Beyond Copper: Fabric.AI and Kopin Model Up to 60% Increase in GPU Utilization in a Neural I/o™-Powered Data Center
Key Highlights
- ➤Fabric.AI (FABC) and Kopin (KOPN) began initial Neural I/o transmission-link testing
- ➤Neural I/o models 50% to 60% training utilization versus 35% to 45%
- ➤Neural I/o targets 75% to 83% lower interconnect energy
- ➤One-hop compute domain modeled to expand from 72 GPUs to 576 to 1,152
- ➤Public Neural I/o technology demonstration targeted for January 2027
- ➤First-generation Neural I/o 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. 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. 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.”
Joint white paper finds today's AI GPUs sit idle half to two-thirds of the time due to copper interconnect limits; MicroLED optical design modeled to cut interconnect energy ~80% and expand the one-hop AI pod from 72 GPUs to ~1,000, with a public demonstration slated for January 2027
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.
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