Hi,
The draft LLM MoE multicast use case proposed at the last IETF side meeting has
been updated based on the feedback received.
Welcome more reviews and comments.
Thank you!
Best regards,
Sandy
Original
From: [email protected] <[email protected]>
To: 段威10036319;Xiaohu Xu <[email protected]>;张征00007940;
Date:
2025年10月20日 15:22
Subject: New Version Notification for draft-zhang-rtgwg-llmmoe-multicast-01.txt
A new version of Internet-Draft draft-zhang-rtgwg-llmmoe-multicast-01.txt has
been successfully submitted by Zheng Zhang and posted to the
IETF repository.
Name: draft-zhang-rtgwg-llmmoe-multicast
Revision: 01
Title: Multicast usage in LLM MoE
Date: 2025-10-20
Group: Individual Submission
Pages: 7
URL:
https://www.ietf.org/archive/id/draft-zhang-rtgwg-llmmoe-multicast-01.txt
Status: https://datatracker.ietf.org/doc/draft-zhang-rtgwg-llmmoe-multicast/
HTML:
https://www.ietf.org/archive/id/draft-zhang-rtgwg-llmmoe-multicast-01.html
HTMLized:
https://datatracker.ietf.org/doc/html/draft-zhang-rtgwg-llmmoe-multicast
Diff:
https://author-tools.ietf.org/iddiff?url2=draft-zhang-rtgwg-llmmoe-multicast-01
Abstract:
Large Language Models (LLMs) have been widely used in recent years.
The Mixture of Experts (MoE) architecture is one of the features of
LLMs that enables efficient inference and cost-effective training.
With the MoE architecture, there are potential multicast use cases
such as tokens dispatching. This draft attempts to analyze these use
cases.
The IETF Secretariat
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