Arista Networks has introduced the EOS Smart AI Suite, a set of advanced capabilities designed to enhance the performance and efficiency of AI clusters. A key feature of this suite is Cluster Load Balancing (CLB), an Ethernet-based solution utilizing RDMA queue pairs to ensure high bandwidth utilization between spine and leaf switches. Traditional load balancing methods often struggle with AI workloads characterized by fewer, high-bandwidth flows, leading to uneven traffic distribution and increased tail latency. CLB addresses these challenges by implementing RDMA-aware flow placement, promoting uniform performance and maintaining low latency across all flows.
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Jag Brar, Vice President and Distinguished Engineer at Oracle Cloud Infrastructure, commented, “Arista’s Cluster Load Balancing feature helps avoid flow contentions and increase throughput in ML networks.” Additionally, Arista’s CloudVision® Universal Network Observability™ (CV UNO™) offers AI job-centric observability, integrating network, system, and AI job data within the Arista Network Data Lake (NetDL™). This integration provides comprehensive insights into AI job health, facilitates deep-dive analytics, and enables proactive issue resolution, thereby ensuring reliability and efficiency in large-scale AI training and inference infrastructures.