What happened
Hyperscalers are spreading GPU clusters across campus data centers 2-20 kilometers apart and linking them with high-speed optical networks—a model called 'scale-across'—to overcome single-data-center limits in power, space, and cooling. As AI interconnect bandwidth rises to 800G and 1.6T, coherent optics are replacing older transmission technology, but vendors are now rolling out cheaper variants (coherent lite and multi-rail designs) to make large-scale deployments affordable.
Why it matters
Single data centers face hard caps on power supply, floor space, and cooling capacity, blocking the expansion hyperscalers need for AI training and inference at scale. Scale-across solves this by distributing workloads across multiple sites linked by fiber. Coherent optics—the optical technology that enables this—were originally built for expensive long-haul networks; the new cost-cutting designs make coherent feasible for AI infrastructure, removing a major barrier to hyperscaler expansion.
What to watch
Optical equipment vendors are competing to offer the most cost-effective coherent solutions for AI data centers through innovations like coherent lite (which simplifies digital signal processors and optical components) and multi-rail systems (which share cabinets, power, and cooling across multiple optical rails to cut repeater costs). The supply chain for these components and systems will be a key bottleneck as hyperscalers ramp scale-across deployments.
Summaries like this, in your inbox every morning.
The shift toward scale-across reflects a fundamental constraint hitting hyperscalers as AI workloads grow: no single data center can provide enough power, physical space, and cooling to train and run the largest AI models. Rather than build bigger single facilities, the industry is pivoting to a distributed model where GPU clusters at multiple sites work together seamlessly via high-speed fiber links. This requires optics technology that can push bandwidth to 800G and 1.6T while spanning kilometers between campuses.
Coherent optics are essential for this job—they can handle the bandwidth and distance that older intensity modulation/direct detection (IMDD) technology cannot—but they were originally engineered for expensive long-haul telecom networks, making them too costly for hyperscaler playbooks. The introduction of cost-reducing variants like coherent lite (simplified processors and components) and multi-rail architectures (shared infrastructure across multiple optical systems) narrows that cost gap. This innovation cycle directly enables the business model: as costs fall, scale-across becomes economically competitive, and hyperscalers can justify the capital and operational expense of managing distributed AI clusters.
Pick your industry and the AI tools you use, and get news related to your work every day.
Free · 30 seconds with Google · unsubscribe anytimeWhat is AIToday? →
Ask AI anything about this article. The AI reads this article, earlier AIToday articles, and Wikipedia, and cites its sources. Q&As are published on this page for other readers too.
