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Design a distributed-training network as two connected but separately engineered tiers: a predictable, high-bandwidth GPU fabric inside each datacenter and a measured, failure-aware WAN between sites. Then choose a training algorithm and parallelism strategy that matches the WAN’s bandwidth, round-trip time (RTT), jitter, cost and outage behavior. A tightly synchronized job can be limited by the slowest inter-site exchange; an asynchronous or low-communication method can use the same sites with far less WAN traffic.
The practical sequence is to characterize the model’s communication, place the most chatty ranks together, isolate GPU collectives from storage and management traffic, select homogeneous RDMA or a heterogeneous RDMA-plus-TCP design, and validate the complete system under congestion and site failures before scaling it out.
Start with the training pattern, not the switches
Network requirements are a consequence of the optimizer, parallelism, batch size, checkpoint policy and failure model. Decide first whether the job requires frequent global synchronization or can exchange updates less often.
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Tightly synchronized training
Data, tensor, pipeline and expert parallelism can all involve communication at different frequencies. In a synchronous job, progress often waits for the slowest participant. Across distant sites, WAN RTT, jitter, retransmissions or a brief link reduction can turn each collective into a barrier. This approach is sensible when sites are close enough and the inter-site service can deliver a predictable bandwidth and latency envelope, but it should not be assumed to behave like an in-rack fabric.
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Asynchronous and low-communication training
Google DeepMind’s 2026 account of Decoupled DiLoCo describes learner units that exchange updates asynchronously rather than synchronizing every training step. In its reported experiment, a 12-billion-parameter model trained across four United States regions used 2–5 Gbps of wide-area networking. Google also reports that the run continued after learner-unit loss and later reintegrated units. Those are results from that company’s setup, not a universal bandwidth guarantee.
In the same account, a comparison across eight datacenters reports 198 Gbps for Data-Parallel and 0.84 Gbps for Decoupled DiLoCo. The first two comparison charts are based on simulated runs. Google reports 27% goodput for Data-Parallel versus 88% for Decoupled DiLoCo under high hardware-failure rates, also from simulated training runs. A reported Gemma 4 benchmark comparison shows 64.4% average accuracy for Data-Parallel and 64.1% for Decoupled DiLoCo. Treat these figures as evidence that communication and failure behavior can change dramatically with the algorithm—not as a sizing rule for another model.
Use a two-tier network architecture
Draw the design as a local GPU communication tier and an inter-datacenter tier. Give each tier its own performance target, oversubscription policy, routing and failure assumptions.
| Tier | Primary purpose | Design priorities | Typical design questions |
|---|---|---|---|
| Inside one datacenter | GPU collectives, reduce-scatter, all-gather and other east-west exchanges | Predictable bandwidth, low latency, lossless or loss-managed RDMA behavior, topology-aware placement and adequate radix | Are communicating GPUs on the same rail, leaf, zone or spine? Where is oversubscription intentional? |
| Between datacenters | Cross-site synchronization or update exchange | Measured throughput, RTT, jitter, distance, route diversity, failure isolation and recurring WAN cost | Can the algorithm tolerate delay? What happens when a site or circuit disappears? |
| North-south and operations | Storage, users, data ingestion, checkpoints, logging and management | Traffic isolation, security, predictable service during training bursts | Can a checkpoint or restore saturate the same links used by a collective? |
NVIDIA’s cross-datacenter NCCL guidance describes both homogeneous deployments, such as InfiniBand (IB) or RoCE inside and between sites, and heterogeneous deployments, commonly RDMA inside each site with TCP between sites. The latter is often more realistic over a carrier or long-haul service. Confirm the NCCL release and feature status used by your framework before committing to an implementation.
Engineer the intra-datacenter GPU fabric
Build around the communication topology
Model the physical paths between GPUs, network interface cards (NICs), switches and racks. A fabric can have excellent port speeds yet poor effective performance if ranks that communicate heavily are placed across an oversubscribed boundary. Reserve enough paths for the collective pattern, not merely enough aggregate ports for the number of GPUs.
Meta Engineering describes a separate backend (BE) RoCEv2 fabric for training and a frontend fabric for data ingestion, checkpointing, logging and storage. Its BE uses a two-stage Clos “AI Zone” and an aggregator layer between zones. Cross-AI-Zone links are deliberately oversubscribed; the scheduler uses GPU-location awareness and a minimum-cut approach to reduce traffic crossing those boundaries. Meta also describes 400G pluggable transceivers over single-mode fiber from rack training switches to spine switches. This is an example of topology and scheduler co-design, not a universal blueprint.
Use rail and plane awareness
NVIDIA’s NVL72 reference architecture describes an RDMA-based, full non-blocking fat-tree compute fabric with leaf-spine organization and rail-optimized connections. It uses two independent compute planes, each with 400 Gb/s links, with load balancing and failure handling across the planes. That reference is tied to a particular hardware generation. Reproduce the principles—independent paths, balanced rails and a defined failure response—only after mapping them to your GPU, NIC and switch generations.
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Decide where oversubscription is acceptable
Keep the paths used by the most frequent collectives as close to non-blocking as the budget permits. Oversubscription can be acceptable between zones when the scheduler keeps high-communication ranks together and the workload’s measured cross-zone volume fits the available capacity. Document the ratio and test it at the intended job scale; do not infer application performance from a switch port count alone.
Choose the inter-site transport
Homogeneous RDMA across sites
An end-to-end IB or RoCE design can preserve a familiar communication model, but it requires compatible switching, NICs, congestion control, routing, operations and a WAN service that can sustain the required behavior over the distance. It also expands the blast radius of configuration errors. Validate loss, queueing, path failover and congestion behavior on the actual route.
Heterogeneous RDMA inside, TCP between
With this model, local collectives use RDMA while the inter-site exchange uses TCP. NCCL’s cross-DC example performs a local reduce-scatter, an inter-site all-reduce and a local all-gather. That hierarchy keeps unnecessary traffic off the WAN: each site combines its local gradients before sending the smaller site-level exchange, then distributes the result locally.
When WAN bandwidth is much lower than intra-site bandwidth, the NCCL guidance emphasizes using multiple inter-site network devices. Spread traffic across independent links and verify that the software actually opens and balances the intended paths.
Can NCCL run across multiple datacenters?
Yes, NCCL can be used in both homogeneous and heterogeneous arrangements described above, but “can run” does not mean that any WAN will deliver acceptable training time. Check the NCCL and framework versions, topology discovery, transport selection, firewall and routing behavior, and the collective algorithm chosen for the job. Benchmark with the same number of sites, GPUs, links and failure assumptions as production.
Size bandwidth from a measured communication budget
No universal minimum bandwidth or latency threshold for multi-site training has been established. A useful engineering calculation is:
required average bandwidth = bytes exchanged by the WAN tier per training interval ÷ time available for that exchange
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Measure bytes and elapsed time with the intended model, precision, optimizer, parallelism and number of sites. Add headroom for protocol overhead, retransmission, concurrent jobs and a degraded-link scenario. Report average throughput and tail behavior separately; a link that meets an average target but periodically drops below it can still stall synchronous collectives.
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| Figure | Source and context | How to use it |
|---|---|---|
| 2–5 Gbps WAN | Google DeepMind, 2026; a 12-billion-parameter model across four U.S. regions using Decoupled DiLoCo | An example of a low-communication, asynchronous design; not a general minimum |
| 198 Gbps versus 0.84 Gbps | Google DeepMind, 2026; Data-Parallel versus Decoupled DiLoCo across eight datacenters; comparison charts based on simulated runs | Evidence that algorithm choice changes WAN demand by orders of magnitude in that setup |
| Up to 1.9× higher all-reduce bandwidth at 10 km | NVIDIA, 2025; Spectrum-XGS test versus off-the-shelf Ethernet reported by NVIDIA | A vendor-reported result for that product and test, not an independent or universal guarantee |
For each candidate design, record site count, distance, RTT distribution, jitter, sustained throughput, packet loss, link-utilization ceiling, and the time to complete a representative collective. Include the cost of additional circuits and the operational cost of keeping capacity idle for failover.
Match parallelism to WAN behavior
NVIDIA’s article for NeMo Framework 25.02 and Megatron-Core 0.11.0 describes adaptive parallelism, hierarchical all-reduce, a distributed optimizer and chunked inter-datacenter communication. It recommends favoring data and pipeline parallelism for cross-site communication because they tolerate delay better than frequent synchronization methods such as tensor, context and expert parallelism. These are version-specific vendor recommendations; test them with your model and framework configuration.
Hierarchical all-reduce
- Run reduce-scatter within each datacenter so each site combines the gradients produced locally.
- Run the all-reduce between datacenters on the reduced site-level data.
- Run all-gather within each datacenter to distribute the result to local ranks.
This sequence reduces repeated WAN transfers when many GPUs share a site. It does not remove the need to provision enough inter-site capacity for the resulting exchange.
Chunk and overlap communication
Split a large inter-site exchange into chunks that can be transmitted while later layers continue computing. Overlap is useful only when the communication schedule, memory budget and framework implementation permit it; excessive chunking can add overhead and create more opportunities for queueing. Measure step time, exposed communication time and GPU utilization rather than assuming overlap is effective.
Separate traffic by operational need
Training collectives should not compete unpredictably with checkpoint writes, data reads, logs or control traffic. The separation can be physical, logical or a combination, depending on the deployment’s risk and budget.
| Traffic class | Examples | Protection measures |
|---|---|---|
| GPU backend | All-reduce, reduce-scatter, all-gather and pipeline exchanges | Dedicated fabric or strict class-of-service, congestion control, reserved capacity and telemetry |
| Frontend and storage | Training data, checkpoints, restores, object or file storage | Separate links or queues; schedule large transfers away from collectives; test restore storms |
| Management and out-of-band | Provisioning, firmware, monitoring, console and recovery access | Independent reachability and rate limits so an overloaded training fabric does not remove control |
| North-south/customer | User access, APIs and external data movement | Firewalls, routing policy and capacity reserved for non-training services |
NVIDIA’s reference architecture distinguishes east-west GPU compute, north-south storage and customer connectivity, and out-of-band management. Meta’s frontend/backend split makes the same operational point. Whether those roles require separate hardware depends on measured contention and the consequences of a shared-fabric incident.
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Place jobs with topology awareness
The scheduler should know the physical location of every GPU, NIC, rail, leaf, zone and datacenter. Prefer placements that keep tensor- or expert-parallel groups inside the smallest low-latency domain. Place data-parallel replicas across sites only when the chosen update method and WAN budget support it.
- Annotate nodes with site, zone, rack, switch, rail and NIC identifiers.
- Express placement preferences and hard constraints for communication-heavy ranks.
- Use a minimum-cut or equivalent placement objective to reduce traffic crossing oversubscribed boundaries.
- Reserve inter-site capacity before admitting another multi-site job.
- Prevent a checkpoint or data-preload operation from silently consuming the collective’s reserved paths.
Placement policy must also account for spare capacity. A job that fills every GPU in every site may have no room to evacuate a failed rack or learner unit.
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Define the failure domains
List the consequences of a NIC, switch, rail, link, router, circuit, datacenter or entire-region failure. A synchronous job may stop at the first unavailable participant; an asynchronous learner design may continue with reduced capacity and later reintegrate it. The recovery behavior is an algorithm property as much as a network property.
Provide path and site redundancy
Use independent inter-site devices and physically diverse circuits where the availability target justifies their cost. Verify that routing, NCCL transport selection and load balancing fail over without creating an oversubscribed single path. Test a circuit withdrawal and a switch failure while a representative collective is running.
Make checkpoints survivable
Store checkpoints where a site outage cannot destroy the only usable copy. Measure checkpoint and restore time over the frontend or storage tier, and ensure that recovery traffic does not starve the backend fabric. For asynchronous training, record learner identity, update order and reintegration state so a recovered unit cannot replay or silently omit updates.
Build and validate the design step by step
- Characterize the job. Record model size, precision, optimizer, parallelism dimensions, global and microbatch sizes, expected step time, checkpoint interval and acceptable loss of progress.
- Measure communication. Run the actual framework at intended scale, capturing bytes per collective, collective frequency, exposed communication time and GPU utilization.
- Partition the hierarchy. Choose the local communication domain, the inter-site exchange domain and the traffic that must remain outside both.
- Select the algorithmic pattern. Compare synchronous training with a low-communication or asynchronous approach when WAN capacity or failure rates make barriers expensive.
- Design the local fabric. Map GPU-to-NIC affinity, leaf-spine paths, rails, zones, oversubscription points and spare links.
- Choose transport. Select end-to-end RDMA only when the WAN and operations can sustain it; otherwise use local RDMA with a supported TCP inter-site tier and hierarchical collectives.
- Set a WAN budget. Specify sustained and peak throughput, RTT and jitter limits, loss tolerance, headroom, route diversity and the degraded-mode capacity.
- Implement isolation. Separate backend, frontend/storage, north-south and out-of-band traffic physically or with tested logical controls.
- Configure placement. Feed topology data to the scheduler and keep communication-heavy groups within the intended domain.
- Test failure and congestion. Inject link reduction, packet loss, switch failure, site loss and concurrent checkpoint traffic; record step-time impact and recovery behavior.
- Set acceptance criteria. Approve the design only when throughput, tail latency, goodput, recovery time and cost remain within the job’s limits at production scale.
Monitor the signals that reveal a bad design
Collect metrics at GPU, host, switch, WAN and training layers. Correlate them by job and step so a network symptom can be tied to an algorithmic event.
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- Training: step time, exposed communication time, tokens or samples per second, goodput, straggler duration and time lost to retries.
- Collectives: operation size, duration distribution, algorithm selected, bytes sent per site and time waiting for the slowest rank.
- WAN: throughput, RTT percentiles, jitter, packet loss, retransmissions, queue depth, route changes and circuit utilization.
- Fabric: per-port errors, congestion marks, buffer occupancy, ECMP imbalance, link flaps and rail utilization.
- Reliability: failed learners, rank restarts, checkpoint age, recovery duration and the number of updates replayed or discarded.
Look at tail values, not only averages. A few long RTT spikes can dominate a synchronized collective even when the daily average appears healthy.
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Common design mistakes and fixes
Treating the WAN as a stretched cluster fabric
Symptom: local benchmarks look excellent, but multi-site steps stall unpredictably. Fix: model the WAN as a separate tier, use hierarchical communication and measure RTT and jitter under realistic load.
Choosing bandwidth from parameter count alone
Symptom: a link sized from model size fails to meet the step-time target. Fix: measure bytes exchanged by the actual optimizer and parallelism configuration, then include protocol overhead, concurrency and degraded capacity.
Allowing storage traffic to consume collective capacity
Symptom: checkpoints cause collective latency spikes. Fix: isolate frontend/storage traffic, rate-limit large transfers or schedule them outside critical communication windows.
Ignoring placement and oversubscription
Symptom: two nominally identical jobs have different scaling results. Fix: expose rack, zone, rail and site topology to the scheduler and constrain communication-heavy ranks.
Assuming RDMA is automatically better over distance
Symptom: operational complexity increases without predictable training gains. Fix: compare a supported heterogeneous RDMA-plus-TCP design with end-to-end RDMA using measured collective performance, failure behavior and total circuit cost.
Quoting a vendor result as a guarantee
Symptom: production performance is below a published figure. Fix: preserve the source’s hardware, distance, workload and test conditions, and reproduce the measurement on the intended topology.
Physical-layer planning
A “400G single-mode optical transceiver” is a relevant product category for a high-bandwidth training fabric; Meta documents 400G pluggable transceivers and single-mode fiber in its topology. Compatibility depends on the switch or NIC, optics standard, wavelength, connector, reach and environmental requirements. Select the optic, cable and port as a tested combination rather than assuming any 400G module is interchangeable.
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Quick Recap
Decision checklist
- Is the job synchronous, asynchronous or deliberately low-communication?
- What WAN bytes, collective frequency, RTT, jitter and throughput were measured with the real workload?
- Which ranks must remain inside a rack, zone or datacenter?
- Where is oversubscription intentional, and does the scheduler understand it?
- Will local RDMA and inter-site TCP be more predictable than end-to-end RDMA?
- Are backend, storage, customer and management paths isolated enough for the failure target?
- What happens to progress, checkpoints and update state when a site disappears?
- Have congestion, route failure and concurrent storage transfers been tested at production scale?
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