Beyond Copper: Fabric.AI and Kopin Model Up to 60% Increase in GPU Utilization in a Neural I/o™-Powered Data Center
Fabric.AI and Kopin published a white paper modeling that their jointly developed Neural I/o MicroLED optical interconnect could lift per-GPU training throughput 1.3x to 1.6x in a 10,000-GPU data center, cutting training run time 23% to 38%. The design uses 2,025 modest-speed optical channels at roughly one picojoule per bit versus four to six for copper and SerDes, extending reach from about one meter to tens of meters and growing the one-hop compute domain from 72 GPUs to roughly 576-1,152.
Key figures
- Demo target
- January 2027
- Payload tbps
- 3.2
- Energy per bit
- approximately 1 picojoule per bit vs 4-6 for copper/SerDes
- Cube scale gpus
- approximately 100,000
- Optical channels
- 2025
- Channel speed gbps
- 2
- Modeled domain gpus
- 576 to 1,152
- Utilization current
- 35-45% dense, 20-35% MoE
- Utilization modeled
- 50-60%
- Modeled throughput gain
- 1.3x to 1.6x per GPU
- Training time reduction
- 23% to 38%
- Production silicon target
- 2028
- Single rack gpu limit copper
- 72
- Interconnect energy reduction
- 75-83%
AI analysis
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