Arista 7700R4 Distributed Etherlink Switch

The Arista 7700R4 Distributed Etherlink Switch represents a fundamental architectural advancement in data center networking, purpose-built for the extreme bandwidth and latency demands of large-scale AI and machine learning training infrastructure. At its core, the 7700R4 implements Arista's Distributed Etherlink architecture, which eliminates the traditional spine-leaf hierarchy by enabling any-to-any single-hop connectivity across massive GPU clusters—removing the cumulative latency, complexity, and failure domains inherent in multi-tier topologies. This approach allows AI fabric operators to scale from hundreds to tens of thousands of GPUs without sacrificing the flat, predictable latency profile that collective communication operations such as AllReduce, AllGather, and AllToAll demand for efficient model parallelism.

The 7700R4 is engineered around a distributed chassis model where switching capacity is horizontally disaggregated across multiple line card nodes interconnected via a high-speed internal fabric, presenting a single logical switching domain to connected endpoints. This design enables seamless non-disruptive capacity expansion and provides carrier-grade resiliency through redundant fabric modules, power supplies, and control planes. The platform natively supports RDMA over Converged Ethernet (RoCEv2) with hardware-enforced Priority Flow Control (PFC) and Explicit Congestion Notification (ECN), ensuring lossless transport for GPU-to-GPU communication at line rate. Integrated telemetry via Arista DANZ and deep buffer management prevent congestion collapse during the bursty, all-to-all traffic patterns generated by transformer model training at scale.

As a 2025-Q3 platform launch, the 7700R4 extends Arista EOS (Extensible Operating System) across the distributed fabric, providing a unified, single-image network operating system with consistent APIs, streaming telemetry, and automation hooks compatible with the broader Arista CloudVision management ecosystem. The switch integrates seamlessly with NVIDIA InfiniBand-adjacent workloads via Ethernet-native GPU Direct RDMA, positioning it as a converged AI fabric solution for hyperscalers, cloud providers, national AI research institutions, and large enterprise AI centers seeking to standardize on Ethernet rather than proprietary interconnects.

Ideal for

• Large-scale generative AI and large language model (LLM) training clusters requiring single-hop lossless Ethernet fabric interconnecting tens of thousands of GPUs with sub-microsecond latency
• Hyperscale cloud provider AI supercomputer pods demanding horizontally scalable, non-disruptive fabric expansion without network redesign or traffic disruption
• High-performance computing (HPC) centers running distributed scientific simulations with collective MPI communication requiring uniform, predictable all-to-all bandwidth
• Enterprise AI inference farms requiring high-throughput, low-latency east-west connectivity between GPU servers serving real-time model inference at massive concurrency
• National AI research laboratories building sovereign AI infrastructure with open, standards-based Ethernet fabric as an alternative to proprietary interconnect ecosystems
• Financial services quantitative research and risk analytics platforms combining GPU-accelerated computing with low-latency Ethernet switching for time-sensitive workloads

Technical specifications

Available from Omnixon Global. Submit an RFQ and our team will confirm configuration and availability for your order.