Powering AI Centers with AI Spines

Leaf-spine architectures have been widely deployed in the cloud, a model pioneered and popularized by Arista since 2008. Arista’s flagship 7800 chassis embodies core cloud principles – industry leading scale, a lossless fabric, ultra-low latency, powerful observability and built-in resiliency. It has evolved into the Universal AI Spine, delivering massive scale, predictable performance, and high‑speed interface support. The Arista 7800 is equipped with powerful features such as Virtual Output Queuing (VOQ) to eliminate head‑of‑line blocking and large buffers to absorb AI microbursts and prevent PFC storms.

Changing the face of AI Networking

Accelerators have intensified network needs that continue to grow and evolve. AI networks of tomorrow must handle 1000X or more workloads for both training and inference frontier models. For training, the key metric is job completion time (JCT) the amount of time an XPU cluster takes to complete a job. For inference, the key metric is different; it is the time taken to process tokens. Arista has developed a comprehensive AI suite of features to uniquely manage AI and cloud workload fidelity across the diversity, duration, and size of traffic flows and patterns.

To address this, Arista’s Accelerated Networking portfolio consists of three families of Etherlink Spine-Leaf fabric that we successfully deploy in scale up, scale out and scale across network designs.

AI Spines to the Rescue

The explosive growth in bandwidth demands from XPUs has driven the evolution of the traditional spine into a new class of purpose-built AI spines. Three major factors are contributing to scale.

• Bisectional bandwidth growth: Bisection bandwidth is essentially the throughput across the “out and across” the network. As workloads become more complex and more distributed, the cross-fabric bandwidth must scale smoothly as additional devices are added to avoid bottlenecks and preserve performance.
• Collective degradation: As you scale up or out, collective communication can become a bottleneck. The system must prevent performance from falling off a cliff as more nodes participate.

• Sustained real-world XPU utilization (~75%, not 50%). The goal isn’t a theoretical peak. It’s about keeping the system doing useful work at scale, consistently, under production-like conditions.

Adapting to Massive Scale of AI Centers

For ultra large-scale AI applications, requiring tens of thousands of XPUs to interconnect within a data center, and in some cases as many as 100k parallel XPUs, Arista’s universal leaf-spine design offers the simplest, most flexible, and scalable architecture to support AI workloads. Arista EOS enables intelligent real‑time load balancing that accounts for actual network utilization to uniformly distribute traffic and avoid flow collisions. Its advanced telemetry capabilities, such as AI Analyzer and Latency Analyzer, give network operators clear insight into optimal configuration thresholds, ensuring XPUs can sustain line‑rate throughput across the fabric without packet loss. Depending on the scale of the AI cluster, AI leaf options could range from fixed AI platforms to high-capacity 7800-series modular platforms.

Arista’s Latest AI Spine - 7800R4

Arista’s 7800R4 is a game‑changing alternative to traditional disaggregated leaf–spine designs, which rely on a fully routed L3 EVPN/VXLAN fabric and require many separate boxes connected across multiple racks. Troubleshooting is no longer a single-layer exercise; it requires navigating multiple control-plane and data-plane layers, as well as overlay and underlay interactions. Even routine diagnostics can become time-consuming and error-prone. The 7800R4 AI spine platform eliminates the unnecessary software complexity, elevated power consumption, and operational overhead inherent in disaggregated leaf–spine designs. Instead, it provides an elegant, integrated solution that is significantly easier to troubleshoot, on board and helps alleviate congestion, ensuring reliable job completion times and performance. The 7800 AI spine consolidates the control plane, power, cooling, data forwarding, and management functions into a single unified system. Customers now benefit from a centralized point for configuration, monitoring, and diagnostics—directly addressing one of the most important customer priorities in AI workloads: operational simplicity with predictable RDMA performance, as shown below.

Designed for Resilience

The 7800 fabric is inherently self-healing. The internal links connecting ingress silicon, fabric modules, and egress silicon are designed with built-in speed-up and are continuously monitored during operation. If a fabric link experiences a fault, it is automatically removed from the scheduling path and reinstated only after it has recovered. This automated resilience reduces operational burden and ensures consistent and predictable system behavior.

The 7800 is engineered for high availability, incorporating redundant supervisor modules, fabric cards, and power supplies. All major components—fabric modules, line cards, power supplies, and supervisors—are field-replaceable, ensuring rapid recovery from hardware faults and minimizing service disruption. This offers a level of elegance that disaggregated boxes struggle to match. It employs a scheduled VOQ fabric with hierarchical buffering, enabling packets to move efficiently from ingress to egress without head-of-line blocking or packet collisions. Because buffering occurs at ingress, any congestion-related packet drops are localized and predictable, greatly simplifying root-cause analysis when issues arise. Key 7800 architecture merits include:

• Deep buffer memory absorbs congestion bursts to ensure lossless AI transport
• Packet Loss Control to avoid packet loss
• Hierarchical packet buffering at DCI / WAN boundaries can enable multi-vendor XPU deployment

AI Spines have Arrived

The 7800 AI Spine is the nucleus connecting many distributed topologies and clusters. In a short time, Arista has designed a rich portfolio of 20+ Etherlink switches that enable 400G/800G/1.6Tbps speeds for AI use cases. Arista realizes its responsibility to enable an open AI ecosystem interoperable with leading companies such as AMD, Anthropic, Arm, Broadcom, Nvidia, OpenAI, Pure Storage and Vast Data. AI Networking requires a modern AI spine and a software stack capable of supporting foundational training and inference models that process tokens at teraflop‑scale across terabit‑class fabrics. Welcome to the new era of AI Spines and AI Centers!

References:

• 7800R4

• AI Innovators Video

• AI Networking White Paper