



RTGWG                                                           Z. Zhang
Internet-Draft                                                   W. Duan
Intended status: Informational                           ZTE Corporation
Expires: 15 October 2026                                           X. Xu
                                                                  Y. Liu
                                                            China Mobile
                                                           13 April 2026


                     Multicast use case in LLM MoE
                 draft-zhang-rtgwg-llmmoe-multicast-02

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.

Status of This Memo

   This Internet-Draft is submitted in full conformance with the
   provisions of BCP 78 and BCP 79.

   Internet-Drafts are working documents of the Internet Engineering
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   Internet-Drafts are draft documents valid for a maximum of six months
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   This Internet-Draft will expire on 15 October 2026.

Copyright Notice

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   This document is subject to BCP 78 and the IETF Trust's Legal
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   Please review these documents carefully, as they describe your rights
   and restrictions with respect to this document.  Code Components



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   extracted from this document must include Revised BSD License text as
   described in Section 4.e of the Trust Legal Provisions and are
   provided without warranty as described in the Revised BSD License.

Table of Contents

   1.  Introduction  . . . . . . . . . . . . . . . . . . . . . . . .   2
     1.1.  Requirements Language . . . . . . . . . . . . . . . . . .   3
   2.  Use case - tokens dispatching . . . . . . . . . . . . . . . .   3
     2.1.  Intra-node multicast  . . . . . . . . . . . . . . . . . .   4
     2.2.  Inter-node multicast  . . . . . . . . . . . . . . . . . .   4
     2.3.  Dynamic Requirements  . . . . . . . . . . . . . . . . . .   4
     2.4.  Reliability requirements  . . . . . . . . . . . . . . . .   5
   3.  Multicast technologies analysis . . . . . . . . . . . . . . .   5
   4.  IANA Considerations . . . . . . . . . . . . . . . . . . . . .   6
   5.  Security Considerations . . . . . . . . . . . . . . . . . . .   6
   6.  References  . . . . . . . . . . . . . . . . . . . . . . . . .   6
     6.1.  Normative References  . . . . . . . . . . . . . . . . . .   6
     6.2.  Informative References  . . . . . . . . . . . . . . . . .   7
   Authors' Addresses  . . . . . . . . . . . . . . . . . . . . . . .   7

1.  Introduction

   In recent years, large language models (LLMs) have been widely used.
   Mixture of Experts Model (MoE) is one of the functions of LLM to
   achieve efficient inference and economical training.  Many LLMs
   currently adopt the MoE architecture, such as DeepSeek-V2/V3, Google
   Gemini 1.5 Pro, xAI Grok-1, Mistral 8*22B, Qwen3, etc.  During
   inference, MoE only activates a small number of parameters to
   determine each output token, which significantly reduces the amount
   of computation required by the processor, thereby reducing the
   overall computational requirements.  Therefore, the fewer parameters
   are activated, the less computation the processor needs to perform.
   In the MoE architecture, one token needs to be sent to multiple
   experts, which is a typical multicast use case.

   In most LLMs, two experts are activated during the computation: one
   is a routed expert and the other is a shared expert.  In DeepSeekV3,
   one token activates eight routed experts and one shared expert.

   When all activated experts are located on a node with multiple GPUs
   installed, only intra-node communication is required.  When activated
   experts are located on different nodes, inter-node communication is
   required.  Due to the bandwidth difference between intra-node and
   inter-node scenarios, communication across leaf switches and even
   spine switches is inevitable.

   This draft analyzes the multicast use case of LLM in data centers.



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1.1.  Requirements Language

   The key words "MUST", "MUST NOT", "REQUIRED", "SHALL", "SHALL NOT",
   "SHOULD", "SHOULD NOT", "RECOMMENDED", "MAY", and "OPTIONAL" in this
   document are to be interpreted as described in [RFC2119].

2.  Use case - tokens dispatching

               +-----------+               +-----------+
               |  Spine 1  |               |  Spine x  |
               +-+------+--+               +-+------+--+
                 |      |                    |      |
                 |      |                    |      +---------+
        +--------+      +--------------------|---------+      |
        |      +-----------------------------+         |      |
        |      |                                       |      |
      +-+------+-+     +----------+                  +-+------+-+
      |  Leaf 1  |     |  Leaf 2  |    ......        |  Leaf n  |
      +-+--+---+-+     +----------+                  +--+--+--+-+
        |  |   |                                        |  |  |
        |  |   +-------------------------------------+  |  |  |
        |  +----------------+                        |  |  |  |
        |                   |              +---------|--|--+  |
      +-+            +------|--------------|---------|--+     +-----+
      |              |      |              |         |              |
    +-+--+----+---+--+-+  +-+--+----+---+--+-+     +-+--+----+---+--+-+
    |GPU1|GPU2|...|GPU8|  |GPU1|GPU2|...|GPU8| ... |GPU1|GPU2|...|GPU8|
    +----+----+---+----+  +----+----+---+----+     +----+----+---+----+
           node 1                 node 2                   node m

                                  Figure 1

   During the pre-filling and decoding phases, tokens need to be sent to
   all selected experts, including routed experts and shared experts.
   The tokens dispatching can be intra-node or inter-node.  Different
   LLMs use different numbers of experts.  For example, Mixtral uses 8
   experts and activates 2 experts at a time; LlaMa 4 uses 16 experts
   (Scout) or 128 experts (Maverick), and activates 2 experts at a time;
   DeepSeekV3 uses 256 experts and activates 9 experts at a time.  The
   more routed experts there are, the more distribution there is between
   nodes.  In order to balance the experts, it is difficult to limit the
   number of experts to one node even if only two experts (one routed
   expert and one shared expert) are used.

   The tokens dispatching can be optimized.  For example, in DeepSeekV3,
   LLM first selects the node group and then selects the expert from the
   node.  By implementing the node restricted routing function, a
   maximum of four nodes are selected to reduce the inter-node



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   consumption of tokens dispatching.  In addition, in order to maximize
   the usage of the high intra-node bandwidth, after the switch or GPU
   in the node receives the tokens, the switch or GPU needs to
   distribute the tokens to the experts in the same node.  This
   optimization aims to reduce the inter-node distribution, but it
   cannot avoid multicast between nodes.

   It is worth noting that even during an inference or training process,
   the experts selected by the token are not fixed.  Each token may be
   sent to a different combination of experts.  For example, in
   DeepSeekv3, each token may be sent to 9 different experts.

   Therefore, the use of multicast may be intra-node or inter-node.  The
   existing multicast implementation methods are different in intra-node
   and inter-node scenarios, and multicast management is more difficult.

2.1.  Intra-node multicast

   When tokens need to be sent to multiple GPUs in the same node, the
   GPU or the switch connected to the GPU may send the tokens in a
   multicast manner.  This requires the switch or GPU to support the
   multicast function.  This function can reduce the computational
   burden of the source GPU and reduce the bandwidth consumption between
   nodes.

2.2.  Inter-node multicast

   When tokens need to be sent to multiple nodes, Leaf switches and even
   Spine switches need to forward tokens.  Due to the limitation of
   inter-node bandwidth, the more packets there are, the greater the
   risk of congestion.  Using multicast technology can reduce the burden
   on the source GPU and reduce the risk of congestion.

2.3.  Dynamic Requirements

   Due to the random nature of multicast destination selection, for
   example, in the token dispatching process mentioned above, each token
   may select a different expert combination.  The selection process is
   very short, leaving no time for multicast technologies like PIM to
   establish a multicast tree.  Therefore, multicast technology that can
   meet dynamic needs is needed.










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2.4.  Reliability requirements

   The transmission of all types of data (including tokens) used for LLM
   calculations requires extremely high reliability.  This means that
   packet loss, long delays or jitters, and retransmissions during data
   transmission can impact LLM calculations.  Reliability is a paramount
   requirement when applying multicast technology to data transmission.

   If reliability is insufficient, even if most data reaches its
   destination quickly, if even one destination fails to receive the
   data in time due to packet loss, long latency, or excessive jitter,
   the LLM calculation may need to be restarted, significantly reducing
   computational efficiency.

   So compared to unicast, multicast reliability technology is more
   complex.  Especially in LLM applications, it is necessary to avoid
   packet loss, long latency, excessive jitter, and retransmissions
   caused by individual multicast branches to minimize the impact on LLM
   calculations.

3.  Multicast technologies analysis

   Protocol Independent Multicast - Sparse Mode (PIM-SM) [RFC7761] is a
   traditional multicast technology.  It relies on PIM signaling to
   build the multicast tree.  When the receivers change, the multicast
   tree may need to be rebuilt.  When PIM is used for intra-node or
   inter-node multicast, the stability of the multicast tree is more
   important.  It may not be applicable when the expert combination is
   flexible.  Even in the intra-node scenario, the number of potential
   multicast trees may be large despite the limited number of GPUs in a
   single node.

   BIER (Bit-Indexed Explicit Replication) [RFC8279] is an architecture
   that provides optimal multicast forwarding through a "multicast
   domain", without requiring intermediate routers to maintain any per-
   flow state or to engage in an explicit tree-building protocol.  BIER
   is more flexible than PIM.  Experts can be numbered and can act as
   ingress or egress BFRs in BIER.  BIER header encapsulation can be a
   function defined in [RFC8296], [I-D.ietf-bier-bierin6], or
   [I-D.zzhang-bier-unmasked-bier].  By using the BIER function, Leaf
   and Spine switches, and even GPUs or switches connected to GPUs, can
   pre-build expert-based forwarding tables. tokens can be sent to any
   selected expert.

   Other multicast methods, such as PIM DM (dense mode) and ingress
   replication, may consume more bandwidth and may not be a good choice
   for multicast scenarios such as LLM tokens dispatching.




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   Considering dynamic requirements like token dispatching, technologies
   like PIM, which require the establishment of a multicast tree, are
   inadequate.  BIER, on the other hand, allows the source GPU (similar
   to BFIR in BIER) to directly specify the destination expert group
   (similar to BFERs in BIER) and encapsulate it into the message,
   eliminating the time for multicast tree establishment.  Therefore,
   BIER is the most suitable multicast technology.  Of course, prior to
   this, control-plane negotiation between the source GPU and the
   experts regarding data transmission is required.

   While the network layer can provide multicast capabilities for
   multicast scenarios, the multicast approach needs to work in
   conjunction with the LLM software.  It may work in conjunction with
   the implementation of collective communication and NIC (network
   interface card).

4.  IANA Considerations

   There are no IANA consideration introduced by this draft.

5.  Security Considerations

   There are no security issues introduced by this draft.

6.  References

6.1.  Normative References

   [RFC2119]  Bradner, S., "Key words for use in RFCs to Indicate
              Requirement Levels", BCP 14, RFC 2119,
              DOI 10.17487/RFC2119, March 1997,
              <https://www.rfc-editor.org/info/rfc2119>.

   [RFC7761]  Fenner, B., Handley, M., Holbrook, H., Kouvelas, I.,
              Parekh, R., Zhang, Z., and L. Zheng, "Protocol Independent
              Multicast - Sparse Mode (PIM-SM): Protocol Specification
              (Revised)", STD 83, RFC 7761, DOI 10.17487/RFC7761, March
              2016, <https://www.rfc-editor.org/info/rfc7761>.

   [RFC8279]  Wijnands, IJ., Ed., Rosen, E., Ed., Dolganow, A.,
              Przygienda, T., and S. Aldrin, "Multicast Using Bit Index
              Explicit Replication (BIER)", RFC 8279,
              DOI 10.17487/RFC8279, November 2017,
              <https://www.rfc-editor.org/info/rfc8279>.







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   [RFC8296]  Wijnands, IJ., Ed., Rosen, E., Ed., Dolganow, A.,
              Tantsura, J., Aldrin, S., and I. Meilik, "Encapsulation
              for Bit Index Explicit Replication (BIER) in MPLS and Non-
              MPLS Networks", RFC 8296, DOI 10.17487/RFC8296, January
              2018, <https://www.rfc-editor.org/info/rfc8296>.

6.2.  Informative References

   [I-D.ietf-bier-bierin6]
              Zhang, Z., Zhang, Z. J., Wijnands, I., Mishra, M. P.,
              Bidgoli, H., and G. S. Mishra, "Supporting BIER in IPv6
              Networks (BIERin6)", Work in Progress, Internet-Draft,
              draft-ietf-bier-bierin6-13, 23 February 2026,
              <https://datatracker.ietf.org/doc/html/draft-ietf-bier-
              bierin6-13>.

   [I-D.zzhang-bier-unmasked-bier]
              Przygienda, T., Zhang, Z. J., Bigdoli, H., and I.
              Wijnands, "Unmasked BIER Mode", Work in Progress,
              Internet-Draft, draft-zzhang-bier-unmasked-bier-01, 23
              February 2025, <https://datatracker.ietf.org/doc/html/
              draft-zzhang-bier-unmasked-bier-01>.

Authors' Addresses

   Zheng Zhang
   ZTE Corporation
   China
   Email: zhang.zheng@zte.com.cn


   Wei Duan
   ZTE Corporation
   China
   Email: duan.wei1@zte.com.cn


   Xiaohu Xu
   China Mobile
   China
   Email: xuxiaohu_ietf@hotmail.com


   Yisong Liu
   China Mobile
   China
   Email: liuyisong.ietf@gmail.com




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