Rail vs ToR Architectures: Which Is Best for AI Clusters?

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Traditionally, two approaches have shaped how infrastructure teams build AI clusters—rail-optimized and top-of-rack (ToR)-optimized architectures. So, which is better for AI clusters—rail or ToR? Are these truly the only options? Or are emerging innovations reshaping the landscape?

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Fabric-scheduled Ethernet as an effective backend interconnect for large AI compute clusters

Understanding the two architectures

ToR-optimized architecture

ToR designs are common in cloud and hyperscaler’s data centers. In this configuration, each rack has a ToR switch that aggregates all GPUs inside the rack, with upstream spine switches handling inter-rack traffic (Clos).

Benefits

Simpler to deploy and manage due to better cable management and lower overall cost
Standardized and familiar to network teams (standard leaf-spine design)

Limitations

Inter-GPU traffic across racks involves two or more switch hops
Risk of oversubscription and latency
Not ideal for AI workloads due to less dynamic GPU to GPU connectivity

ToR Optimized Architecture

Rail-optimized architecture

Rail architectures usually connect the same-index GPUs (e.g., GPU0 in every rack) horizontally across racks via dedicated switch layers. This design minimizes latency and switch hops—crucial for workloads like collective operations or model-parallel training.

Benefits

Low-latency, single-hop communication (for same-index GPUs)
Designed for tightly synchronized AI training workloads

Limitations

Cross-rail traffic (GPU1 → GPU2) reintroduces multi-hop paths
High deployment complexity (cabling and more switch layers)
Managing and scaling such systems can be challenging

Rail Optimized Architecture

It’s clear that both architectures introduce limitations when trying to build complicated and large-scale AI workloads. That’s why a hybrid solution is also being utilized.

The hybrid approach: Rail + NVLink/NVSwitch

This hybrid architecture combines rail-style interconnects between same-index GPUs across racks with the proprietary NVLink/NVSwitch fabric within each node or DGX pod.

This approach offers ultra-fast intra-node GPU communication (via NVLink/NVSwitch) and reduced latency for structured, index-aligned inter-node GPU synchronization.

However, even this hybrid approach has limitations:

Rail still breaks at scale, leading to performance and operational challenges
NVLink/NVSwitch are proprietary, limiting flexibility and long-term vendor choice
Lack of fabric-native, cluster-wide scheduling limits performance optimization, especially in multi-tenant environments

Hybrid Rail Optimized

DriveNets Network Cloud-AI: Breaking the ToR ceiling

DriveNets takes a different approach—keeping the simplicity of ToR but transforming it into a fully AI-optimized fabric. By leveraging a scheduled, predictable fabric that connects all ToR switches with 800G of lossless bandwidth, DriveNets makes ToR-based architectures not only viable—but optimal—even at massive scale.

DriveNets’ Fabric-Scheduled Ethernet (FSE) architecture utilizes cell spraying, end-to-end virtual output queues (VOQs) and zero-impact failover, which work together to deliver an optimal networking architecture for large AI clusters.

With FSE, the traditional trade-offs of ToR disappear. You retain the modularity and flexibility of any-to-any GPU connectivity without the cabling complexity and costs typically associated with large-scale rail deployments. At the same time, FSE delivers the lossless, high-performance networking essential for supporting AI workloads at scale.

In short, DriveNets Network Cloud-AI transforms ToR into a fully capable, backend AI fabric that delivers the following key advantages:

Lossless and predictable connectivity
Reduced deployment complexity and cost
Flexible and dynamic any-to-any GPU connectivity
Inherent multitenancy solution without any complex tuning

DriveNets Network Cloud-AI

While traditional ToR and rail architectures still serve foundational roles, their inherent limitations mean that they struggle to meet the demands of large-scale AI workloads. Hybrid approaches offer some improvements but introduce new complexities and dependencies.

DriveNets Network Cloud-AI enables AI cluster builders to retain the simplicity of the ToR model and gain dynamic, any-to-any connectivity that delivers AI-grade performance, making it ideal for large-scale, high-performance AI environments. Its Fabric-Scheduled Ethernet architecture eliminates ToR’s traditional drawbacks and offers the predictable, lossless connectivity required at scale.