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Separate the networks before calculating capacity
The first planning mistake is adding every byte together and calling the result the cluster bandwidth requirement. AI infrastructure normally carries several distinct traffic classes. NVIDIA’s NCP architecture identifies tenant access (TAN), secure management (SMN), GPU cluster interconnect (CIN), and in-rack NVLink as separate roles. Its documentation states that TAN and SMN are always Ethernet, CIN can use Ethernet or InfiniBand, and NVLink is a proprietary NVIDIA standard. See NVIDIA’s data-center architecture documentation.
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| Network role | Typical traffic | Transport described in the reference architecture | Capacity question |
|---|---|---|---|
| TAN Tenant access |
User requests, APIs, notebook sessions, job submission, external application traffic | Ethernet | What north-south throughput, connection rate, and isolation do users and applications require? |
| SMN Secure management |
Provisioning, monitoring, orchestration, firmware work, logging, and out-of-band administration | Ethernet | Can operators still reach every device and collect telemetry during a compute-fabric incident? |
| CIN GPU cluster interconnect |
All-reduce, all-to-all, parameter exchange, checkpoint coordination, and other east-west GPU traffic | Ethernet or InfiniBand, depending on the design | What aggregate bandwidth, latency, path diversity, and collective-communication behavior does the workload need? |
| NVLink In-rack scale-up |
GPU-to-GPU communication inside a supported rack-scale system | NVIDIA proprietary in-rack fabric | Which traffic stays inside the rack, and which traffic must cross the inter-rack fabric? |
Keep these roles separate in the capacity model even when they share a physical switch family. A shared physical infrastructure can be appropriate, but it needs deliberate segmentation, quality-of-service policy, failure-domain design, and monitoring. Do not let a storage burst or a tenant transfer consume the links needed for a synchronized GPU collective.
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Bandwidth is only useful when tied to a workload. Training jobs often generate synchronized east-west bursts: a slow or congested path can hold every worker at a collective barrier. Inference clusters may have a steadier request stream but can create large east-west transfers when models, caches, or sharded state move between servers. Checkpointing and dataset reads create storage traffic that may be periodic, very large, and independent of GPU collectives.
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Write the workload assumptions down before selecting ports or switches:
- Job type: distributed training, fine-tuning, batch inference, interactive inference, or a mixture.
- Communication pattern: predominantly all-reduce, all-to-all, point-to-point, parameter-server, or storage-heavy.
- Concurrency: number of simultaneous jobs, tenants, and scheduler queues.
- Placement: whether jobs stay within one rack, one scalable unit, or span multiple units.
- Service objective: completion time, tail latency, throughput, recovery time, or a combination.
- Failure behavior: whether a job may slow during a link or switch failure, or whether the design must preserve a target capacity.
- Growth horizon: the initial deployment, the planned expansion, and the largest cluster the room, power, and cooling design can support.
Use a traffic matrix rather than a single average. Record source and destination groups, expected peak and sustained rates, burst duration, and whether the traffic is loss-sensitive or latency-sensitive. A five-minute average can hide a collective burst that overloads a short uplink every few seconds.
Build the design from a node to a scalable unit
NVIDIA’s enterprise reference architecture recommends starting with the node, scaling to a repeatable scalable unit, and then designing a multi-unit cluster. The scalable unit is more than a server count: it connects compute, network adapters, power, cooling, rack layout, and operational support. The planning sequence is described in NVIDIA’s enterprise reference-architecture design tenets.
1. Inventory one server configuration
For every node, record the GPU model and count, the network-adapter model and count, adapter speed, connector type, protocol, and the switch ports to which the adapters will connect. Record which adapters serve CIN and which serve storage, management, or tenant access. A nominal GPU count is not enough: two eight-GPU systems can have different network capabilities.
2. Define the repeatable unit
A repeatable unit might be a rack, a group of racks, or another assembly that can be deployed and supported consistently. Document its GPU count, network-port count, power draw, cooling envelope, rack positions, cable paths, and expected east-west injection. If the unit cannot be repeated without changing switch tiers or cabling, it is not yet a reliable scaling block.
3. Expand to the target cluster
Multiply the unit’s traffic demand by the number of units that can communicate concurrently. Then map those endpoints onto the selected leaf-spine or fat-tree topology. The result should show where traffic is local, where it crosses a leaf pair, and where it reaches a spine or higher tier. This makes oversubscription and failure impact visible before equipment is ordered.
NVIDIA illustrates this kind of accounting with C-G-N-B notation, representing CPU sockets, GPUs, network adapters, and average east-west bandwidth per GPU. The useful lesson is that network interfaces and bandwidth belong in the compute configuration itself, not in a separate afterthought.
Use platform-specific bandwidth figures
The following figures are current NVIDIA manufacturer guidance accessed on October 11, 2026. They are reference recommendations for named platforms, not independent measurements or universal requirements.
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| Platform reference | Documented total compute-network bandwidth | Interface detail | Simple eight-GPU average |
|---|---|---|---|
| HGX H100, H200, or B200 system | Greater than 200 GB/s minimum; 400 GB/s recommended total compute network bandwidth | 400 Gb/s interfaces are specified in the cited configurations | More than 25 GB/s minimum and 50 GB/s at the recommended total, if divided evenly across eight GPUs; this arithmetic is not a per-GPU vendor guarantee |
| HGX B300 system | 400 GB/s minimum; 800 GB/s recommended total compute network bandwidth | 400 Gb/s interfaces are specified in the described configurations | 50 GB/s minimum and 100 GB/s at the recommended total, if divided evenly across eight GPUs; actual topology and traffic distribution determine usable capacity |
| DGX GB rack-scale system | NVLink 5.0 is specified at 1.8 TB/s bidirectional per GPU | In-rack NVLink scale-up; this is not the inter-rack Ethernet or InfiniBand requirement | Not comparable to a cluster scale-out figure |
The HGX H100/H200/B200 component guidance is published at NVIDIA HGX AI Factory components. The B300 values are in NVIDIA’s HGX AI Factory component guide. The rack-scale NVLink figure and the documented hybrid use of NVLink, InfiniBand, and Ethernet appear in the DGX GB Rack Scale Systems networking guide.
Do not confuse gigabits per second with gigabytes per second. A 400 Gb/s interface has a nominal line rate of 50 GB/s before protocol overhead, encoding, software, and traffic patterns are considered. The 400 GB/s and 800 GB/s figures above are aggregate compute-network guidance for the named systems; they are not the speed of one cable.
How to apply the numbers to a cluster
For an illustrative calculation, 16 identical eight-GPU HGX B200 nodes using the 400 GB/s per-system recommendation would present 6,400 GB/s of aggregate node-level compute-network demand before topology effects and overhead. That arithmetic does not mean the cluster needs one 6.4 TB/s link, nor does it prove that every workload will consume the full figure. It tells you the scale of traffic that the fabric, uplinks, and failure policy must be able to accommodate.
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For every platform, calculate at least three views:
- Node injection: the traffic one server can place on the CIN.
- Scalable-unit demand: the simultaneous traffic generated by the repeatable deployment block.
- Cluster bisection and failure demand: the traffic that must cross the middle of the fabric, including the capacity that remains after a link, leaf, spine, or plane failure.
Set a written headroom policy rather than silently assuming that a nominal port rate equals usable application throughput. State whether the target is based on line rate, measured payload rate, or a workload completion-time objective.
Choose a topology that matches scale and failure domains
NVIDIA’s HGX and NVL72 references describe leaf-spine or fat-tree compute fabrics with rail-optimized GPU connections. As endpoint counts grow, additional spine tiers may be required. The physical-topology guidance is documented in NVIDIA’s NVL72 AI Factory networking topology guide.
Leaf-spine and fat-tree
A leaf-spine design gives each leaf a predictable path to every spine and makes it easier to add repeatable units. A fat tree adds capacity and path diversity through higher tiers. The correct radix, number of tiers, and uplink count depend on endpoint count, port speeds, cable reach, rack geography, and the amount of traffic that must cross leaves.
Do not label a fabric non-blocking simply because every node has a high-speed adapter. A fabric can be oversubscribed at the leaf uplink, spine, or higher tier. Calculate the offered load at each tier and state the oversubscription ratio for normal operation and for the largest planned failure.
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Rail-optimized connections
Rail optimization aligns corresponding GPU or adapter rails with predictable switch paths. It can simplify placement and improve collective behavior, but it must match the actual server wiring and software topology. Verify the platform’s rail map rather than assuming that adapter numbering has the same meaning across server generations.
Single-plane and multi-plane fabrics
A single compute plane is simpler to cable and operate. Multiple planes can provide additional capacity and a separate failure path. NVIDIA’s NVL72 guidance describes separate compute network planes and a multi-link approach intended to retain capacity while reducing dependence on one link or fabric path; it also describes NCCL handling resiliency and load balancing between planes for that design.
Multi-plane networking is not mandatory for every cluster. Decide whether its extra adapters, switches, optics, cabling, configuration, and monitoring are justified by the workload’s collective sensitivity and the required behavior during a plane failure.
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The architecture references establish that Ethernet and InfiniBand can both be used for the CIN in appropriate designs. They do not establish a workload-neutral performance or cost winner. Make the decision against the selected GPU platform and operating model.
| Decision axis | Questions to answer |
|---|---|
| Platform support | Does the server configuration support the required adapter, switch, driver, firmware, connector, and protocol combination? |
| Aggregate bandwidth | Can the proposed ports meet node injection, scalable-unit demand, and cluster bisection targets after the stated failure policy? |
| Collective behavior | What latency, synchronization, congestion-control, and all-to-all behavior does the actual training or inference workload require? |
| Topology | How many endpoints, tiers, rails, and uplinks are needed, and where are the boundaries between racks or scalable units? |
| Resilience | What happens when a link, adapter, leaf, spine, or complete plane is unavailable? |
| Other traffic | Will storage, management, and north-south traffic use separate networks or share hardware with enforceable isolation? |
| Operations | Does the team have the skills, automation, telemetry, spare parts, and change-control process to run the chosen fabric? |
| Total deployment cost | Include adapters, switches, optics, transceivers, cabling, power, rack space, software support, and operational labor—not just switch port prices. |
Choose the fabric that satisfies the measured workload and operational requirements for the specific platform. Avoid statements such as Ethernet is always sufficient or InfiniBand is always faster; the available evidence does not support either universal claim.
Account for storage, tenant, and management traffic separately
Storage bandwidth is often the largest non-CIN demand. Dataset staging, checkpoint writes, model distribution, and feature-store access can create synchronized bursts. Model the storage path from the storage system through its switches and adapters to the GPU nodes; do not count only the server-side port.
Tenant access has a different shape: many connections, variable request sizes, and potentially unpredictable north-south bursts. Size it for the user and application service objective rather than for the GPU collective target. Secure management traffic usually consumes less bandwidth, but its availability is more important during an outage. Keep management access usable when the compute fabric is congested or being reconfigured.
If physical separation is not practical, document the logical separation, queueing policy, routing boundaries, monitoring, and failure behavior. Validate that a storage or tenant event cannot starve CIN queues, and that a CIN incident cannot remove the management path needed to repair it.
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Verify adapters, switches, optics, and cabling before purchase
NVIDIA’s component documents show that network details vary by platform. The cited HGX configurations include 400 Gb/s SuperNIC options and QSFP112 models, but a product category or line rate alone does not prove compatibility. Treat every proposed component as a system-level match.
- Server compatibility: confirm the exact HGX or rack-scale platform, adapter placement, supported firmware, and host interface.
- Speed and protocol: verify 400 Gb/s versus another line rate and confirm Ethernet or InfiniBand support for the intended CIN.
- Connector and optics: match the adapter connector, switch cage, transceiver type, fiber or copper medium, reach, and breakout arrangement.
- Port count: count host ports, leaf ports, spine ports, management ports, and reserved capacity for expansion and spares.
- Rail mapping: ensure the physical cabling preserves the platform’s intended rail or plane topology.
- Software stack: check driver, firmware, fabric-management, telemetry, and collective-communication support as one tested combination.
- Serviceability: document cable labels, replacement procedures, spare optics, and the effect of removing one component.
Before committing to a 400GbE QSFP112 network adapter or any similar high-speed component, obtain the current platform compatibility matrix and verify the switch-side port and protocol details. Availability, model revisions, and support status can change independently of the bandwidth label.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Validate capacity with representative tests
A design spreadsheet should be followed by tests that reproduce the intended communication pattern. Use the production server, adapter, switch, firmware, topology, and cabling wherever possible.
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- Baseline one node: confirm link state, negotiated speed, error counters, and the expected adapter-to-GPU mapping.
- Test the scalable unit: run representative collective, storage, and tenant workloads concurrently and record payload throughput, latency, completion time, and queue behavior.
- Test cross-unit traffic: place jobs across the same racks and across different rack groups to expose leaf and spine constraints.
- Exercise bursts: include checkpointing, model distribution, dataset reads, and login or API surges rather than testing only a steady stream.
- Remove one path: fail a link, adapter, leaf, spine, or plane according to the design’s fault model and measure the remaining capacity and job behavior.
- Check management access: verify that monitoring, orchestration, and repair access remain available during compute and storage congestion.
- Compare results with the model: explain every gap between calculated line-rate capacity and measured application performance before scaling out.
Record test conditions, software versions, topology, job placement, and whether results are line-rate, payload-rate, or application-level. Manufacturer guidance is a starting point; only a workload-specific validation can show whether the planned capacity meets the service objective.
Common planning failures and their fixes
Using one bandwidth-per-GPU number everywhere
Symptom: a design applies the same per-GPU target to H100, H200, B200, and B300 systems.
Fix: start with the exact platform guidance, record the source and access date, then adjust for the workload, topology, and failure policy. The HGX B300 reference, for example, specifies higher aggregate compute-network figures than the cited H100/H200/B200 guidance.
Counting NVLink as cluster scale-out bandwidth
Symptom: in-rack NVLink capacity is used to justify insufficient inter-rack links.
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Ignoring the middle of the fabric
Symptom: every node has a fast adapter, but leaf uplinks or spines saturate when jobs span racks.
Fix: calculate traffic at each tier, include simultaneous cross-rack jobs, and state the behavior after the largest planned component failure.
Mixing incompatible high-speed components
Symptom: adapters negotiate at a lower rate, links remain down, or the fabric works only in one protocol mode.
Fix: verify platform, speed, connector, transceiver, protocol, port count, firmware, and cable reach as a single bill of materials. Do not infer compatibility from a shared 400 Gb/s label.
Letting storage or tenant bursts consume CIN
Symptom: training slows during checkpointing or user traffic spikes.
Fix: separate the traffic physically or logically, establish queueing and isolation policy, and test the worst concurrent burst rather than each traffic class in isolation.
Designing redundancy without defining degraded operation
Symptom: a dual-link or dual-plane design survives a failure but loses more capacity than the workload can tolerate.
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Fix: write the degraded-state target first, then verify that remaining links, switches, and software load balancing can meet it. Redundancy is a capacity requirement as well as an availability feature.
A practical planning worksheet
Use one row per server type and one section per repeatable unit. Keep the following fields under configuration control:
| Field | What to record |
|---|---|
| Platform | Exact GPU server or rack-scale system and revision |
| GPU configuration | GPU count, in-rack fabric, and placement boundaries |
| CIN adapters | Model, count, speed, connector, protocol, rail, and plane |
| Other adapters | Storage, TAN, and SMN interfaces and their switch domains |
| Traffic targets | Sustained rate, peak rate, burst duration, latency objective, and collective pattern |
| Topology | Leaf, spine, higher tiers, uplinks, radix, endpoint count, and cable routes |
| Failure policy | Link, adapter, leaf, spine, and plane failures to tolerate |
| Expansion | Reserved ports, rack positions, power, cooling, and cable capacity |
| Validation | Test workload, software versions, measured payload rate, completion time, and degraded results |
Recalculate the worksheet whenever the GPU platform, adapter count, switch model, protocol, rack grouping, or workload mix changes. A small change in server wiring can alter the required rail topology and the number of switch ports even when the GPU count is unchanged.
Decision rule
The defensible design is the one that can show, for the selected platform and target cluster, how each traffic class is carried; how node demand becomes scalable-unit and cluster demand; where capacity exists at every fabric tier; what remains after a defined failure; and how the result was validated with representative workloads. Use NVIDIA’s published figures as platform-specific starting points, not as a substitute for that calculation.
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