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Design the fabric from the workload outward: quantify collective and storage traffic, separate GPU east-west flows from management and north-south services, choose a topology that delivers the required bisection bandwidth at your target scale, then configure RoCE, priority flow control (PFC), routing and congestion controls as one tested system. There is no universal switch model, oversubscription ratio or latency number that fits every AI or HPC cluster.
The practical sequence is therefore requirements → traffic separation → topology → matched hardware and software → RoCE/PFC configuration → representative validation. NVIDIA’s Spectrum-X and AI Factory documents are useful reference architectures, but their settings and scale assumptions are vendor- and platform-specific rather than general rules.
What “low latency” means in an AI or HPC fabric
Cluster applications exchange data continuously between GPUs, CPUs and storage. Distributed training uses collectives such as all-reduce and all-to-all; HPC codes may exchange halo data or synchronize frequently. A fabric that looks fast in a single-link test can still perform poorly when many nodes contend for the same uplinks.
Evaluate latency as a distribution, not a single average. Record median, high-percentile (tail) latency, completion time for collectives, throughput under simultaneous flows, packet loss or retransmission, and behavior during link or node failure. The NVIDIA Enterprise AI Factory design guide discusses tail-latency goals, but it does not provide an independent number that can be applied to every vendor or workload (NVIDIA Ecosystem Architecture).
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Before purchasing equipment, write down the performance objective for each traffic class and the test that will prove it. A useful objective might be a maximum all-reduce completion-time increase at a defined concurrency, rather than an abstract “sub-microsecond” promise.
How do I design a low-latency Ethernet network for an AI cluster?
- Characterize the workload and growth target. Count endpoints, link rates, collectives, storage transfers, management sessions and user-facing services. Estimate simultaneous traffic and acceptable oversubscription.
- Separate traffic by role. Decide which flows require deterministic, lossless treatment and which can use ordinary best-effort Ethernet.
- Select a scalable topology. Size leaf, spine and (if needed) super-spine tiers for endpoint count, port speed, uplinks, bisection bandwidth and expansion.
- Choose a supported hardware/software combination. Match switches, NICs or SuperNICs, optics, cabling, firmware, drivers and orchestration versions.
- Configure RoCE and congestion behavior end to end. Apply the profile documented for the exact reference architecture; do not transplant values from another platform.
- Validate with production-like traffic and failure cases. Measure throughput, latency distribution, congestion response and recovery before admitting jobs.
1. Characterize bandwidth, communication and scale
NVIDIA’s reference-architecture design tenets place bandwidth, traffic separation, topology and management requirements at the start of the process (Design Tenets of NVIDIA Reference Architectures). Use the same discipline regardless of vendor.
Inventory endpoints and link capacity
- List GPU servers, CPU-only nodes, storage clients, service nodes and external gateways.
- Record the physical port speed and number of ports per endpoint. A dual-port adapter may be intended for separate rails or redundant paths; do not count both ports as usable aggregate bandwidth until the application and topology support that pattern.
- Record rack location and the number of leaf uplinks available to each server. This determines the actual path capacity, not the headline NIC rate.
- State the growth target: additional nodes, higher-rate adapters, new racks and expected job concurrency.
Describe the traffic matrix
For each application, identify who talks to whom, how often and in which direction. Distributed training can create synchronized bursts when a collective starts. HPC jobs may generate many small messages whose latency matters more than bulk throughput. Storage checkpoints can create long-lived flows that compete with collectives. Management, telemetry, image distribution and interactive access have different tolerances for delay and packet loss.
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Calculate capacity and oversubscription
For every tier, compare the sum of possible downstream demand with the uplink capacity. A simple planning ratio is:
oversubscription = aggregate downlink capacity ÷ aggregate uplink capacity
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Use the ratio separately for each leaf and spine block and under the concurrency you actually expect. A nominal 1:1 ratio is not automatically nonblocking: port counts, rail wiring, equal-cost paths, buffering and failure conditions still determine whether every source can reach every destination at line rate. The reviewed NVIDIA material does not establish a universal oversubscription ratio or latency target; derive yours from the workload and verify it in tests.
Define bisection bandwidth
Bisection bandwidth is the capacity available when the cluster is divided into two equal groups and traffic crosses between them. State the intended bisection for normal operation and after a single link, port or device failure. This prevents a design from being called “nonblocking” merely because each server has a fast local link.
2. Separate GPU, storage, management and north-south traffic
GPU east-west traffic is the performance-critical path for distributed training and many inference workloads. Management traffic, storage, tenant access and internet-facing services have different security and availability requirements. NVIDIA AI Factory guidance describes isolated GPU compute networks and treats east-west networking as central to distributed workloads (design tenets; NVL72 network logical architecture).
Choose physical, logical or combined separation
| Approach | When it fits | Trade-offs |
|---|---|---|
| Dedicated GPU fabric | Collectives require predictable capacity, strict fault containment or independent maintenance. | Highest equipment, optics and cabling count; requires separate operations and monitoring. |
| Logical separation on a shared fabric | Scale or budget favors shared switches and the design can enforce VLAN/VRF, QoS and policy boundaries. | Shared buffers and links can still create contention; configuration and troubleshooting are more complex. |
| Hybrid | GPU east-west is isolated while storage or service traffic shares a separate, controlled segment. | Requires clear routing and security boundaries between fabrics. |
Document which traffic may share links, which classes receive lossless treatment, where routing occurs and how a failed fabric affects jobs. Logical isolation is not equivalent to physical independence if the same ports, buffers, supervisors or power domains remain common.
Keep north-south paths out of the collective critical path
Place gateways, storage front ends and user access at defined service tiers. Size their links for checkpoint, dataset and result movement without assuming that GPU-to-GPU traffic can borrow the same capacity during a collective. Apply policy so a sudden external transfer cannot consume the queues intended for RoCE traffic.
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3. Select a topology that scales predictably
Leaf-spine or fat-tree
A leaf-spine (fat-tree) fabric gives each leaf multiple equal-cost paths to the spines. You add leafs for endpoints and spines for aggregate capacity, then calculate uplinks and path diversity for the desired bisection. NVIDIA’s RTX PRO AI Factory logical architecture documents a full nonblocking Spectrum-X fabric with rail-optimized GPU connectivity (RTX PRO AI Factory networking logical architecture). Treat that as an example tied to that platform, not a universal prescription.
- Leaf sizing: reserve enough server-facing ports for the current population and enough uplinks for the target oversubscription and failure case.
- Spine sizing: provide sufficient ports and aggregate bandwidth for every leaf, including a maintenance or failure scenario.
- Path symmetry: keep rail connections and routing predictable so a server’s first GPU path does not unexpectedly traverse a congested or lower-capacity link.
- Expansion: leave power, rack space, optics and switch ports for the stated growth target; otherwise a later “scale-out” may force a disruptive redesign.
When a super-spine appears
In NVIDIA’s NVL72 reference architecture, a super-spine layer is introduced at around 1,024 nodes and above (NVL72 network logical architecture). That threshold belongs to the named architecture. Other clusters may need an additional tier earlier or later depending on port speeds, rack blocks, oversubscription, routing scale and failure objectives.
Topology decision checklist
- Can the fabric deliver the required bisection bandwidth with one expected failure?
- Are equal-cost paths and routing behavior documented for the chosen switch operating system?
- Does the design preserve rail locality or other application-aware wiring requirements?
- Are buffers, queue counts and telemetry adequate for the selected traffic classes?
- Can the operations team drain, upgrade and troubleshoot a tier without stopping the entire cluster?
4. Match switches, NICs, optics and software
NVIDIA identifies Spectrum switches, ConnectX NIC/SuperNIC families and BlueField-3 SuperNICs as Spectrum-X components, with Spectrum switches used in the cited leaf-spine reference design (Spectrum-X Ethernet Networking Platform). This establishes a component family, not one universally suitable model.
| Layer | Verify before purchase or deployment |
|---|---|
| Switches | Port speed and density, buffer and queue behavior, routing scale, QoS/PFC support, telemetry, software release and support for the intended topology. |
| Adapters | NIC or SuperNIC speed, RoCE features, firmware, driver, virtualization mode, rail wiring and host PCIe capacity. |
| Optics and cabling | Reach, connector type, breakout support, lane speed, transceiver coding, power and vendor interoperability for every hop. |
| Host and orchestration | Kernel, OFED or vendor driver, container network components, Network Operator release and the exact profile used by the reference architecture. |
Build a compatibility matrix with part numbers and version numbers. A link that comes up proves only physical interoperability; it does not prove correct RoCE marking, PFC behavior, congestion control or application performance.
5. Configure RoCE as an end-to-end system
RoCE performance depends on coordinated endpoint and fabric settings. NVIDIA Network Operator 26.7 Spectrum-X documentation describes a profile containing RoCE, adaptive routing, congestion-control and inter-packet-gap settings, and specifies Ethernet link type and a single VF for the described Spectrum-X optimization (NVIDIA Spectrum-X NIC Configuration).
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Use the exact supported profile
- Identify the switch family, NIC or SuperNIC, firmware, host driver and Network Operator release in your bill of materials.
- Open the configuration page for that release and reference architecture. Record the profile name and every default it changes.
- Confirm the host link type is Ethernet where required and that the virtual-function count matches the documented optimization; the cited Spectrum-X profile calls for a single VF.
- Apply switch-side QoS, marking, PFC and congestion settings that the same profile expects.
- Reboot or reload components as required, then verify negotiated speed, link state, queue counters, RoCE statistics and error counters on every hop.
Do not copy a value from the 26.7 documentation into another NIC generation, switch operating system or orchestration release without checking that combination’s support statement. A setting that is valid for one reference architecture can be harmful or ignored elsewhere.
Adaptive routing and congestion control
RoCE profiles may coordinate adaptive routing and congestion-control algorithms so flows avoid hot paths and react to queue pressure. Inter-packet gap settings can also be part of the profile. Treat these as a coupled system: changing one parameter can alter queue occupancy, marking frequency and the traffic pattern seen by the others. Capture the before-and-after counters during a controlled test instead of assuming that a lower gap or more aggressive control is always faster.
6. Configure PFC without making the whole network “lossless”
Priority Flow Control (IEEE 802.1Qbb) pauses selected traffic classes rather than every class on a link. NVIDIA’s MLNX_OFED documentation gives an example in which RoCE traffic receives lossless treatment while ordinary Ethernet traffic remains best effort (NVIDIA MLNX_OFED Priority Flow Control documentation, version 24.10 LTS Update 2).
Design the priority map
- Choose the class or classes that carry RoCE and define how packets are marked at the host and trusted at each switch hop.
- Map that class to a PFC-enabled queue on every participating port. Keep ordinary management and best-effort traffic on queues that do not pause with RoCE.
- Set ingress admission, headroom and pause thresholds according to the switch and NIC documentation. The sources cited here do not provide a universal priority number or threshold.
- Verify that the mapping survives VLAN, trunk, routing and virtualization boundaries. A single unmarked or non-PFC hop breaks the intended behavior.
- Test congestion and pause storms. Confirm that a blocked RoCE receiver does not spread pause frames into unrelated classes or exhaust buffers.
PFC is one mechanism in a broader design; it does not supply topology, routing, bandwidth or fault isolation. Excessive reliance on pause can turn congestion into head-of-line blocking, so pair PFC with the documented congestion-control and telemetry features for your platform.
7. Validate before production workloads
Build a staged test plan
- Physical and link tests: check every optic, lane, negotiated speed, error counter and redundant path.
- Control-plane tests: confirm routing adjacencies, equal-cost paths, VLAN/VRF boundaries, host addressing and management reachability.
- RoCE function tests: generate traffic between all intended rails and verify marking, queue selection, PFC counters, congestion notifications and retransmission behavior.
- Scale tests: run representative multi-node collectives at the planned concurrency, not only two-node bandwidth tests.
- Contention tests: run collectives alongside checkpoint, dataset, management and north-south flows to expose shared-buffer or uplink contention.
- Failure tests: remove a link, drain a switch, reboot a host and exercise a congested receiver. Record path changes, job impact and recovery time.
- Soak tests: run long enough to reveal thermal, buffer, firmware or queue-counter drift.
Measure the right outputs
- Collective completion time and application throughput.
- Median and tail latency for small messages.
- Per-link utilization and oversubscription under the test matrix.
- PFC pause duration and count, congestion marks, drops, retransmissions and queue occupancy.
- Recovery time and the amount of traffic lost or delayed during each failure.
Keep test configuration, software versions and workload parameters with the results. Vendor performance positioning should be labeled as a vendor claim; the reviewed sources did not identify independent, cross-vendor benchmarks that support a universal latency or throughput figure.
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Comparing candidate fabrics
| Comparison axis | Question to answer |
|---|---|
| Endpoint and bisection bandwidth | Can the fabric sustain the required collective and storage traffic, including the stated failure case? |
| Oversubscription | What is the ratio at each leaf, spine and super-spine tier under expected concurrency? |
| Latency distribution | What are average and tail values during representative load, not just an idle ping? |
| Congestion behavior | How do marking, PFC, adaptive routing and retransmission behave when a receiver or uplink is saturated? |
| Resiliency and isolation | Can a failed component or noisy tenant be contained without pausing unrelated GPU traffic? |
| Scale and operations | How many racks, ports and paths can be added, and how are upgrades and drains performed? |
| End-to-end support | Are switch, NIC, firmware, driver, orchestration and profile versions jointly supported? |
Common failure modes and corrective actions
“The links are fast, but training is slow”
Check collective traffic against the actual bisection and uplink count. Look for a rail wired through a different-capacity path, oversubscribed leaf uplinks, or storage transfers sharing the same queues. Re-run with storage and management traffic isolated to determine whether contention is causal.
“PFC counters rise continuously”
Find the congested receiver or oversubscribed hop, then inspect headroom, queue occupancy, marking and pause propagation. PFC activity is a symptom of pressure, not proof that the fabric is correctly sized. Confirm that best-effort queues are not being paused unintentionally.
“RoCE works between two hosts but fails at scale”
Verify marking and queue maps on every trunk and routing boundary, then test all rails and equal-cost paths. A missing PFC or congestion setting on one intermediate port may be invisible in a short path but emerge when many flows converge.
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“A profile copied from another deployment gives unstable results”
Compare switch, NIC, firmware, driver and Network Operator versions. Return to the profile documented for the exact supported reference architecture; version-specific settings are not portable by default.
“Adding more nodes reduces performance”
Recalculate bisection bandwidth, spine port consumption and failure-case capacity. Confirm that the growth plan did not turn a previously nonblocking block into an oversubscribed tier or introduce a new aggregation layer without matching uplinks.
Procurement and operational checklist
- Document node count, endpoint rates, rail topology, collective patterns, storage concurrency and growth horizon.
- Choose physical or logical traffic separation with an explicit fault and security rationale.
- Publish a port-level topology showing server links, leaf uplinks, spine connections and any super-spine.
- Create a versioned compatibility matrix for switches, NICs, optics, firmware, drivers and orchestration.
- Store the approved RoCE, QoS, PFC, adaptive-routing and congestion-control profile in configuration management.
- Define acceptance tests with workload parameters, latency percentiles, throughput, pause counters and failure recovery criteria.
- Instrument every tier so operators can correlate application slowdowns with queue, link and congestion telemetry.
- Schedule controlled upgrades and retain a rollback path for firmware, driver and network-operator changes.
Bottom line
The best Ethernet topology for an AI or HPC cluster is the one that meets your measured communication matrix and growth target with predictable bisection bandwidth, controlled congestion and verifiable fault isolation. A leaf-spine or fat-tree is a strong scalable starting point, while additional tiers may be appropriate at very large platform-specific scales. RoCE and PFC should be deployed only as a matched, version-qualified system and proven with multi-node, concurrent and failure testing—not inferred from link speed or an idle latency check.
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