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Design an AI-cluster network from the workload outward: identify the training collectives or inference flows, estimate host and storage traffic at the target scale, then choose a fabric, topology, congestion strategy and failure-domain layout that can sustain those flows. Keep GPU east-west traffic isolated or deliberately rail-optimized, and validate the complete NIC-to-switch-to-optic system with representative jobs rather than relying on a port-speed total.
InfiniBand and Ethernet with RoCE can both be appropriate. There is no generally proven winner in the available evidence. The practical choice depends on measured step time or inference latency, congestion and tail behavior, resilience, operational capability and total lifecycle cost.
Start with the AI workload, not the GPU count
Distributed training is dominated by synchronized node-to-node communication. Every worker may need to exchange gradients or activations with many other workers before the next step can complete, so a slow or congested path can make otherwise idle GPUs wait. Inference clusters have a different mix: model parallelism can require frequent exchanges inside a request, while replicated services may generate smaller, less synchronized flows but stricter latency objectives.
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- Number of servers and GPUs now, plus the planned expansion size.
- GPU-to-GPU and server-to-server communication patterns, including all-reduce, all-gather, all-to-all and point-to-point traffic.
- Training, batch-inference and interactive-inference proportions; expected collective sizes and concurrency.
- NIC count per server, port speed, GPU-to-NIC rail mapping and whether multiple network planes are required.
- Required training-step time, collective-operation time, inference throughput and p95/p99 latency.
- Storage throughput and burst behavior for checkpoints, datasets and feature stores.
- Multi-tenant isolation, quality-of-service requirements and the amount of north-south traffic.
- Rack layout, cable distances, power and cooling limits, and the failure or maintenance events the cluster must survive.
- Existing operations skills, automation, telemetry, firmware policy and support arrangements.
Do not size from GPU count or advertised line rate alone. A cluster with fewer GPUs but large, synchronized collectives may need more bisection bandwidth and stricter congestion control than a larger inference fleet with mostly independent requests.
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Classify the communication pattern
| Workload pattern | Network implication | Measurements to capture |
|---|---|---|
| Synchronous data or tensor parallel training | Large collectives can make all workers wait for the slowest path. Uniform rail mapping and low tail latency matter. | All-reduce and all-gather duration, step-time variance, queue depth and p95/p99 collective latency. |
| Mixture-of-experts or other all-to-all-heavy training | Many simultaneous destinations stress radix, bisection bandwidth and congestion handling. | All-to-all completion time, hot links, packet drops or retransmissions and fairness between jobs. |
| Model-parallel inference | Per-request exchanges can make latency sensitive to microbursts and path imbalance. | Per-request network latency, tail latency during peak concurrency and link-utilization bursts. |
| Replicated inference services | Traffic is often more independent, but storage, load balancing and east-west service calls may compete with GPU traffic. | Throughput, p95/p99 latency, bandwidth by traffic class and behavior during failover. |
Choose InfiniBand or Ethernet/RoCE against operational reality
InfiniBand is a purpose-built high-performance fabric. NVIDIA documentation describes adaptive routing, congestion-control features and SHARP in-network collective offload, and identifies supported topologies including Fat Tree and Dragonfly+. Those capabilities can be valuable when collective communication dominates, but their benefit still depends on the exact adapters, switches, software and workload.
Ethernet with RoCE can fit organizations that want to use Ethernet operations, automation and skills. It is not simply ordinary Ethernet with a faster port: synchronized AI traffic requires deliberate loss, queue, priority and path-management policies. NVIDIA’s Spectrum-X materials describe purpose-built switches and NICs and RoCE-specific mechanisms. Treat those statements as vendor claims and verify them on the intended hardware and software versions.
| Decision axis | Questions for an InfiniBand design | Questions for an Ethernet/RoCE design |
|---|---|---|
| Application performance | What step-time and collective results do the selected adapters, switches and software deliver? | What do RoCE queueing and retransmission behavior produce under synchronized collectives? |
| Congestion and tail behavior | How do adaptive routing and fabric congestion controls behave with concurrent jobs? | How are priorities, pause behavior, explicit congestion notification and loss recovery configured and monitored? |
| Resilience | How quickly do jobs recover from a link, adapter or switch failure, and what traffic is affected? | What is the failure blast radius when a path, queue or priority class is impaired? |
| Operations | Do the team’s tools and skills cover fabric management, firmware and diagnostics? | Can the team operate lossless or near-lossless queues, ECMP, telemetry and RoCE-specific troubleshooting? |
| Total cost | Include fabric switches, adapters, optics, cables, support, spares and training. | Include compatible switches, adapters, optics, cables, licensing or support and the engineering effort needed for tuning. |
Do not reduce the decision to “Ethernet is cheaper” or “InfiniBand is always faster.” The available source set does not establish an independent, controlled comparison that settles the choice for every cluster. Benchmark equivalent configurations with the actual model, parallelism, job concurrency and failure tests you expect to run.
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Isolate the compute fabric
Use a dedicated GPU east-west network, or enforce a deliberate rail-aware design that prevents storage, management and ordinary user traffic from consuming the paths needed by collectives. NVIDIA’s HGX AI Factory logical architecture describes “a separate, isolated leaf-spine architecture for high-bandwidth, low-latency node-to-node communication.” That is an architectural example, not a universal bill of materials.
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A leaf-spine fabric is a common scalable shape because each leaf can reach every spine, but the useful properties are nonblocking capacity, predictable pathing and sufficient switch radix—not the labels alone. Calculate host injection bandwidth, uplink capacity, expected oversubscription and bisection bandwidth for the maximum participating set of servers.
Map rails and planes consistently
When a server has multiple NICs or network planes, map each GPU rail to a known fabric path. Keep the mapping consistent across servers so collective libraries can use all rails without accidentally funneling traffic through one adapter or leaf. Verify that the NIC, switch, driver, orchestration layer and collective software support the selected plane count and load-balancing mode.
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One NVIDIA HGX reference configuration specifies 32 × 400G compute uplinks per scalable unit. This number belongs to that hardware and topology context. It is not a rule that every AI rack needs 32 uplinks or 400G links. Before adopting it, check host port count, switch radix, rail mapping, oversubscription, cable reach, power and the number of servers in the scalable unit.
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Engineer congestion for synchronized collectives
AI collectives behave differently from ordinary web requests. Large flows can start at nearly the same time, converge on the same links and finish only when the slowest participant finishes. A small number of congested paths can therefore increase every worker’s step time.
Understand path collisions
NVIDIA’s technical discussion of AI networking describes how static ECMP hashing can place several large flows on one path while other paths remain underused. The resulting queueing can delay a straggling flow and hold up the entire collective. Measure path utilization and collective completion time; do not infer balance from the number of equal-cost paths.
Configure pause and loss behavior deliberately
In RoCEv2 deployments, NVIDIA describes how priority flow control can propagate pauses and create head-of-line blocking. PFC is therefore not a substitute for a complete congestion strategy. Define traffic classes, queue thresholds, marking or notification behavior, buffer policy and recovery actions, then test them with simultaneous jobs and microbursts. The exact behavior depends on the switch ASIC, NIC firmware and configuration, so validate rather than assuming that a vendor default is safe.
Test the traffic that matters
- Run representative all-reduce, all-gather and all-to-all operations at the expected message sizes.
- Run multiple jobs concurrently, including the worst combination of collective-heavy and storage-heavy activity.
- Inject link, NIC and switch failures while measuring application recovery.
- Collect queue occupancy, congestion marks, drops, retransmissions, pause counters, link utilization and tail latency.
- Record training step time or inference latency, not just synthetic bandwidth.
Size links, switches and planes as a system
Calculate the right capacities
For each server, start with the aggregate host injection capacity: the number of active NIC ports multiplied by their line rate. Then account for protocol overhead, application efficiency, simultaneous rails, storage traffic and the fraction of traffic that must cross a leaf-to-spine boundary. Compare that demand with uplink capacity and switch radix at the largest failure or maintenance condition you intend to tolerate.
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Line rate is not application bandwidth. Optics, encoding, packet size, software, PCIe limits, NIC placement and collective algorithms can all reduce useful throughput. Use measured application results to set an operating headroom target instead of running every link at its theoretical maximum.
Plan growth and oversubscription
Document the initial cluster, the next expansion step and the largest supported cluster. A topology that is nonblocking for one rack may become oversubscribed after adding leaves without adding spine capacity. Reserve switch ports, rack space, power and cable routes for the growth stage you can realistically fund, and state the oversubscription ratio for each tier.
Keep storage and control traffic explicit
Checkpoint writes, dataset reads and metadata operations can create bursts that coincide with collectives. Give storage connectivity its own capacity and, where appropriate, its own fabric or traffic class. Management, provisioning, monitoring and out-of-band recovery need separate availability objectives from GPU data traffic. The HGX reference architecture describes distinct storage, customer-network and management connectivity; adapt that separation to your operational model.
Select compatible NICs, switches, optics and cabling
Procure a supported end-to-end design, not a collection of components with matching numbers on their labels. Confirm adapter firmware, switch operating system, port mode, transceiver coding, connector, fiber type, reach, breakout behavior and driver or operator versions before placing an order.
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| Component | Compatibility checks | Typical failure if skipped |
|---|---|---|
| GPU-server NIC | PCIe generation and slot placement, port speed, rail mapping, firmware, driver and collective-library support. | One rail is slower, unavailable or mapped to the wrong GPU. |
| Switch ASIC and software | Port breakout, buffer behavior, routing or ECMP features, congestion controls, telemetry and supported firmware. | Advertised bandwidth exists but queues, routing or telemetry cannot support the workload. |
| Optics and transceivers | Exact form factor, coding, wavelength, connector, fiber, reach and vendor support matrix. | Links fail to establish, flap at load or run at a lower mode. |
| Copper or fiber cabling | Length, bend radius, polarity, trunk design, labeling and thermal limits. | Cabling mistakes create intermittent errors and difficult-to-localize congestion. |
| Orchestration and software | Driver, firmware, Network Operator, fabric manager and collective-library versions. | Planes are not exposed correctly or updates break collective performance. |
A 400G QSFP-DD optical transceiver is a plausible component in a design using 400G links, but the designation alone does not establish compatibility. Confirm the exact switch and NIC matrix, fiber and reach with the relevant vendor documentation. NVIDIA’s LinkX material describes cable and transceiver categories for InfiniBand connectivity; it does not provide a generic guarantee for every 400G system.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Design failure domains before deployment
Distributed jobs can fail from a single impaired path even when most links remain healthy. Define the blast radius and recovery behavior for each of these events:
- One optical module or cable fails.
- A server NIC or PCIe path fails.
- A leaf switch loses power or requires maintenance.
- A spine or inter-switch link fails.
- A firmware change leaves one rail or plane at a different capability.
- A storage or management network outage blocks job startup or checkpoint recovery.
Use redundant paths where the workload and budget justify them, but verify that software actually detects and uses the redundancy. Decide whether a failed component drains a rack, a rail, a job or the entire cluster. Document maintenance procedures that preserve enough bandwidth for running jobs, and keep spare optics, cables and adapters that match the deployed part numbers.
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Implement the design in a controlled sequence
- Freeze workload requirements. Record model, parallelism, collective sizes, concurrency, latency objectives, storage rates, growth target and failure objectives.
- Choose the fabric family. Shortlist InfiniBand and Ethernet/RoCE, then define equivalent hardware and software configurations for a measured comparison.
- Draw traffic domains. Separate GPU east-west, storage, management and customer or north-south paths. Mark every rail, plane and failure domain.
- Size the topology. Calculate host injection, uplinks, radix, bisection bandwidth, oversubscription and expansion ports for normal and degraded operation.
- Specify the supported stack. Select NICs, switches, optics, cables, firmware, drivers, fabric software and orchestration versions as one compatibility set.
- Build a representative pilot. Include the same GPU-server type, rail mapping, switch generation, optics and software planned for production.
- Tune congestion controls. Configure routing, queueing, priorities, marking or pause behavior and telemetry; record every setting in version control.
- Run application and failure tests. Measure collectives, full jobs, concurrent tenants, storage bursts and component failures before scaling out.
- Approve production gates. Require target step time or inference latency, acceptable tail behavior, recovery time, link health and documented rollback procedures.
Validate with application-level measurements
| Test area | Record | Acceptance question |
|---|---|---|
| Single-job collectives | All-reduce, all-gather and all-to-all time by message size. | Does the job meet the expected step-time or latency budget? |
| Concurrent jobs | Per-job throughput, fairness, queue depth, congestion marks and p95/p99 latency. | Does one tenant create unacceptable stragglers for another? |
| Degraded fabric | Performance and recovery after link, NIC, leaf or spine failure. | Is the blast radius and recovery time within the service objective? |
| Storage contention | Checkpoint and dataset throughput while collectives run. | Are storage bursts isolated from GPU communication? |
| Expansion | Path utilization and collective time at each planned scale step. | Does adding servers preserve the intended oversubscription and headroom? |
Use synthetic tests to find limits, but make the release decision with the actual training or inference stack. A vendor benchmark may demonstrate a capability under its tested configuration; it is not independent proof of the result in your racks.
Operate the fabric as part of the AI platform
Monitor the signals that explain slow jobs
- Application step time, collective duration and inference p95/p99 latency.
- Per-link utilization, errors, flaps, retransmissions and negotiated speed.
- Queue depth, pause events, congestion marks, drops and buffer occupancy.
- NIC, switch, optic and cable health, including temperature and corrected errors.
- Rail or plane imbalance and the number of active paths per server.
- Storage and management network saturation during provisioning and checkpoint windows.
Correlate these signals with job and tenant identifiers. A flat average utilization graph can hide one congested link that is delaying an entire collective.
Control changes
Keep firmware, driver, switch configuration and congestion-policy changes versioned. Test upgrades on the pilot topology, compare collective and tail-latency results with the previous version, and retain a rollback path. Revalidate optics and breakouts after any port-mode change.
Quick Recap
Common design mistakes and fixes
- Using GPU count as the only sizing input: derive demand from communication patterns, collective sizes and concurrency.
- Mixing storage and GPU traffic without a policy: isolate the paths or define and test traffic classes with measurable guarantees.
- Assuming a leaf-spine diagram is automatically nonblocking: calculate radix, uplinks, bisection bandwidth and degraded-state capacity.
- Ignoring rail placement: document GPU-to-NIC affinity and verify that every rail is active and balanced.
- Treating PFC or ECMP as a complete congestion solution: test queueing, path collisions, pause propagation, marking, drops and recovery together.
- Buying optics by speed alone: match coding, connector, fiber, reach, firmware and support matrices.
- Copying a published reference architecture unchanged: adapt its assumptions for your servers, scale, rack limits, software and failure objectives.
- Approving on link-rate sums: require representative application measurements and concurrent-job tests.
A practical decision checklist
- The workload, collective pattern, scale and growth stages are documented.
- InfiniBand and Ethernet/RoCE have been compared on application performance, tail behavior, resilience, operations and lifecycle cost.
- GPU east-west traffic has an explicit isolated or rail-optimized design.
- Host injection, uplink capacity, radix, bisection bandwidth and oversubscription are calculated for normal and degraded states.
- NICs, switches, optics, cables, firmware, drivers and orchestration versions are validated as a supported system.
- Storage, management and customer-network connectivity have their own capacity and availability objectives.
- Congestion behavior has been tested with realistic all-reduce, all-gather, all-to-all, concurrent jobs and microbursts.
- Link, NIC and switch failures have measured recovery procedures and documented blast radii.
- Telemetry exposes application latency alongside queue, link, congestion and hardware-health data.
- Published reference architectures are being used as starting points, with their hardware-specific assumptions clearly identified.
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