Fabric.AI Publishes White Paper With Kopin That Posits How Copper Wires Inside AI Data Centers Can Be Replaced With Massively Parallel MicroLED Optical Channels

The paper models that Neural I/o™, the MicroLED optical interconnect the two companies are jointly developing, could let a 10,000-GPU system deliver 1.3 to 1.6 times more useful training throughput per GPU, finishing the same training run in 23% to 38% less time, because the interconnect’s spare channels connect, synchronize and monitor the system instead of leaving GPUs waiting.

The white paper, "The Data Center in a SerDes-Free World," is authored by Kopin Chief Executive Officer Michael Murray, and Fabric.AI Senior Strategic Advisor James Altucher. It looks past the performance of a single link to ask a system-level question: if the interconnect stops being the constraint, how should an AI data center be built?

The white paper can be found on Fabric A’s site here.

Today, the answer is set by copper, and copper can’t argue with physics. At 200 gigabits per second, a copper link reaches about one meter, and that meter caps a tightly connected AI compute domain at 72 GPUs in a single rack. A 10,000-GPU cluster is therefore roughly 139 separate domains joined by a slower network, and traffic that leaves a rack faces a four- to eight-fold drop in bandwidth. The paper cites typical model FLOPs utilization of 35% to 45% for frontier-scale dense training and 20% to 35% for mixture-of-experts training, meaning the most expensive machines in the world sit idle for half to two-thirds of their cycles, with a meaningful share of that loss traced to the interconnect.