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Design a data center interconnect (DCI) around what must cross between sites—not around a transceiver’s headline speed. First determine whether facilities exchange datasets, checkpoints, models, or inference traffic while running separate jobs, or whether GPUs at different sites must participate in the same synchronous job. The first pattern can use the DCI as a transport and routing service; the second makes end-to-end latency, congestion, and job behavior central design constraints. Measure the workload and paths before choosing capacity, reach, or a cross-site GPU networking design.
What kind of multi-site AI workload are you connecting?
A multi-site AI estate is not automatically one distributed GPU cluster. Moving data or serving inference across sites is different from coordinating GPUs at distant facilities in a single tightly coupled training or disaggregated-inference job. That distinction determines whether the interconnect is mainly carrying transfers between independent environments or participating in a time-sensitive compute fabric.
Cisco’s distributed-inference material describes traffic such as model distribution, cache synchronization, collectives, and user-to-inference flows. NVIDIA’s Spectrum-XGS scale-across proposal addresses a more specific case: one training or disaggregated-inference workload spanning sites. Treat these as different workload categories, not interchangeable reasons to stretch a local GPU fabric across a WAN.
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| Workload pattern | What crosses the DCI | What to prioritize |
|---|---|---|
| Independent or loosely coupled sites | Datasets, checkpoints, model files, cache or key-value updates, and user traffic routed to inference capacity | Transfer windows, effective service bandwidth, resumability, data location, routing, security, and capacity during failures |
| Synchronous cross-site job | GPU collective traffic and other exchanges that contribute directly to the same job’s progress | Measured step time, effective throughput, tail latency, jitter, loss, congestion response, and performance under degraded paths |
The first pattern does not by itself require cross-site RDMA or a unified GPU back end. For the second, a nominal link rate is not enough to establish that a job will perform acceptably; validate representative application behavior at the actual site-pair distances.
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What should you measure before sizing the interconnect?
Build a workload inventory and demand matrix before ordering transport. Record each traffic class separately: its synchrony, concurrency, size, rate, timing, and application impact when delayed or interrupted.
Inventory flows by purpose
- Training collectives: Identify all-reduce and all-to-all activity, whether it is confined to one facility or spans sites, how often it occurs, and how it relates to training-step completion.
- Checkpoint and dataset movement: Record transfer sizes, desired completion windows, busy-hour concurrency, and whether a partially completed transfer can resume.
- Model distribution: Establish which sites need each model, how updates are scheduled, and whether distribution competes with latency-sensitive traffic.
- Inference state and requests: Separate cache or key-value synchronization from user-to-inference traffic. Their destinations and application consequences may differ.
- Operations and management: Identify which management or monitoring traffic must cross sites, and keep it distinct from performance-critical GPU traffic.
Cisco’s AI POD guidance describes synchronized GPU collectives on a site-local back-end fabric; its distributed-inference DCI material also identifies model, cache, and collective flows. Use those examples to avoid treating all AI traffic as one undifferentiated bulk flow.
Measure a candidate cross-site job
For a job that may span facilities, collect measurements from the workload and the network together. A useful record includes bytes exchanged per training step, collective frequency, job completion time, effective throughput, tail latency, packet loss and retransmission, and each site pair’s demand at busy hour. State where each metric is observed—for example, at the application, host, DCI border, or transport path—so that unlike measurements are not mistaken for the same thing.
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Separate propagation from service latency
Distance creates a real latency floor, but propagation is only one part of application delay. NVIDIA’s 2025 technical article gives an approximate fiber propagation illustration of 5 nanoseconds per meter, or 5 microseconds per kilometer, one way. It is not an end-to-end round-trip estimate or a maximum supported distance. Optical equipment, forwarding, queues, congestion, and application synchronization add to the path and workload behavior.
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How should each site and the DCI boundary be laid out?
Keep each facility’s GPU back-end, front-end or storage connectivity, and out-of-band management roles legible. Then define an explicit DCI boundary and connect the traffic that needs to leave the site to an appropriate routed and optical or provider transport design. This avoids treating a WAN path as though it were simply another local leaf-spine hop.
Cisco’s training AI POD guidance separates front-end and back-end fabrics, and Dell’s technical example also distinguishes GPU back-end, front-end, and out-of-band roles. These are architectural examples, not requirements that every site copy one vendor’s topology. The useful principle is to know which traffic enters the DCI, where it is classified and routed, and which local fabric is responsible for it.
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In Cisco’s AI inference routing blueprint, the site-fabric encapsulation terminates at a DCI node, which performs a Layer 3 lookup and re-encapsulates traffic toward the backbone. Host-specific reachability can remain available to the DCI, while summarized prefixes are advertised toward the backbone. This is a documented routing design choice; it should not be presented as proof that every possible Layer 2 extension is impossible or prohibited.
The guide states: “From a scalability and operational perspective, Layer 2 extension is not provided beyond the AI frontend fabric.” The statement is specific to Cisco’s AI Inference Routing Guide, version 1.0, updated October 23, 2026. For a proposed design, decide explicitly where Layer 2 ends, which prefixes are advertised, and how route reachability behaves during failure rather than allowing those decisions to emerge accidentally from transport provisioning.
Which transport option fits the site pair?
Choose transport from measured reach, optical conditions, host compatibility, resiliency, and operating economics. Coherent pluggables, dark fiber, point-to-point DWDM, and optical line systems are different building blocks; they are not interchangeable labels for “a fast link.”
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| Transport building block | What it provides | Questions to resolve |
|---|---|---|
| Coherent ZR or ZR+ pluggables | A coherent optical interface option for a supported host and link design | Does the host support the exact form factor and optic? Are reach, fiber condition, optical budget, and any line-system requirements satisfied? |
| Dark fiber | A fiber path on which the deployment supplies or arranges the optical equipment | What are the actual route and fiber characteristics, and what equipment, diversity, and operating responsibilities apply? |
| Point-to-point DWDM | A wavelength-based transport option between endpoints | Which wavelengths, optics, optical budget, and compatible equipment are available on the route? |
| Optical line system, including ROADMs | A line-system approach for optical transport needs beyond a pluggable-only design | What line system, supported transceivers, power, licensing, and operational model does the path require? |
Cisco’s inference guide lists DCO ZR/ZR+ pluggables, dark fiber, point-to-point DWDM, and ROADMs as options. Cisco’s 2026 paper describes a coherent-optics and optical-system portfolio spanning 2 km to more than 3,000 km. That is a product-family range, not a guarantee that one optic, one link design, or a synchronous GPU job will work at every distance in that range.
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How do the main network design choices differ?
There is no vendor-independent winner between the scale-across approaches described in the available material. Compare them on representative traffic and failure scenarios, and make the decision against measured workload outcomes rather than a single product claim.
| Design choice | What it is intended to address | What to evaluate |
|---|---|---|
| Separate sites with DCI for data and inference | Dataset and checkpoint transfers, model distribution, capacity pooling, and inference routing without assuming a shared cross-site GPU back-end | Transfer windows, service bandwidth, routing, resiliency, security, data location, and transport cost |
| NVIDIA Spectrum-XGS scale-across | NVIDIA describes Spectrum-X Ethernet switches and ConnectX-8 SuperNICs with distance-aware, telemetry-driven congestion control and adaptive routing for scale-across workloads | Collective throughput, tail latency, jitter, loss, job completion, and behavior at target site-pair distances and during failures |
| Cisco deep-buffer scale-across proposal | Cisco’s 2026 proposal emphasizes deep-buffer scale-across routers, including the Cisco 8223, to absorb bursts and prevent loss | Whether buffering helps or harms the target workload’s latency and jitter under the same traffic and failure conditions used for other candidates |
The congestion-control philosophies differ. Cisco argues that deep buffers can absorb inter-site bursts and prevent loss; NVIDIA emphasizes distance-aware congestion control and telemetry, and argues against jitter it associates with deep buffers. Those are vendor positions, not a settled general rule. Compare the approaches under the same representative collectives, site-pair distances, and failure cases.
Cisco’s AI POD describes a local RoCEv2 back-end with nonblocking, QoS-controlled fabric behavior, ECN, congestion-notification traffic, scoped PFC, and adaptive load-balancing considerations. That site-local design is not a ready-made recipe for the inter-site link. Do not assume that local PFC behavior, a short-reach 800G OSFP optic, or ordinary ECMP by itself resolves long-distance congestion and latency requirements.
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How do you plan security, resiliency, and operations?
Plan for the path and trust boundaries the traffic will actually cross. If traffic traverses provider or administrative boundaries, define tenant segmentation, geographic constraints, and suitable transport encryption. Cisco’s inference blueprint identifies IPsec, MACsec, and encrypted overlays as possible options, but the right choice depends on which endpoints and segments are trusted; naming a method does not prove that every segment is protected.
Design for path and equipment failure
Identify route diversity and shared-risk paths, not just the number of cables or circuits. A pair of links may still share a route or another failure exposure. Check whether the surviving path and equipment can carry the traffic the workload requires, and model congestion when demand is shifted onto that path. Include planned maintenance and regional demand changes in the same capacity exercise.
Make the design observable
Define how network evidence will be correlated with application outcomes. For training, record effective throughput, latency and jitter, loss, congestion signals, and job-level timing during ordinary and failure operation. For inference, Cisco’s assurance model also calls out time to first token, inter-token latency, completion and failure rates, and correlation with routing, DCI, and endpoint evidence. Document the measurement point for every metric; a link counter and an application percentile do not describe the same thing.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How should you validate a multi-site AI DCI?
Commissioning should use representative transfers and, where the design supports them, representative synchronous collectives across each site pair. Validate both the network path and the application result, and repeat the relevant checks during induced link and node failures. The goal is to determine whether the real workload meets its stated objectives, not merely whether the interface comes up.
- Start with a baseline. Capture path latency and jitter, packet loss, congestion signaling, effective throughput, and application-level outcomes for the workload classes in scope. Record the site pair, route, traffic conditions, and measurement location.
- Exercise the intended traffic mix. Test the relevant collective, checkpoint, dataset, model, cache, or inference flows at representative concurrency. A bulk transfer result alone does not establish synchronous job behavior.
- Induce failure conditions. Test the planned link and node failure cases, and check the capacity and congestion behavior on the surviving route. Include shared-risk path and maintenance scenarios in the design validation.
- Connect infrastructure and application evidence. For inference, correlate time to first token, inter-token latency, completion and failure rates with routing, DCI, and endpoint evidence. For training, relate network behavior to effective throughput and job completion.
- Resolve the observed constraint before scaling. If the application misses its objective, use the recorded path and endpoint evidence to distinguish propagation, equipment, routing, congestion, loss, or insufficient surviving capacity. Revalidate after a change rather than assuming a higher nominal link rate will solve the observed problem.
How do you choose between candidate designs?
Use a common comparison sheet for every candidate. The design that matches a product category or has the largest stated capacity is not automatically the right one for a workload.
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- Workload coupling: Does the design serve independent sites, or a single synchronous job across them?
- Measured performance: What effective bandwidth, end-to-end latency, tail latency, jitter, loss, and congestion behavior does the actual traffic produce?
- Failure behavior: What route and capacity remain after link, node, shared-risk path, or maintenance events?
- Optical fit: Is the reach appropriate for the measured route, and are fiber condition, optical budget, line system, wavelengths, and supported transceivers verified?
- Compatibility: Are the host platform and exact transceiver supported together?
- Isolation and location: Can the design meet tenant segmentation and geographic or sovereignty constraints?
- Operations: Can teams observe application and network behavior together and operate the route, encryption, and failure model?
- Total cost and complexity: What transport and equipment costs, as well as the ongoing operational burden, follow from the complete design?
A design decision should name the workload and the measured path requirements it satisfies. Do not generalize a vendor reference design, a product-family reach range, or a single benchmark into a universal distance limit, oversubscription target, or guarantee for a different site pair.
Frequently Asked Questions
What does Cisco’s 51.2 Tbps figure for the 8223 mean?
Cisco states 51.2 Tbps as the fixed-router capacity of the 8223 in its 2026 product material. That is a product specification, not measured application throughput or proof that a particular multi-site job will achieve that rate.
Does NVIDIA’s 10 km all-reduce result predict performance at other distances?
No. NVIDIA reported up to 1.9× NCCL all-reduce bandwidth at a 10 km multi-site separation versus the off-the-shelf Ethernet comparison used in its 2025 test. It is a vendor-reported result for that comparison, not an independent benchmark or a promise at other distances.
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