AI Cluster Fabric Planner & Spine-Leaf Architect

Simulate high-bandwidth network backbones for large-scale GPU training pods. Compare Cisco Silicon One / Ultra Ethernet vs. InfiniBand fabrics, analyze optical transceiver power, tail latency, and non-blocking switch topologies.

Presets:
Bisection Bandwidth 3.28 Pbps Full-duplex cluster throughput
Total Switches 128 64 Leaf + 64 Spine Tiers
Optical Transceivers 8,192 4,096 Intra-Pod + Inter-Tier
Fabric Power Draw 215.0 kW ~52.5W per GPU networking
Dynamic Spine-Leaf Topology & Packet Flow 2-Tier Non-Blocking
Spine Switches
Leaf / Rail Switches
Compute Nodes (GPUs)
Active Packets
Cluster Network Flow Efficiency 96.4% Line Rate
Est. Fabric Hop Latency
1.25 µs
Tail Jitter / RoCEv2 Drop
< 0.001%
Optics vs ASIC Power
53% / 47%
Component / Tier Quantity Specification
Telemetry Metric Modeled Value Impact on AI Training
Architecture model up-to-date. Ready for deployment export.

Why Telecom & Ethernet Giants Are Powering the AI Wave

As detailed by industry analysts and infrastructure benchmarks, modern AI clusters have outgrown standalone server backplanes. A 100,000-GPU cluster requires over 200,000 high-speed optical connections and dozens of petabits per second of bisection bandwidth. Companies like Cisco and established networking vendors have staged a major resurgence by offering open Ultra Ethernet Consortium (UEC) standards, high-radix silicon switches (such as Cisco Silicon One G200), and robust optical backbones that compete with proprietary InfiniBand fabrics.

Spine-Leaf & Rail Optimization

Rail-optimized fabrics map identical GPU ranks across nodes directly into dedicated leaf switches, minimizing intra-chassis crossbar contention and keeping GPU-to-GPU collective communications within single-hop domains.

Optics & The Power Wall

At 800G and 1.6T speeds, optical transceivers account for up to 50% of the entire network fabric's energy consumption. Linear Pluggable Optics (LPO) and Co-Packaged Optics (CPO) eliminate power-hungry DSP chips to save critical megawatts in AI datacenters.

RoCEv2 vs. InfiniBand

While InfiniBand offers credit-based flow control, modern RoCEv2 Ethernet with packet spraying (packet-by-packet multi-pathing instead of flow hashing) achieves comparable or superior effective bisection bandwidth without vendor lock-in.

How is bisection bandwidth calculated in this tool?

Bisection bandwidth represents the maximum data rate across the narrowest cut dividing the cluster into two equal halves. For a 1:1 non-blocking fat tree, bisection bandwidth equals (Total GPUs × Fabric Port Speed) / 2. When oversubscription is applied, spine bandwidth scales inversely with the leaf-to-spine uplink ratio.

What is the difference between Pluggable, LPO, and CPO optics?

Standard pluggable transceivers contain internal digital signal processors (DSPs) consuming ~14W each at 800G. Linear Pluggable Optics (LPO) removes the DSP, relying on the host switch SerDes to drive signals directly, reducing power to ~8W. Co-Packaged Optics (CPO) mounts silicon photonic engines directly on the switch substrate, dropping transceiver power to ~5.5W.

Can this configuration be exported to datacenter design documentation?

Yes. The 'Export Fabric Spec' button generates a structured JSON manifest containing calculated tier counts, cable schedules, optical power budgets, and bisection bandwidth ratings formatted for integration with Terraform, NetBox, or datacenter planning sheets.

Bisection Bandwidth and Non-Blocking Fabric Scaling

Read the explanation

In large AI training clusters, thousands of GPUs stream parameter gradients through leaf switches up to non-blocking spine switches. Bisection bandwidth measures the maximum rate across the narrowest cut dividing the cluster into two equal halves. At a one-to-one non-blocking ratio, four thousand ninety-six GPUs at eight hundred gigabits provide sixteen thirty-eight point four petabits per second modeled bisection capacity. Selecting a two-to-one oversubscription halves the spine uplink channels, saving switches and optical power while reducing modeled throughput to eight nineteen point two petabits.

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