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Chapter 7: Topology Design -- From One Node to a SuperPOD · Part 4 of 11

Act 3 -- DGX BasePOD and SuperPOD: NVIDIA's reference designs

Building the model…

Eight scalable units: 2,048 GPUs, 64 rail leaves and 32 spines, the ceiling for a two-tier fabric of radix-64 switches. Slide down to see how the counts fall out of the radix.

NVIDIA publishes two reference architectures for DGX clusters. Understanding their exact port allocation and switch counts gives you a concrete framework for topology calculations.

NVIDIA reference designs — BasePOD vs SuperPOD

Nodes
32
Total GPUs
256
Fabric stages
2
Leaf switches
8
Spine switches
32
Core switches
—
Click any row to expand detail

DGX BasePOD

Scale: Up to 32 DGX H100 nodes = 256 GPUs

Compute fabric structure (2-stage, non-blocking):

  • 8 leaf switches (one per rail), each: 32 downlinks to DGX NICs + 32 uplinks to spine
  • 32 spine switches, each: 8 downlinks to leaf switches (one per leaf)
  • Switch hardware: Spectrum-X SN5600 (64 ports x 400G each)

How the port math works: Each SN5600 has 64 ports. A leaf switch uses:

  • 32 ports facing DGX nodes (4 DGX nodes x 8 NICs each? No -- 32 nodes x 1 NIC per rail = 32 downlinks)
  • 32 ports facing spine switches

A spine switch uses all 8 ports to connect to the 8 leaf switches. All 32 spine switches do the same, giving each leaf switch 32 uplink paths -- one to each spine switch.

Bisection bandwidth check: Split 32 nodes into two halves of 16. Each node has 8 NICs at 400G = 3.2 Tb/s per node. 16 nodes = 51.2 Tb/s of potential traffic crossing the bisection. How much spine bandwidth crosses the cut? Each leaf switch contributes 32 uplinks x 400G = 12.8 Tb/s to the spine layer. 8 leaf switches x 12.8 Tb/s = 102.4 Tb/s total spine bandwidth. Split evenly = 51.2 Tb/s per half. Full bisection bandwidth achieved.

AllReduce on a BasePOD can run at exactly line rate, bounded only by NIC speed.

Real deployment: The NVIDIA DGX BasePOD wiring diagram. Shows 32 DGX H100 nodes across 4 compute racks, connected to 8 leaf switches, which connect to 32 spine switches in a separate switch rack.

![DGX BasePOD reference wiring diagram -- 32 nodes, 8 leaf, 32 spine] Source: NVIDIA DGX BasePOD Reference Architecture document, available at developer.nvidia.com/dgx-basepod Image placeholder: The NVIDIA BasePOD documentation contains an official topology diagram. Search "NVIDIA DGX BasePOD reference architecture PDF" for access.

DGX SuperPOD

Scale: Up to 256 DGX H100 nodes = 2,048 GPUs (standard configuration)

Compute fabric structure (3-stage):

  • 64 leaf switches (8 rails x 8 groups of 8 nodes per rail)
  • 64 spine switches (connecting leaf to core)
  • Additional core layer for inter-group traffic

Why 3 stages are needed: With 256 nodes and 8 NICs per node, you have 2,048 NIC connections. Even with 64-port switches, 2,048 / 32 (downlinks per switch) = 64 leaf switches. 64 leaf switches need 64 uplink ports each = 4,096 uplinks. With 64 ports per spine switch and all ports used for downlinks = 64 switches handle only 64 x 64 = 4,096 connections. This works for a 2-stage design, but the bandwidth at the spine layer would be oversubscribed if all 256 nodes tried to AllReduce simultaneously.

The SuperPOD uses a 3-stage design with a core layer to maintain non-blocking bandwidth across the full 256-node cluster.

BasePODSuperPOD
DGX H100 nodes32256
Total GPUs2562,048
Total NICs2562,048
Fabric stages23
Leaf switches864
Spine/agg switches3264+
Core switches064
Max bisection bandwidthFullFull (with correct config)
AllReduce hop count24
DGX H100 nodes
BasePOD32
SuperPOD256
Total GPUs
BasePOD256
SuperPOD2,048
Total NICs
BasePOD256
SuperPOD2,048
Fabric stages
BasePOD2
SuperPOD3
Leaf switches
BasePOD8
SuperPOD64
Spine/agg switches
BasePOD32
SuperPOD64+
Core switches
BasePOD0
SuperPOD64
Max bisection bandwidth
BasePODFull
SuperPODFull (with correct config)
AllReduce hop count
BasePOD2
SuperPOD4

The SuperPOD's extra stages double the AllReduce hop count -- 2 hops in BasePOD (down to leaf, up through spine), 4 hops in SuperPOD (down to leaf, up to spine, up to core, back down). Each hop adds microseconds of latency. For a training run that executes millions of AllReduce steps, the latency difference compounds.

This is why NVIDIA's reference designs use the highest-radix switches available -- to minimise the number of fabric stages needed and keep AllReduce hop counts low.


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