<?xml version="1.0" encoding="UTF-8"?>
<reference anchor="I-D.xu-idr-fare" target="https://datatracker.ietf.org/doc/html/draft-xu-idr-fare-03">
   <front>
      <title>Fully Adaptive Routing Ethernet using BGP</title>
      <author initials="X." surname="Xu" fullname="Xiaohu Xu">
         <organization>China Mobile</organization>
      </author>
      <author initials="S." surname="Hegde" fullname="Shraddha Hegde">
         <organization>Juniper</organization>
      </author>
      <author initials="Z." surname="He" fullname="Zongying He">
         <organization>Broadcom</organization>
      </author>
      <author initials="J." surname="Wang" fullname="Junjie Wang">
         <organization>Centec</organization>
      </author>
      <author initials="H." surname="Huang" fullname="Hongyi Huang">
         <organization>Huawei</organization>
      </author>
      <author initials="Q." surname="Zhang" fullname="Qingliang Zhang">
         <organization>H3C</organization>
      </author>
      <author initials="H." surname="Wu" fullname="Hang Wu">
         <organization>Ruijie Networks</organization>
      </author>
      <author initials="Y." surname="Liu" fullname="Yadong Liu">
         <organization>Tencent</organization>
      </author>
      <author initials="Y." surname="Xia" fullname="Yinben Xia">
         <organization>Tencent</organization>
      </author>
      <author initials="P." surname="Wang" fullname="Peilong Wang">
         <organization>Baidu</organization>
      </author>
      <author initials="" surname="Tiezheng" fullname="Tiezheng">
         <organization>IEIT SYSTEMS</organization>
      </author>
      <date month="May" day="21" year="2025" />
      <abstract>
	 <t>   Large language models (LLMs) like ChatGPT have become increasingly
   popular in recent years due to their impressive performance in
   various natural language processing tasks.  These models are built by
   training deep neural networks on massive amounts of text data, as
   well as visual and video data, often consisting of billions or even
   trillions of parameters.  However, the training process for these
   models can be extremely resource-intensive, requiring the deployment
   of thousands or even tens of thousands of GPUs in a single AI
   training cluster.  Therefore, three-stage or even five-stage CLOS
   networks are commonly adopted for AI networks.  The non-blocking
   nature of the network become increasingly critical for large-scale AI
   models.  Therefore, adaptive routing is necessary to dynamically
   distribute the traffic to the same destination over multiple equal-
   cost paths, based on the network capacity and even congestion
   information along those paths.


	 </t>
      </abstract>
   </front>
   <seriesInfo name="Internet-Draft" value="draft-xu-idr-fare-03" />
   
</reference>
