Arista 7800R4 Series AI Spine Line Card

The Arista 7800R4 Series AI Spine Line Card is a next-generation modular line card designed for deployment within the Arista 7800R4 chassis, targeting hyperscale AI training infrastructure where spine-layer bandwidth, latency determinism, and buffer depth are mission-critical. Each line card leverages Arista's latest Etherlink ASIC generation to deliver native 800G Ethernet ports — supporting both 800GbE single-lambda coherent interconnects and breakout configurations to 2x400G or 8x100G — enabling fabric operators to scale AI training clusters without optical recabling as GPU pod density grows. The architecture is expressly optimized for east-west traffic patterns generated by distributed deep learning workloads, with hardware-enforced Quality of Service, lossless fabric operation via Priority Flow Control (PFC), and Explicit Congestion Notification (ECN) tuned for RDMA over Converged Ethernet (RoCEv2) transport.

At the heart of the 7800R4 line card is Arista's deep-buffer design philosophy, providing tens of gigabytes of shared packet buffer per line card to absorb incast bursts characteristic of collective communication operations such as AllReduce, AllGather, and ReduceScatter in frameworks including NCCL, PyTorch Distributed, and DeepSpeed. Unlike shallow-buffer merchant silicon, the Arista 7800R4 line card maintains sub-microsecond average forwarding latency under nominal load while gracefully absorbing multi-millisecond microburst events without dropping frames, which directly translates to higher GPU utilization and reduced job completion times in large transformer model training runs.

The line card integrates natively with Arista EOS (Extensible Operating System), enabling unified control-plane management across the full 7800R4 chassis through a single pane of glass via Arista CloudVision. Advanced telemetry features including per-flow latency histograms, fine-grained buffer occupancy streaming via gNMI/gRPC, and integration with Arista's AI Networking Analytics (ANNA) platform give network operations teams unprecedented visibility into fabric health. Support for BGP-EVPN, OpenConfig, and standard YANG models ensures compatibility with modern intent-based and infrastructure-as-code operational toolchains prevalent in AI cloud environments.

Ideal for

• Large-scale GPU cluster spine fabric interconnecting thousands of NVIDIA H100/H200 or AMD MI300X nodes for foundation model pre-training workloads requiring lossless RoCEv2 transport
• Hyperscale AI inference serving infrastructure where spine-layer 800G links aggregate traffic from hundreds of inference accelerator leaf nodes with strict latency SLOs
• Multi-tenant AI-as-a-Service platforms requiring logical fabric segmentation via BGP-EVPN/VXLAN across shared physical spine infrastructure
• High-performance computing (HPC) cluster fabrics running MPI-based simulation workloads that generate synchronized incast traffic requiring deep buffer absorption
• Disaggregated storage-compute AI architectures where all-flash NVMe-oF storage arrays communicate with GPU compute pods across a unified 800G spine layer
• AI factory network refresh programs replacing legacy 400G spine tiers with 800G-native line cards to double fabric bisection bandwidth without chassis forklift upgrades

Technical specifications

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