Why This White Paper Matters
Today's AI pod is a single rack of 72 GPUs because copper links at 200 gigabits per second stop working beyond about a meter. A 10,000-GPU cluster is therefore not one machine but roughly 140 small ones, joined by a slower network, and every large training run pays for that boundary thousands of times per hour.
This white paper, by Kopin Corporation and Fabric.AI, describes what becomes possible when the link changes: thousands of parallel optical channels instead of a few fast copper lanes, reach of tens of meters instead of one, and a data center that works more like one computer than a building full of cables.
What You'll Learn
- Why copper's one-meter reach sets the size, density and cooling of today's AI racks
- How Neural I/o™ delivers 3.2 Tbps over 2,025 parallel MicroLED channels in one square millimeter
- How spare channels correct errors silently and keep a thousand GPUs in step
- The light-native row: about 500 to 1,100 GPUs one hop apart, with switches moved to row-center
- The Cube: a modeled path to 100,000 GPUs in one building, each within about a microsecond of every other
- A modeled 1.3 to 1.6 times more training throughput from the same GPUs, and what still has to be true
About the Authors
In collaboration with Kopin Corporation (KOPN)
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