SOLUTION

AI Training Cluster Optical Interconnect: 800G QSFP-DD & DWDM for 10K–50K GPU Fabrics

As GPU clusters scale beyond 10,000 accelerators, the optical interconnect layer becomes the single largest cost and complexity driver. Apex delivers a turnkey, vendor-agnostic optical fabric built on 800G QSFP-DD transceivers, active optical cables, and DWDM multiplexing — validated across leading switch platforms.

50%FEWER LEAF PORTS

18 kWPOWER SAVED

100 GbpsPER LANE PAM4

1The Challenge

Training a single frontier LLM today demands 20,000–50,000 GPUs running nonstop for months. Every GPU exchanges gradients, parameters, and activations with peers thousands of times per second. At 400G per port, the all-reduce traffic alone saturates fabric bandwidth long before the silicon does.

Network architects face four compounding constraints:

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Port Explosion

A 10K-GPU cluster at 400G demands 1,024+ leaf ports and 64 spine switches. Doubling to 20K GPUs doubles the pain.

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Power Ceiling

Each 400G-DR4 module draws ~10W. At 2,000+ modules, the interconnect alone consumes 20 kW before counting switch silicon.

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Cable Density

MPO-12/APC trunk cables eat rack space and block airflow. Managing 512+ fiber pairs per row is a logistics headache.

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End-to-End Latency

Every extra O-E-O conversion in the fabric adds nanoseconds that compound across thousands of all-reduce rounds.

THE ROOT CAUSE

The bottleneck is not the GPU or the switch ASIC — it is the optical interconnect. Moving from 400G to 800G optics cuts port count in half, shrinks the switch radix requirement, and trims per-bit power by 15–25%.

2Solution Architecture

Apex engineers a three-tier optical fabric that scales from intra-rack GPU links to inter-building cluster aggregation:

┌─────────────────────────────────────────────────────┐
│ Tier 1: GPU ↔ ToR Switch (0–100 m) │
│ 800G QSFP-DD SR8 + 800G AOC │
│ 8× 100G PAM4 lanes per link │
├─────────────────────────────────────────────────────┤
│ Tier 2: Leaf ↔ Spine (0–500 m) │
│ 800G QSFP-DD DR8 / FR4 + SMF Duplex │
│ 512× 800G leaf ports → 256 spine ports │
├─────────────────────────────────────────────────────┤
│ Tier 3: Cluster ↔ Inference / DCI (2–500+ km) │
│ 800G QSFP-DD ZR+ + DWDM MUX/DEMUX + EDFA │
│ 1λ = 800 Gbps coherent, 40λ per fiber pair │
└─────────────────────────────────────────────────────┘

Product Selection Matrix

* Quantities are representative for a 10,240-GPU cluster using 8-GPU nodes with 1:1 oversubscription. Adjust for your topology.

3Key Benefits

50%

Fewer leaf ports vs. equivalent 400G deployment

18 kW

Interconnect power saved per 10K-GPU cluster

2×

Bandwidth density per RU vs. 400G-DR4

<1 µs

Added latency per optical link (in-cabinet)

Why Apex Optics

4Deployment Scenario: 10,240-GPU Training Pod

A cloud provider building a new AI training region with 10,240 H100-class GPUs across 1,280 nodes:

BOTTOM LINE

Moving to an 800G optical fabric with Apex transceivers, AOCs, and DWDM MUX saves 8 racks, 18 kW, and 2,048 optical ports per 10K-GPU pod — while cutting fiber count by 75%. The freed power budget goes directly into GPU density.

Designing an AI cluster optical fabric?

Our solutions engineers will model your topology, recommend a product matrix, and provide a full BOM with lead times — typically within 48 hours.

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