Rail-optimized · Two-tier and three-tier · Coherent DCI

AI Fabric Topologies: Rail-Optimized Designs to 16K GPUs

Match the topology to the GPU count: a rail-optimized two-tier leaf-spine pod up to 4,096 GPUs, a third super-spine tier above that up to 16,384, and coherent DCI to span sites. Pick the smallest non-blocking design that keeps every GPU's link saturated during collectives. All three run on OcNOS-DC over a lossless RoCEv2 (PFC + ECN) L3 fabric, sized here in concrete port-counts on Broadcom Tomahawk 4 and Tomahawk 5.

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up to 16,384GPUs, one design system
1:1non-blocking GPU plane
up to 800GTH5 leaf and spine
1 imageOcNOS-DC, every tier

The smallest non-blocking design that fits your GPU count

An AI fabric topology has one job: keep every GPU's outbound link saturated during a collective without creating tail-latency outliers. The right topology is the smallest one that does this for your GPU count, with a fall-back path for the next size up. Three reference points, with concrete port maths on Broadcom Tomahawk silicon.

One image, every tier of scale
256 GPUs
Entry non-blocking pod
One rack-row of rail-aligned leaves over a small spine tier. Two-tier leaf-spine, 1:1 non-blocking.
8 leaves · 4 spines · TH5 · 2 × 400G per GPU
4,096 GPUs
Rail-optimized 2-tier pod
Rail-aligned leaves with a 1:1 non-blocking spine, the largest two-tier build on Tomahawk 5. Each GPU's 800G lands on two 400G leaf ports. Intra-rail AllReduce stays on the leaf; cross-rail traffic uses the spine.
128 leaves · 64 spines · TH5 · 2 × 400G per GPU
16,384 GPUs
Rail-optimized 3-tier fabric
Above 4,096 GPUs, a super-spine plane joins two-tier pods. DLB at every tier; GLB end-to-end in OcNOS 7.1. Sized for the trillion-parameter training class.
512 leaves · 256 spines · 128 super-spines · TH5 · 800G

A two-tier leaf-spine reaches 4,096 GPUs at 800G per GPU; a 1,024-GPU build is 32 leaves and 16 spines, 48 switches. Above 4,096 GPUs, add the super-spine tier. The same OcNOS-DC image runs every tier, so the fabric scales out with the cluster instead of being redesigned at each step.

Sizing your own cluster? The AI Fabric Design Suite gives a fast first cut: it sizes a non-blocking two-tier leaf-spine pod and flags when you cross into three-tier scale. The reference designs below use the same non-blocking leaf/spine math and add a super-spine tier above 4,096 GPUs, so the switch counts line up with the tool. Rail-optimized here is the wiring discipline of an 8-NIC GPU server (one rail per leaf, so intra-rail AllReduce stays on the leaf) layered on that non-blocking fabric: it changes traffic locality, not the switch count. Use the tool for a ballpark; use these designs for the build.

Two-tier pod, three-tier fabric, and coherent DCI

Three reference topologies cover single-pod, multi-pod, and multi-site AI fabrics, each sized on HCL-listed Broadcom hardware.

Reference design 1

Rail-optimized single pod

Each GPU server's 8 NICs land on 8 dedicated rail leaves, so same-rail AllReduce stays on one leaf and never traverses the spine. A non-blocking spine carries cross-rail flows. The single-pod scalable unit, up to 4,096 GPUs on Tomahawk 5.

Rail-optimized network
Reference design 2

Three-tier rail-optimized fabric

Leaf, spine, and super-spine tiers take the fabric past 4,096 GPUs, to 16,384 in the reference design. Non-blocking within each pod, with the super-spine plane setting the cross-pod ratio. DLB at every hop, GLB end-to-end in OcNOS 7.1.

GLB fabric-wide balancing
Reference design 3

Coherent multi-DC DCI

When a training run spans data halls, extend the fabric with 400G ZR / ZR+ coherent optics on the spine. Transponder-free DCI between sites, with the in-site fabric unchanged.

Coherent DCI

Rail-optimized single pod

Each GPU server has 8 NICs, one per rail (a dedicated xCCL (NCCL / RCCL / oneCCL) collective channel). Each rail is its own dedicated leaf, so all 8 NICs on every server land on a different leaf. AllReduce across rail-N stays inside leaf-N, so there is no east-west pressure on the spine for the dominant collective pattern.

Rail-optimized AI fabric topology: four 800G Tomahawk 5 spines above eight rail leaves, with four GPU servers each mapping one of its eight NICs to a different rail leaf, so intra-rail AllReduce stays on one leaf.
Rail-optimized single pod: each GPU server's 8 NICs map one per rail to 8 dedicated leaves, so same-rail AllReduce stays on the leaf and only cross-rail traffic reaches the spine.

OcNOS pieces: BGP-unnumbered L3 underlay, RoCEv2 lossless (PFC + ECN) on every leaf, DLB at the spine tier. Built on HCL-listed hardware: the 800G scalable unit uses TH5 64x800G leaves and spines (Edgecore AIS800-64D or UfiSpace S9321-64E); the entry 256-GPU pod uses Edgecore AS9736-64D (TH4, 64x400G).

Three-tier rail-optimized fabric: above 4,096 GPUs

A two-tier rail-optimized pod on Tomahawk 5 tops out at 4,096 GPUs at 800G per GPU, when the spines run out of ports. Above that, the same rail-optimized design adds a third tier: leaf, spine, super-spine. Any two GPUs are at most four switch hops apart; same-leaf and same-pod peers are closer.

The hard part on a Clos is spreading flows evenly so no link becomes a hot spot. Approaches run from per-flow ECMP, through dynamic load balancing, to per-packet spray, the model Ultra Ethernet uses with reordering handled at the NIC. A separate family, cell-based scheduled fabrics such as Broadcom DDC, segments traffic into cells and schedules it inside the fabric. OcNOS keeps the GPU plane balanced with DLB and adds fabric-wide GLB in OcNOS 7.1.

The diagram is schematic: it draws a reduced tier count. The 16,384-GPU build is 512 leaves / 256 spines / 128 super-spines on TH5 800G.

Three-tier rail-optimized AI fabric topology: a super-spine tier over a spine tier over leaf switches feeding GPU pods, sized to 16,384 GPUs on 800G Tomahawk 5 with DLB at every hop.
Three-tier rail-optimized fabric: leaf, spine, and super-spine tiers scale a non-blocking GPU plane to thousands of GPUs, with DLB at every hop and fabric-wide GLB in OcNOS 7.1.

OcNOS pieces: eBGP-unnumbered L3 underlay, RoCEv2 lossless (PFC + ECN), DLB at every tier, GLB end-to-end in OcNOS 7.1, and gNMI streaming telemetry to your observability stack. Built on HCL-listed TH5 64x800G chassis throughout.

Subscription is a dial, not a fixed rule. These counts make each 1,024-GPU pod 1:1 non-blocking and use a cost-optimized ~2:1 super-spine for cross-pod traffic, the rail-optimized approach hyperscale Ethernet fabrics rely on (published large-scale designs oversubscribe the top tier far more, because collective traffic stays pod-local). Want maximal any-to-any headroom instead? A fully non-blocking 1:1 build is 512 / 512 / 256 at 16,384 GPUs; only the spine and super-spine counts change. Model either in the AI Fabric Design Suite.

Multi-DC AI fabric: coherent DCI

When a single training run spans more than one data hall, increasingly common for trillion-parameter models, the fabric extends across the WAN. OcNOS-DC supports 400G ZR / ZR+ coherent optics directly on the spine for transponder-free DCI across sites. The fabric inside each site is unchanged.

Multi-data-center AI fabric topology: two leaf-spine sites joined by 400G ZR and ZR+ coherent optics on the spine, extending the fabric across the WAN, no external transponders.
Coherent multi-DC DCI: two AI data centers joined by 400G ZR and ZR+ optics on the spine, extending the fabric across sites without external transponders.

OcNOS pieces: 400G ZR/ZR+ pluggable coherent optics on a DWDM-capable spine or border-leaf port, with gNMI telemetry across sites. No external transponders required. Reach: 400ZR to roughly 120 km amplified; OpenZR+ reaches farther on oFEC.

Design rules of thumb

A handful of rules keep an AI fabric on the right side of the performance and cost curve, whichever reference design you build from.

  • Match the topology to the GPU count. Smallest pods (under one leaf's NIC radix): rail-only is enough. Single-pod scale, up to 4,096 GPUs on Tomahawk 5: rail-optimized leaf-spine. Above that: add a super-spine tier to the same rail-optimized design, with the pod non-blocking and the cross-pod ratio set to the workload.
  • Always 1:1 subscription on the AI plane. Storage and CPU racks can run higher oversubscription ratios. The GPU plane should not.
  • Plan the rail count from xCCL, not from cabling convenience. 8 rails is the current de-facto standard for 8-NIC GPU servers. Do not combine rails into fewer leaves.
  • Pick the silicon by power and density, not the badge. TH4 (25.6T) and TH5 (51.2T) are the workhorses; the choice between them is rack power and breakout-cable cost.
  • Validate against the HCL. Every reference here is built on hardware listed in the OcNOS Hardware Compatibility List; pick from there for first-class support.
RoCEv2 lossless fabric DLB: dynamic load balancing GLB: fabric-wide (7.1) Ultra Ethernet (UEC) EVPN-VXLAN BGP underlay OcNOS feature matrix Validated hardware (HCL) OcNOS-DC AI Fabric solution

AI fabric topology FAQ

What is a rail-optimized topology, and how is it different from rail-only?
Rail-optimized wiring connects each of a GPU server’s 8 NICs to its own dedicated rail leaf, so the dominant same-rail AllReduce traffic stays on one leaf and never traverses the spine. Rail-only is the small-cluster case: a single rack-row of rail-aligned leaves with no spine tier, where cross-rail traffic relies on the GPU scale-up domain. Rail-optimized adds a non-blocking spine so cross-rail flows have a network path.
When does an AI fabric need a third tier?
Only above the two-tier ceiling. On Tomahawk 5 switches, a 2-tier leaf-spine reaches 4,096 GPUs at 800G per GPU, 1:1 non-blocking: each switch runs as 128×400G and each GPU lands on two 400G leaf ports, for 128 leaves and 64 spines. Adding a super-spine tier to the same rail-optimized design extends that up to 16,384 GPUs in the reference designs above. Because most collective traffic stays rail-local, the super-spine plane is sized to the cross-pod ratio you actually need.
Should I use Tomahawk 4 or Tomahawk 5?
Both run OcNOS-DC. Tomahawk 4 (25.6 Tbps, 64×400G) is the cost-optimized choice for entry pods and 400G GPU NICs. Tomahawk 5 (51.2 Tbps, 64×800G) is the workhorse for 800G GPU servers and larger fabrics. Tomahawk 4 has no native 800G, so match the switch to your NIC speed.
Do I need InfiniBand, or is Ethernet enough?
Ethernet is now a first-class AI-fabric transport. RoCEv2 with PFC and ECN delivers lossless RDMA today. Ultra Ethernet (UEC) removes the network-wide PFC dependency using endpoint packet-spray, selective retransmission, and link-level retry on UEC-capable NICs. OcNOS-DC runs the RoCEv2 fabric today, and IP Infusion is a member of the Ultra Ethernet Consortium.
Where does scale-up end and scale-out begin?
Inside a GPU server and its NVLink domain (for example GB200 NVL72), GPUs communicate over the scale-up fabric at terabit speeds. The rail, leaf-spine, and Clos network is the scale-out fabric between servers and pods. Collective libraries use the scale-up domain to move data onto the destination rail first, so the network mostly carries same-rail and cross-pod traffic, which is why 1:1 non-blocking matters most on the GPU plane.

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Datasheet

OcNOS-DC Datasheet

Full OcNOS-DC specification: the EVPN-VXLAN and Ethernet for AI feature set, software SKUs, supported hardware platforms, and the solution ordering guide.

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Solution Brief

OcNOS 800G Lossless AI Fabric

Non-blocking RoCEv2 fabric on Broadcom Tomahawk 4/5 spines: SKU tiers, validated platforms, and deployment architecture.

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Solution Brief

EVPN-VXLAN Data Center Fabric

Carrier-grade leaf-spine data center fabric: symmetric IRB, Type-2/Type-5 routes, and distributed anycast gateway.

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Solution Open AI Fabric The complete 800G AI fabric: open switches, OcNOS-DC, and support under one contract.
You are here AI Fabric Topologies Rail-optimized two-tier and three-tier fabrics, and coherent DCI, sized in real port counts.
Size & build AI Fabric Design Suite Size a GPU fabric: leaf, spine, and super-spine counts with a component and power summary.
New to AI fabrics? Start here What is an AI fabric? What is a GPU fabric? What is lossless Ethernet? What is RDMA? AI networking switches
The technology inside RoCEv2 lossless Rail-optimized network DLB GLB (7.1) Ultra Ethernet Tomahawk 6 at 1.6T DCQCN PFC deadlock InfiniBand vs Ethernet RoCE vs InfiniBand AI fabric architecture Coherent DCI