<?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-02">
   <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="September" day="1" year="2024" />
      <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, 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-02" />
   
</reference>
