Photonic Interconnects

Photonic interconnects solve the most immediate bottleneck in AI data center scaling: the bandwidth wall between chips. As AI model sizes grow beyond what fits on a single accelerator, training requires distributing computation across thousands of GPUs connected by a network fabric. Electrical interconnects are hitting fundamental limits — signal integrity degrades with distance, power consumption scales with data rate, and copper traces can't keep pace with the bandwidth demands of trillion-parameter models.

Co-packaged optics (CPO) places optical transceivers directly on or adjacent to the compute die, converting electrical signals to light at the earliest possible point. This eliminates the lossy, power-hungry electrical path to separate pluggable optics modules. Lightmatter's Passage L200 has demonstrated a record 1.6 Tbps per fiber and over 200 Tbps per package — bandwidth that enables fully non-blocking communication between AI accelerators.

The commercial impact is substantial: Lightmatter claims up to 8x faster AI model training with their interconnect fabric, stemming from reduced communication bottlenecks during distributed training. The company's partnership with Qualcomm signals that CPO is moving from specialized AI infrastructure toward broader adoption in mobile and edge computing.

The broader significance is architectural. Photonic interconnects could enable disaggregated computing — separating memory, compute, and storage into independently scalable pools connected by optical fabric, rather than the monolithic architectures that dominate today.

Key Claims

• 1.6 Tbps per fiber achieved — Lightmatter Passage L200 sets record for single-fiber bandwidth. Evidence: strong (Lightmatter Passage L200)
• 200+ Tbps per package — Co-packaged optics enabling fully non-blocking chip-to-chip communication. Evidence: strong (Lightmatter Passage L200)
• Up to 8x faster AI training claimed — Reduced communication bottlenecks in distributed training. Evidence: moderate (Lightmatter Passage L200)
• CPO moving beyond AI niche — Qualcomm partnership signals broader adoption trajectory. Evidence: moderate (Lightmatter Passage L200)
• First UCIe-compliant optical chiplet — Ayar Labs standardizes optical I/O into the chiplet ecosystem. SuperNova 16-wavelength light source at 8 Tbps. TSMC COUPE partnership makes it accessible to broader semiconductor industry. Evidence: strong (Ayar Labs UCIe Chiplet)
• $500M Nvidia-backed raise signals market conviction — Nvidia sees optical interconnect as essential for next-gen AI systems. Production samples targeted for 2026. Evidence: strong (Ayar Labs UCIe Chiplet)
• 2026-2028 deployment timeline for AI data centers — Early adopters deploying CPO in 2026; broader 800G/1.6T adoption in 2027; photonic interconnects standard for AI-scale networking by 2028. Evidence: moderate (Photonics Shift)
• Manufacturing readiness is the real bottleneck — Photonic component yield, InP laser supply constraints, and workforce skills gap constrain adoption pace more than the physics. Evidence: moderate (Photonics Shift)

Open Questions

• What is the cost premium of CPO vs. pluggable optics at data center scale?
• Can CPO integration avoid yield losses from combining optical and electronic fabrication?
• Will photonic interconnects enable true disaggregated computing architectures?
• How do optical switching fabrics compare to electrical switches for AI cluster networks?

Related Concepts

• Photonic Neural Networks — Compute layer that photonic interconnects serve
• Photonic Tensor Cores — High-density compute requiring high-bandwidth data feeds

Backlinks

Pages that reference this concept:

• Lightmatter
• Ayar Labs