Scale-across is an emerging architecture for training one AI model across GPUs installed in multiple data centers, campuses or regions. The key 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 to behave as one system.
What scale-across means
Ciena defines scale-across as the ability to create “one logical AI training environment” even when the GPUs are physically distributed across campuses or regions. In a conventional data-center interconnect (DCI), sites usually remain independent facilities connected for replication, application traffic, redundancy or workload distribution. In scale-across, the network itself becomes part of the coordinated computation: it must behave like a deterministic extension of the intra-data-center fabric.
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That distinction matters because distributed training repeatedly exchanges model updates and synchronization traffic. A link that is acceptable for backup or asynchronous application replication may be too variable for tightly synchronized training.
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Why distance is a boundary rather than a simple limit
Greater separation generally makes the engineering harder, but distance alone does not determine whether a design works. Packet loss, congestion and changing latency can all reduce training efficiency. Longer routes also introduce more optical impairments, route complexity and opportunities for latency variation.
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- Latency: propagation time increases with route length and can extend synchronization cycles.
- Loss: retransmissions and recovery consume capacity and disrupt coordinated work.
- Jitter and asymmetry: inconsistent delivery times make collective operations less predictable.
- Congestion: bursts from many GPU endpoints can interfere with the traffic pattern that training expects.
- Optical conditions: long spans require careful management of signal quality, amplification and protection.
The practical design target is therefore deterministic delivery with enough headroom, not a universal maximum-distance number.
Near and far scale-across designs
| Architecture | Typical setting | Optical and route approach | Main engineering burden |
|---|---|---|---|
| Near scale-across | Campus, metro or regional sites | 800G coherent pluggables, Raman-assisted reach extension where useful, and optical protection; the aim is to avoid intermediate amplifier sites when possible | Keeping latency, loss and protection deterministic while ramping capacity |
| Far scale-across | Broader regional or inter-regional routes | Intermediate line-amplifier sites, more fiber pairs, efficient amplification and higher photonic density | Route-level engineering, amplifier planning, power, space and operational complexity |
| Conventional DCI | Independent facilities exchanging data or maintaining replicas | Designed around application and facility connectivity rather than a single synchronous training fabric | It may not provide the deterministic behavior required by tightly coordinated distributed training |
Near scale-across is not automatically easy: the network still has to meet the training system’s loss, congestion and synchronization requirements. Far scale-across adds more physical infrastructure and more failure and maintenance points.
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Why coherent optics matter
Ordinary 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 keep high-rate signals usable over metro and regional fiber spans. Coherent pluggables place transponder functionality directly in router or switch ports, reducing the need for separate standalone DWDM transponders.
Ciena describes scale-across as requiring both packet-layer congestion management and an optical foundation. Depending on reach, that foundation can include Raman assistance, optical protection, line amplifiers, dense photonics and deliberate route engineering. Coherent technology is consequently becoming an enabling component of distributed AI training rather than an optional long-haul add-on.
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How the network changes the AI-cluster design
Capacity planning
GPU count is only part of the calculation. Engineers must size inter-site links for collective communication, account for bursts and preserve headroom when workloads or cluster membership change. A design that works at average utilization can still fail when synchronization traffic arrives simultaneously from many accelerators.
Congestion control
Packet-layer controls must prevent one traffic class or route from starving training collectives. Congestion behavior should be measurable and predictable, with telemetry tied to the training scheduler and the optical layer.
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Synchronization and resilience
Protection paths, maintenance procedures and failover behavior need to be evaluated for their effect on training synchronization. A resilient design is not merely one with a backup fiber; it is one whose recovery behavior remains within the training system’s tolerance.
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Scale-across can multiply optical ports, line-system equipment, rack units and power requirements compared with conventional DCI. Those costs appear at both ends of the route and, for far designs, at intermediate amplifier and photonic sites.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What the published figures actually show
Cisco modelling from 2026, as reported by The Register, estimates a scale-across optical footprint at roughly 14 times a conventional DCI baseline. The same modelling describes 12,000–32,000 coherent optical ports for two 100MW AI sites. These are vendor modelling figures, not independent industry averages, and they should not be treated as a universal bill of materials.
The 400–800Gbps signals over hundreds of kilometers discussed in that 2026 report illustrate the technical challenge. They describe a class of deployment problem, not a requirement that every scale-across project use those exact rates or distances.
Can GPUs in different data centers train one model?
Yes, provided the sites and network are engineered as one distributed system. The deciding questions are whether the inter-site fabric can sustain the required throughput, keep loss and congestion within bounds, deliver predictable synchronization timing, and recover from faults without destabilizing training. Two nearby buildings can fail these tests if their shared route is congested or poorly controlled; more distant sites can be viable when the optical and packet layers are designed for the workload.
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- Define the training pattern: identify collective operations, synchronization frequency, throughput targets and acceptable recovery behavior.
- Classify the reach: determine whether the proposal is campus, metro, regional or inter-regional, then account for the corresponding optical equipment.
- Model worst-case traffic: include synchronized bursts, failover paths, maintenance events and future GPU expansion.
- Engineer the optical layer: select coherent pluggables, Raman assistance, protection, amplifiers, fiber pairs and photonic density appropriate to the route.
- Validate packet behavior: test congestion control, loss, latency variation and telemetry under realistic load rather than relying on idle-link measurements.
- Budget the whole footprint: count ports, power, racks, intermediate sites, spares and operational staffing alongside GPU capacity.
Where scale-across is a poor fit
A conventional DCI design is usually more appropriate when facilities only need asynchronous replication, ordinary application connectivity, backup, or independent workload scheduling. Scale-across adds substantial optical and operational complexity; using it without a workload that benefits from tightly coordinated multi-site training can increase cost without improving results.
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