends on arithmetic intensity, memory bandwidth, batching, context length, latency target, concurrency, software support and where the service will run. Some preprocessing, embedding, small-model or low-volume services can fit on CPUs. Other workloads benefit from one accelerator server, while large models, high request volume or strict latency objectives may require multiple accelerators or nodes.
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Benchmark the smallest viable shape
Test a CPU-only implementation when the model and traffic make that plausible. Then test one accelerator server and, if the model or service objective requires it, a multi-node configuration. Keep the model, tokenizer, prompt/output distribution, quality checks and software versions constant so the comparison measures architecture rather than a changed application.
An AMD CPU-versus-accelerator infographic presents this framing and labels its results as dependent on the tested configuration. Its aggregate end-to-end test derived from TPCx-AI is not comparable with published TPCx-AI results because it does not comply with that specification. Use such vendor material to form hypotheses, then run your own representative test.
Check model fit and memory headroom
Account for weights, runtime workspace, activations, tokenizer state, communication buffers and key-value cache. Measure the largest context and batch combination you will permit, not only a short prompt. Quantization can reduce memory use, but it may change quality, kernel availability and latency; accept it only after the application-level quality tests pass. Require headroom for a model refresh and for fragmentation rather than sizing to the exact nominal weight footprint.
3. Inspect the complete server bill of materials
A balanced node moves data between host memory, accelerators, storage and the network without one component starving the others. Ask for a topology diagram showing CPU sockets, memory channels, PCIe switches and root ports, accelerator slots, NICs, NVMe devices and management controllers. Confirm which accelerator and NIC is attached to which socket.
NVIDIA’s example inference-server checklist
The current NVIDIA guide describes the following starting points for its inference-server examples. They are vendor recommendations for those configurations, not universal minimums for every accelerator, framework or deployment.
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| Component | Guide example | How to use it in due diligence |
|---|---|---|
| Accelerators per server | 2, 4 or 8 GPUs | Confirm the selected count is balanced across CPU sockets and PCIe root ports. |
| CPU sockets | At least 2 | Check NUMA placement and whether processes can reach local memory and PCIe resources. |
| CPU generation and clock | PCIe Gen5-capable CPUs (or later when available); at least 2.0 GHz base clock | Verify the exact processor SKU, firmware support and sustained behavior under your serving load. |
| CPU cores | At least 6 physical cores per GPU | Include tokenization, networking, orchestration and retrieval work when calculating usable cores. |
| System memory | At least twice aggregate GPU memory | Confirm capacity is distributed across sockets and memory channels; treat the ratio as a starting point. |
| GPU slots | PCIe speed and lane width matched to the specific GPU requirement | Request the slot map and verify that switches or bifurcation do not reduce the advertised link. |
| Multi-node NIC | Minimum 200 Gbps; up to 400 Gbps per GPU in the guide’s examples | Ask the supplier to justify bandwidth for your message sizes, collectives and node count. |
| Local storage | One NVMe per CPU socket, minimum 1 TB | Confirm endurance, qualification, RAID or replication policy and space for models, logs and container layers. |
| Remote management | Redfish 1.0 or greater | Check that the management controller exposes the controls your operations team requires. |
| Platform security | TPM 2.0 for secure boot | Verify secure-boot chain, key ownership, attestation and recovery procedures. |
These figures are a useful checklist when reviewing a proposal, but they do not prove that a system is suitable. A supplier should map every requirement to the exact server, accelerator, firmware, driver and serving software versions being quoted.
Review NUMA and PCIe locality
On a dual-socket server, a process may access remote memory or a remote PCIe root complex across the inter-socket link. That can increase latency and consume bandwidth needed by accelerators. Request a topology output from the proposed operating system or accelerator tools, then pin representative serving workers to the appropriate CPU and memory nodes. Verify that all GPUs have the required lane width at the same time; a slot that is electrically narrower than advertised can become a hidden bottleneck.
Check host memory behavior
Capacity is only one part of RAM selection. Confirm memory channels are populated symmetrically, the supported speed is available with the chosen DIMM count, and the platform’s error-correction and replacement policy match the service’s availability needs. Measure page faults, NUMA traffic and memory bandwidth during a realistic run. If retrieval, decompression, tokenization or logging shares the host, reserve those resources explicitly instead of assigning every core to the accelerator runtime.
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Inference clusters carry several different kinds of traffic. Combining them on an undersized or poorly isolated path can leave expensive accelerators waiting for data.
East-west accelerator traffic
Distributed model execution and collective operations exchange data between compute nodes. Latency, bandwidth, message size, topology and congestion all matter. Ask whether the serving framework uses direct GPU communication, which transport and fabric features are enabled, and how performance changes as nodes are added. The NVIDIA guide cites 200 Gbps as a minimum NIC recommendation for multi-node inference and up to 400 Gbps per GPU in its examples; those are reference values, not a universal prescription.
North-south customer traffic
Client requests and responses need load balancing, TLS termination, authentication and rate limiting. Specify peak ingress and egress, connection counts, packet size distribution and whether streaming responses keep connections open. Confirm that the customer-facing path cannot consume the bandwidth or CPU reserved for accelerator collectives.
Storage and data movement
Model loading, retrieval corpora, checkpoints, logs and container images may use a separate storage network. Size it for startup, refresh and steady-state traffic. A slow model load can extend maintenance windows; a saturated data path can reduce GPU utilization even when the accelerators themselves are fast. The NVIDIA reference-architecture design tenets distinguish east-west compute traffic, customer and storage connectivity, storage data movement, customer uplink and management services. Require a diagram that shows each path and its oversubscription.
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Management and support traffic
Out-of-band management, telemetry, firmware updates and support access should remain available during a data-plane incident. Isolate these functions with the required security controls, address ranges and jump-host process. Confirm how a failed NIC, switch or management controller is replaced and how configuration is restored.
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5. Validate the storage path and local NVMe design
Local NVMe is often used for model files, container layers, temporary compilation artifacts, caches and logs. The NVIDIA example recommends one NVMe per CPU socket with at least 1 TB of local storage. Treat that as a starting point: final capacity depends on the number and size of models, retention policy, replication and whether a shared filesystem is also present.
Ask storage questions that affect serving
- What are measured sequential and random read results at the queue depths generated by model loading and cache activity?
- Are drives enterprise-qualified for the expected write endurance, power-loss protection and temperature range?
- Does each socket have local access to its assigned drive, or does traffic cross a PCIe switch or NUMA link?
- How are models versioned, verified and rolled back after an incomplete update?
- Where do logs, traces and crash dumps go, and what prevents them from filling the model volume?
- What happens to startup time when several nodes load a model simultaneously?
Do not assume that a consumer SSD is server-qualified merely because its capacity matches the bill of materials. Require the exact drive model, endurance rating, firmware support and replacement procedure.
6. Confirm site readiness before signing the order
A cluster can be electrically or thermally infeasible even when its compute benchmark looks excellent. Perform a site assessment covering power delivery, cooling, airflow, rack layout, floor loading, environmental limits, cabling and physical security. The GPU-ready data-center resource highlights power, cooling, architecture, rack layout, storage and networking as preparation topics.
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Power and rack planning
Obtain the supplier’s maximum and typical draw for the complete node, including CPUs, memory, drives, NICs and fans. Ask for startup and transient requirements, power-factor assumptions, connector types, voltage, redundant-feed behavior and the number of rack units. Compare those figures with available circuits, breaker capacity, rack power distribution and backup runtime. Include switches, storage and management equipment rather than calculating only accelerator nameplate power.
Cooling and airflow
Confirm the cooling method, inlet temperature range, humidity limits, exhaust direction and required blanking panels. Measure whether the room can remove the heat at the proposed density and whether hot-aisle or cold-aisle containment is needed. NVIDIA’s configuration guidance notes that temperature, airflow and hardware choices affect component temperature and workload performance. Request thermal alarms, fan-failure behavior and the procedure for operating during a cooling-unit outage.
Environmental and mechanical checks
Verify rack depth, rail kits, cable bend radius, floor loading, seismic requirements where applicable, dust control and access for service. Record the time and cost of electrical, cooling or rack work in the project plan. A reference architecture’s scalable unit is a planning pattern for topology, power, cooling and expansion; it is not evidence that your facility should begin with that exact unit size.
7. Make software, security and support part of the specification
Hardware only performs as promised when the complete software path is supported. List the operating system, accelerator driver, runtime, communication libraries, container engine, serving framework, orchestration platform and observability agents. Require a compatibility matrix for the exact versions in the proposal and a policy for security patches and rollback.
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- Verify secure boot with TPM 2.0, firmware-signing policy and ownership of platform keys.
- Confirm Redfish 1.0 or greater access, role-based permissions, audit logs and integration with your management system.
- Define tenant isolation, secrets handling, encryption in transit and at rest, and the process for sanitizing replaced drives.
- Require vulnerability-response timelines for the host OS, drivers, firmware, containers and serving dependencies.
- Document who can access out-of-band consoles and how emergency access is recorded.
Support and lifecycle
Obtain written warranty coverage, response and replacement targets, spare-parts location, firmware maintenance responsibility and escalation contacts. Ask how long the proposed accelerator, server BIOS, NIC firmware and serving stack will remain supported. Delivery dates should identify dependencies such as rack work, network circuits, licenses and acceptance testing rather than promising a single shipment date.
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8. Require a reproducible acceptance test
Do not accept peak throughput from an unrelated benchmark as proof of fit. Run the proposed stack with your model, tokenizer, prompt and output distributions, concurrency, cache state, quality tests and failure policy.
Put test provenance in the report
Every result should identify:
- Hardware model, accelerator count, CPU and memory configuration, NICs, storage and topology.
- Operating system, firmware, drivers, runtime, serving framework, container image and configuration flags.
- Model checkpoint, precision or quantization, tokenizer, context limits and batching policy.
- Prompt and output datasets, concurrency levels, warm-up method, cache state and test duration.
- Latency definitions, percentile calculation, throughput units, error rate and quality or accuracy score.
- Network mode, storage path, power or thermal limits and any background services.
Report median and tail latency at each required concurrency, not only the fastest run. Include time to first token, inter-token latency and complete-response latency where streaming is used. Record throughput and quality together: a configuration that increases tokens per second by lowering accuracy may not meet the service objective.
Test failure and degradation behavior
Drain a node, restart a serving process, interrupt a storage path and introduce a controlled network impairment if your availability target requires it. Measure recovery time, in-flight request handling, cache rebuilding and the performance of the remaining nodes. Repeat after a model refresh and after a rolling software update. These tests reveal whether the planned spare capacity is real.
The MLPerf foundational method paper explains why hardware and software variety makes architecture-neutral, representative and reproducible comparison difficult, and why rules are needed for comparable results. Use that principle even when you are not submitting to a benchmark: freeze versions, publish conditions and retain raw logs so a supplier’s claim can be reproduced.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.9. Compare vendor proposals on one decision sheet
When two configurations pass basic feasibility, score them against the same workload and acceptance criteria. Keep measured values separate from supplier assertions and require a source or test artifact for each material claim.
| Decision area | Questions to answer |
|---|---|
| Model fit | Does the configuration hold the largest model, context and cache with documented memory headroom and required quality? |
| Service performance | What are p95 or p99 latency and throughput at target concurrency, including warm-up and realistic traffic mix? |
| End-to-end balance | Are CPU, memory, PCIe, GPU links, NICs, storage and serving software sized together, with topology evidence? |
| Site feasibility | Are power delivery, cooling, rack space, airflow, environmental limits and cabling approved in writing? |
| Software compatibility | Are exact driver, runtime, framework, OS and firmware versions supported and patchable? |
| Operations and security | Are Redfish management, secure boot, monitoring, access control, replacement and incident procedures documented? |
| Availability and support | What are lead time, warranty, response targets, spare strategy and escalation path? |
| Expansion | How are nodes, network ports, power and cooling added, and what utilization or topology limit appears first? |
| Cost evidence | Which dated quote covers hardware, software, support, installation, facility work and recurring operations? |
No generally applicable price, total-cost, power-draw or cost-per-token figure is established for an unspecified buyer. Obtain dated quotes and measured results for the candidate system. Treat every benchmark score as scoped to its reported configuration and conditions.
10. Plan growth without overbuying
Define the smallest repeatable expansion unit: a server, a rack group or another topology unit. State how many units can share the current fabric, storage service, power domain and cooling zone before an upgrade is required. Ask whether adding nodes changes collective performance, oversubscription, licensing, management load or failure-domain size.
Model at least three traffic cases: expected demand, the approved peak and the growth point at which another unit is ordered. Include lead time for accelerators, servers, switches, rack work and acceptance testing. A smaller initial cluster may be sensible when the workload is uncertain, provided the network, power and rack plan can expand without a disruptive redesign. Conversely, buying a large reference unit before validating demand can strand capital and operating capacity.
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11. Common buying mistakes and how to prevent them
Choosing by accelerator count
Symptom: a proposal advertises many GPUs but gives no socket, PCIe or network topology. Fix: require the slot map, NUMA plan, link widths and measured end-to-end test.
Using a peak benchmark as a service guarantee
Symptom: a high throughput number is reported at a favorable batch size with no tail latency or quality result. Fix: repeat the test with your prompt/output mix, target concurrency, cache state and acceptance thresholds.
Ignoring host and storage work
Symptom: accelerators are underutilized while tokenization, retrieval, model loading or logging waits on CPU, RAM, PCIe or NVMe. Fix: profile each pipeline stage and reserve resources for non-accelerator work.
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Symptom: customer traffic, storage transfers and east-west collectives contend on one undersized path. Fix: show each traffic class on the topology diagram, size it for measured demand and verify isolation or quality-of-service controls.
Leaving the facility until delivery
Symptom: the cluster arrives but circuits, cooling or rack depth are unavailable. Fix: make site approval, thermal and power calculations, cabling and delivery dependencies conditions of the purchase.
Accepting an unrepeatable demonstration
Symptom: the supplier cannot provide model versions, software versions, workload data or measurement definitions. Fix: place provenance requirements and an acceptance test in the contract.
12. Final pre-purchase checklist
- Write down models, versions, token distributions, context, concurrency, latency, throughput, quality, cache state and growth assumptions.
- Benchmark CPU-only, single-server accelerator and multi-node shapes that are plausible for that workload.
- Confirm memory headroom, quantization quality, CPU cores, NUMA placement, PCIe lanes, GPU links and host RAM.
- Map east-west, customer, storage and management traffic; obtain measured bandwidth and tail-latency evidence.
- Specify local and shared storage capacity, endurance, locality, model-update and recovery procedures.
- Approve power, cooling, airflow, rack, environmental, cabling and physical-security requirements before ordering.
- Freeze and document operating-system, firmware, driver, runtime, framework and container versions.
- Verify secure boot, TPM 2.0, Redfish management, access controls, monitoring and patch responsibilities.
- Run a representative acceptance test and retain raw logs, quality results and failure-recovery measurements.
- Compare dated proposals for support, lead time, expansion, facility work and recurring operating cost, not just accelerator price.
If a supplier cannot answer these questions with a topology, compatibility matrix, workload-specific test and site assumptions, the cluster is not yet specified well enough to buy.
Quick Recap
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