Beyond Copper: Fabric.AI and Kopin Model Up to 60% Increase in GPU Utilization in a Neural I/o™-Powered Data Center
Key Highlights
- ➤Neural I/o models 1.3–1.6x more useful training throughput per GPU
- ➤Training utilization modeled at approximately 50%–60%, up from 35%–45%
- ➤Neural I/o targets 2,025 optical channels and 3.2 Tbps payload
- ➤Public technology demonstration targeted for January 2027
- ➤First-generation production silicon targeted for 2028
Expert Statements
James Altucher, Senior Strategic Advisor to Fabric.AI
“Every AI data center in the world is designed around how far copper can carry a signal, and that distance is about one meter”
James Altucher, Senior Strategic Advisor to Fabric.AI
“The rack, the cooling, the density and the 72-GPU ceiling all follow from it. Fabric.AI was founded to remove that constraint. This paper is our system-level view of what replaces it: a row of a thousand GPUs that behaves like one machine, and an interconnect that does far more than move data.”
James Altucher, Senior Strategic Advisor to Fabric.AI
“The most interesting result isn’t raw bandwidth”
James Altucher, Senior Strategic Advisor to Fabric.AI
“When you have 2,025 channels instead of a handful, the spare ones stop being insurance and start doing work. They fix errors, keep a thousand GPUs in step and watch the health of the system while it runs. Copper can’t do that, because copper has no spare channels to give.”
NEW YORK, NY, Oct. 19, 2026 (GLOBE NEWSWIRE) -- Fabric.AI, Inc. (Nasdaq: FABC) (“Fabric.AI” or the “Company”), a fabless semiconductor company developing MicroLED-based optical interconnects for AI infrastructure, today released a white paper with Kopin Corporation (Nasdaq: KOPN) that imagines how the 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?
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