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Scale-across AI links GPUs in separate campuses, data centers or regions so they operate as one logically coordinated training environment. The practical limit is not a fixed number of kilometers. It is whether the inter-site network can provide predictable latency, low packet loss, controlled congestion and sufficiently deterministic synchronization for distributed training.
What “scale-across” means
Ciena describes scale-across as building “one logical AI training environment” even when the GPUs are physically distributed across campuses or regions. That makes the network part of the computation itself. In a conventional data-center interconnect (DCI), separate facilities may exchange replicated data, application traffic, backups or independent workloads. In a scale-across design, the inter-site fabric must behave more like an extension of the servers’ local network.
The distinction matters because distributed training repeatedly exchanges model parameters, gradients and synchronization signals. A link that is adequate for asynchronous replication can still introduce enough delay, loss or jitter to leave expensive GPUs waiting.
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| Architecture | Where compute sits | Network role | Primary engineering concern |
|---|---|---|---|
| Scale-up | Within one server or tightly coupled system | Local interconnects join processors and accelerators | Maximum local bandwidth and low latency |
| Scale-out | Across many servers in one facility or closely coupled cluster | Data-center fabric coordinates distributed workers | East-west bandwidth, congestion control and synchronization |
| Scale-across | Across campuses, metro sites or broader regions | Inter-site network becomes a deterministic extension of the training fabric | End-to-end latency consistency, loss, route behavior, optical reach and resilience |
| Conventional DCI | Separate facilities | Connects otherwise independent environments | Replication, application exchange, redundancy or workload movement |
Why distance creates a boundary
Distance increases propagation delay, but kilometers alone do not define feasibility. Distributed training is sensitive to packet loss, congestion and variation in latency. Longer routes also make optical impairments, fiber-path complexity and restoration planning harder to control. A short route with severe congestion can perform worse than a longer route engineered for predictable delivery.
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The design target is therefore deterministic capacity and behavior: enough bandwidth for synchronization bursts, congestion management that prevents queues from expanding, and latency variation low enough that workers remain coordinated. As sites move farther apart, maintaining those properties generally requires more route engineering and optical infrastructure.
Near and far scale-across designs
Near scale-across
Near scale-across covers campus, metropolitan and regional arrangements. The objective is to extend reach while avoiding intermediate amplifier sites where possible. Ciena highlights 800G coherent pluggables, Raman-assisted reach extension and optical protection for these deployments.
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These designs can use existing regional fiber routes, but they still need careful path diversity, packet-layer congestion controls and a measured latency budget. “Near” is a design category, not a universal distance threshold.
Far scale-across
Far scale-across covers broader regional or inter-regional routes. Intermediate line-amplifier locations become unavoidable, so the optical plan must account for efficient amplification, additional fiber pairs, higher photonic density and route-level engineering. Protection and restoration must be designed across more physical elements, increasing operational complexity.
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| Design type | Typical geography in the architecture | Optical and route features emphasized |
|---|---|---|
| Near scale-across | Campus, metro or regional sites | 800G coherent pluggables, Raman-assisted reach extension and optical protection; avoid intermediate amplifier sites where possible |
| Far scale-across | Broader regional or inter-regional routes | Intermediate line amplifiers, efficient amplification, more fiber pairs, dense photonics and route-level engineering |
Why coherent optics matter
Short-range data-center optics are not designed to carry 400–800Gbps signals across hundreds of kilometers. Coherent optics use advanced modulation and digital signal processing to preserve high-rate signals over longer fiber spans despite loss and dispersion. Coherent pluggables place transponder functionality inside router or switch ports, reducing the need for separate standalone DWDM transponders.
That integration can simplify deployment and increase port density, but it does not remove the need for a properly engineered photonic layer. Fiber characteristics, amplification, protection paths and operating margins still determine whether a route can deliver the required service.
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Ciena executive Brodie Gage describes the objective this way: “Scale across extends AI training beyond a single data center—enabling one AI model to be trained across GPUs located in different campuses or even regions.” The company also says, “For the first time, coherent optical technology is becoming an essential enabler of distributed AI training architectures.”
The network stack required for one training environment
- Packet-layer control: congestion management must handle synchronization traffic and prevent transient queues from creating large latency swings.
- Optical foundation: coherent transport, suitable modulation and digital signal processing carry high-rate traffic over metro and regional spans.
- Route engineering: planners must account for fiber length, impairments, amplifier locations, protection paths and restoration behavior.
- Capacity planning: links need enough headroom for synchronized bursts, not merely an average-throughput target.
- Operational determinism: telemetry, fault isolation and change control must cover the packet and photonic layers together.
Scale, power and space implications
Making sites behave as one training system can multiply infrastructure requirements compared with conventional DCI. Cisco modelling from 2026, reported by The Register, estimates roughly 14 times the optical-port, power and space requirements of a conventional DCI baseline in the scenario it examined. The same modelling estimates 12,000–32,000 coherent optical ports for two 100MW AI sites.
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Those figures are vendor modelling and a technical scenario, not independent industry averages. They illustrate the planning effect: connecting very large AI sites is not just a matter of purchasing faster line cards. Port counts, optical shelves, power budgets, rack positions, fiber pairs and maintenance procedures can all grow substantially.
Can GPUs in different data centers train one model?
Yes, provided the software stack supports distributed training and the inter-site network meets its timing, loss and capacity requirements. The phrase “can” should not be read as a guarantee for every geography or model. A route may be physically available yet unsuitable if congestion, packet loss, latency variation or recovery behavior causes workers to stall.
Teams should validate the complete path under expected synchronized load, measure tail latency and loss rather than averages alone, and test failure and restoration scenarios. If the network cannot behave predictably, placing independent training jobs at each site may deliver better utilization than forcing a single tightly synchronized job across the boundary.
A practical design checklist
- Define the training system’s bandwidth, latency, loss and synchronization targets.
- Classify the topology as near or far scale-across based on route and optical requirements, not a distance label alone.
- Map diverse fiber paths, amplifier sites, protection routes and restoration procedures.
- Select coherent optics and pluggables rated for the required reach and capacity; do not assume short-range data-center optics will suffice.
- Engineer packet congestion controls together with the photonic transport layer.
- Budget optical ports, power, rack space, fiber pairs and operational staffing for the full two-way design.
- Run failure, congestion and capacity tests before committing production training workloads.
What the boundary really is
Scale-across is best understood as a network-behavior boundary. Campus, metro and regional sites can function as one logical AI environment when the transport system supplies predictable delivery and adequate capacity. Farther routes remain possible, but they demand more optical equipment, route diversity, amplification and operational discipline. The decisive question is not “How many kilometers apart are the GPUs?” but “Can the complete network keep distributed training synchronized under real load and failure conditions?”
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