



Benchmarking Methodology Working Group                       F. Calabria
Internet-Draft                                                     Cisco
Updates: 2544 (if approved)                                 C. Pignataro
Intended status: Informational                      Blue Fern Consulting
Expires: 28 August 2026                                            Q. Wu
                                                             G. Fioccola
                                                                  Huawei
                                                        24 February 2026


   Benchmarking Methodology for AI Inference Serving Network Fabrics
            draft-calabria-bmwg-ai-fabric-inference-bench-01

Abstract

   This document defines benchmarking terminology, methodologies, and
   Key Performance Indicators (KPIs) for evaluating Ethernet-based AI
   inference serving network fabrics.  As Large Language Model (LLM)
   inference deployments scale to disaggregated prefill/decode
   architectures spanning hundreds or thousands of accelerators (GPUs/
   XPUs), the interconnect fabric becomes the critical bottleneck
   determining Time to First Token (TTFT), Inter-Token Latency (ITL),
   and aggregate throughput in tokens per second (TPS).  This document
   establishes vendor-independent, reproducible test procedures for
   benchmarking fabric-level performance under realistic AI inference
   workloads.

   Coverage includes RDMA-based KV cache transfer between disaggregated
   prefill and decode workers, Mixture-of-Experts (MoE) expert
   parallelism AllToAll communication, request routing and load
   balancing for inference serving, congestion management under bursty
   inference traffic patterns, and scale/soak testing.  The methodology
   enables direct, equivalent comparison across implementations, NIC
   transport stacks (RoCEv2, UET), and fabric architectures.

   This document is a companion to [TRAINING-BENCH], which addresses
   training workloads.

About This Document

   This note is to be removed before publishing as an RFC.

   The latest revision of this draft can be found at
   https://fcalabri.github.io/bmwg-ai-fabric-inference-bench/draft-
   calabria-bmwg-ai-fabric-inference-bench.html.  Status information for
   this document may be found at https://datatracker.ietf.org/doc/draft-
   calabria-bmwg-ai-fabric-inference-bench/.




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   Discussion of this document takes place on the BMWG Working Group
   mailing list (mailto:bmwg@ietf.org), which is archived at
   https://mailarchive.ietf.org/arch/browse/bmwg/.  Subscribe at
   https://www.ietf.org/mailman/listinfo/bmwg/.

   Source for this draft and an issue tracker can be found at
   https://github.com/fcalabri/bmwg-ai-fabric-inference-bench.

Status of This Memo

   This Internet-Draft is submitted in full conformance with the
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   This Internet-Draft will expire on 28 August 2026.

Copyright Notice

   Copyright (c) 2026 IETF Trust and the persons identified as the
   document authors.  All rights reserved.

   This document is subject to BCP 78 and the IETF Trust's Legal
   Provisions Relating to IETF Documents (https://trustee.ietf.org/
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   Please review these documents carefully, as they describe your rights
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   provided without warranty as described in the Revised BSD License.

Table of Contents

   1.  Introduction  . . . . . . . . . . . . . . . . . . . . . . . .   4
     1.1.  Requirements Language . . . . . . . . . . . . . . . . . .   5
     1.2.  Scope and Applicability . . . . . . . . . . . . . . . . .   5
     1.3.  Relationship to Existing BMWG Work  . . . . . . . . . . .   6
     1.4.  Relationship to Companion Documents . . . . . . . . . . .   6
   2.  Terminology and Definitions . . . . . . . . . . . . . . . . .   6
   3.  Test Topology and Architecture  . . . . . . . . . . . . . . .  10



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     3.1.  Reference Fabric Topologies . . . . . . . . . . . . . . .  10
       3.1.1.  Topology A: 2-Tier Clos (Leaf-Spine)  . . . . . . . .  11
       3.1.2.  Topology B: 3-Tier Clos (Leaf-Spine-Superspine) . . .  11
       3.1.3.  Topology C: Disaggregated Prefill/Decode Placement  .  11
     3.2.  Disaggregated Prefill/Decode Topology . . . . . . . . . .  12
     3.3.  Device Under Test (DUT) Identification  . . . . . . . . .  13
     3.4.  Traffic Generator and Workload Emulator Requirements  . .  14
       3.4.1.  Hardware Traffic Generator (RT) - Minimum
               Requirements  . . . . . . . . . . . . . . . . . . . .  14
       3.4.2.  Software Workload Emulator (WE) - Minimum
               Requirements  . . . . . . . . . . . . . . . . . . . .  15
   4.  KPI Framework and Metrics Taxonomy  . . . . . . . . . . . . .  15
     4.1.  Primary Latency KPIs  . . . . . . . . . . . . . . . . . .  16
     4.2.  Primary Throughput KPIs . . . . . . . . . . . . . . . . .  17
     4.3.  Fabric-Level KPIs . . . . . . . . . . . . . . . . . . . .  18
     4.4.  Fabric Health Indicators  . . . . . . . . . . . . . . . .  19
   5.  Test Category 1: RDMA KV Cache Transfer Benchmarks  . . . . .  19
     5.1.  Point-to-Point KV Cache Transfer Throughput . . . . . . .  20
     5.2.  KV Cache Transfer Latency . . . . . . . . . . . . . . . .  20
     5.3.  Concurrent KV Cache Transfer Scaling  . . . . . . . . . .  21
     5.4.  Multi-Tier Storage Transfer Characterization  . . . . . .  21
   6.  Test Category 2: Prefill/Decode Disaggregation Benchmarks . .  22
     6.1.  End-to-End Disaggregated TTFT . . . . . . . . . . . . . .  22
     6.2.  xPyD Ratio Optimization . . . . . . . . . . . . . . . . .  22
     6.3.  Heterogeneous Parallelism Configuration . . . . . . . . .  23
     6.4.  Prefill Queue Depth Impact on Transfer Latency  . . . . .  23
   7.  Test Category 3: MoE Expert Parallelism Benchmarks  . . . . .  24
     7.1.  AllToAll Dispatch Throughput  . . . . . . . . . . . . . .  24
     7.2.  Routing Mode and Dispatch Mode Comparison.  . . . . . . .  25
     7.3.  Wide Expert Parallelism Scaling . . . . . . . . . . . . .  26
     7.4.  Expert Parallelism and KV Cache Transfer Contention . . .  26
   8.  Test Category 4: Congestion Management Benchmarks . . . . . .  26
     8.1.  ECN Marking Under Inference Incast  . . . . . . . . . . .  26
     8.2.  PFC Behavior Under Bursty KV Cache Transfers  . . . . . .  27
     8.3.  Congestion Control Convergence for Mixed Traffic  . . . .  27
     8.4.  PFC Storm and Deadlock Resilience . . . . . . . . . . . .  27
   9.  Test Category 5: Request Routing and Load Balancing . . . . .  28
     9.1.  KV-Aware Request Routing Efficacy . . . . . . . . . . . .  28
     9.2.  Prefix-Aware Cache Hit Rate . . . . . . . . . . . . . . .  28
     9.3.  ECMP and Dynamic Load Balancing Under Inference
           Traffic . . . . . . . . . . . . . . . . . . . . . . . . .  29
     9.4.  Jain Fairness Index for Decode Worker Utilization . . . .  29
   10. Test Category 6: Latency Benchmarks . . . . . . . . . . . . .  29
     10.1.  TTFT Under Varying Prompt Lengths  . . . . . . . . . . .  29
     10.2.  ITL Characterization and Tail Latency  . . . . . . . . .  30
     10.3.  End-to-End Latency Under Multi-Tenant Load . . . . . . .  31
     10.4.  Latency Sensitivity to Fabric Congestion . . . . . . . .  31
   11. Test Category 7: Throughput Benchmarks  . . . . . . . . . . .  31



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     11.1.  Aggregate Tokens Per Second  . . . . . . . . . . . . . .  31
     11.2.  Batch Size Scaling and Continuous Batching Impact  . . .  32
     11.3.  Goodput Under Preemption and Eviction  . . . . . . . . .  32
   12. Test Category 8: Scale and Autoscaling  . . . . . . . . . . .  32
     12.1.  Fabric Scale Limits for Inference Clusters . . . . . . .  32
     12.2.  Dynamic Autoscaling Response Time  . . . . . . . . . . .  33
     12.3.  Link Failure Convergence Impact on Serving . . . . . . .  33
   13. Test Category 9: Soak and Stability . . . . . . . . . . . . .  34
     13.1.  24-Hour Sustained Inference Load . . . . . . . . . . . .  34
     13.2.  KV Cache Memory Leak Detection . . . . . . . . . . . . .  34
     13.3.  Long-Running Serving Stability . . . . . . . . . . . . .  34
   14. Reporting Format  . . . . . . . . . . . . . . . . . . . . . .  35
   15. Security Considerations . . . . . . . . . . . . . . . . . . .  36
   16. IANA Considerations . . . . . . . . . . . . . . . . . . . . .  36
   17. References  . . . . . . . . . . . . . . . . . . . . . . . . .  36
     17.1.  Normative References . . . . . . . . . . . . . . . . . .  37
     17.2.  Informative References . . . . . . . . . . . . . . . . .  37
   Appendix A.  KPI-to-Test Mapping Summary  . . . . . . . . . . . .  38
   Appendix B.  Inference Serving Framework Capability Categories
           (Informational) . . . . . . . . . . . . . . . . . . . . .  40
   Appendix C.  KV Cache Transfer Frame Format . . . . . . . . . . .  41
   Appendix D.  MoE AllToAll Communication Pattern . . . . . . . . .  43
   Appendix E.  Model Architecture Parameters  . . . . . . . . . . .  44
   Acknowledgements  . . . . . . . . . . . . . . . . . . . . . . . .  45
   Authors' Addresses  . . . . . . . . . . . . . . . . . . . . . . .  45

1.  Introduction

   Large Language Model (LLM) inference serving has emerged as a
   dominant consumer of datacenter network capacity, with fundamentally
   different fabric requirements compared to training workloads.  While
   training workloads are characterized by bulk synchronous collective
   operations (AllReduce, AllGather) with predictable periodicity,
   inference workloads exhibit bursty, latency-sensitive request/
   response patterns with strict Service Level Objectives (SLOs) on per-
   token latency and time-to-first-token.

   The advent of disaggregated serving architectures, where the
   computationally intensive prefill phase (prompt processing) is
   physically separated from the memory-bound decode phase (token
   generation), introduces a new class of fabric-critical data movement:
   KV cache transfer.  A single large prompt processed by a typical
   large-scale model generates multiple gigabytes of KV cache state that
   must be transferred from prefill workers to decode workers within a
   fraction of the target TTFT SLO.






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   As clusters scale with thousands of concurrent requests, this creates
   sustained multi-terabyte-per-second aggregate transfer demands on the
   fabric.  Simultaneously, Mixture-of-Experts (MoE) architectures
   introduce expert parallelism (EP), which distributes expert sub-
   networks across GPUs and requires AllToAll communication for token-
   to-expert routing.  Wide EP configurations (e.g., 96-way EP across 12
   nodes of 8 GPUs each) generate fine-grained, latency-sensitive inter-
   node traffic that contends with KV cache transfers on shared fabric
   links.

   This document defines vendor-independent benchmarking methodologies
   for evaluating how well a network fabric supports these inference-
   specific traffic patterns.  All tests are designed for controlled
   laboratory environments using either hardware traffic generators or
   software workload emulators capable of reproducing inference serving
   traffic profiles.

1.1.  Requirements Language

   The key words "MUST", "MUST NOT", "REQUIRED", "SHALL", "SHALL NOT",
   "SHOULD", "SHOULD NOT", "RECOMMENDED", "NOT RECOMMENDED", "MAY", and
   "OPTIONAL" in this document are to be interpreted as described in
   BCP 14 [RFC2119] [RFC8174] when, and only when, they appear in all
   capitals, as shown here.

1.2.  Scope and Applicability

   The scope covers Layer 2/3 fabric performance (switch forwarding,
   link utilization, congestion management), RDMA transport performance
   (one-sided PUT/GET operations for KV cache transfer, two-sided SEND/
   RECV for expert parallelism dispatch), and the interaction between
   fabric behavior and application-level inference metrics (TTFT, ITL,
   TPS).

   The DUT boundary for all measurements in this document is defined as
   the NIC-to-NIC Ethernet fabric segment — specifically, the path from
   the point of packet transmission by the source NIC Ethernet port to
   the point of packet reception at the destination NIC Ethernet port.

   Intra-node transfer segments (NVLink GPU-to-GPU, PCIe/CXL GPU-to-NIC)
   are explicitly OUT OF SCOPE as primary benchmarked entities.  Where
   intra-node transfer contributes measurably to an end-to-end latency
   measurement (e.g., TTFT decomposition in Section 6.1), implementers
   MUST report intra-node transfer time as a separately labelled
   component so that the fabric contribution can be isolated.  See
   Section 3.2 for DUT boundary diagram.





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   The document does NOT address benchmarking of individual accelerator
   (GPU/XPU) compute performance, model accuracy or quality metrics
   benchmarking of the inference serving software stack in isolation
   from the fabric.

   All methodologies assume controlled laboratory conditions per BMWG
   convention.

1.3.  Relationship to Existing BMWG Work

   This document builds upon the foundational BMWG benchmarking
   framework established by [RFC1242], [RFC2544], [RFC2889], and
   [RFC6349].

   The test structure follows RFC 2544 conventions for trial duration
   (minimum 60 seconds), statistical repetition (minimum 20 trials for
   latency, 50 for burst), and reporting format (graphical and tabular).

   The methodologies extend RFC 2544 Section 26 benchmarks (throughput,
   latency, frame loss rate, back-to-back frames, system recovery,
   reset) to inference-specific scenarios including KV cache transfer,
   expert parallelism dispatch, and disaggregated serving request
   routing.

1.4.  Relationship to Companion Documents

   This document is a companion to [TRAINING-BENCH], which defines
   benchmarking methodologies for AI training network fabrics.  Both
   documents share common terminology (Section 2), test topology
   conventions (Section 3), and reporting formats (Section 14).  Both
   documents use the terminology defined in [TERMINOLOGY], which
   provides the common terminology base for AI fabric benchmarking.

   Where training workloads are dominated by bulk synchronous collective
   communication (AllReduce, AllGather) with high bandwidth utilization
   and periodic synchronization barriers, inference workloads are
   dominated by bursty, latency-sensitive point-to-point transfers (KV
   cache) and fine-grained AllToAll dispatch (MoE expert parallelism).
   Implementers deploying converged fabrics that serve both training and
   inference workloads SHOULD run both test suites.

2.  Terminology and Definitions

   The following terms are used throughout this document.  Terms defined
   in the companion training document are referenced but not redefined
   unless the inference context introduces substantive differences.

   TTFT:  Time to First Token.  The elapsed time from receipt of an



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      inference request by the serving system to emission of the first
      output token.  Includes prompt processing (prefill), KV cache
      generation, optional KV cache transfer (in disaggregated
      architectures), and initial decode step.  Target: < 500 ms for
      interactive serving.

   ITL:  Inter-Token Latency.  The elapsed time between successive
      output tokens during the autoregressive decode phase.  Measured at
      P50, P95, P99, and P99.9 percentiles.  Target: < 50 ms P99 for
      interactive serving.

   TPS:  Tokens Per Second.  Aggregate throughput of the serving system
      measured as the total number of output tokens generated per second
      across all concurrent requests.  Reported separately for input
      (prefill) TPS and output (decode) TPS.

   KV Cache:  Key-Value Cache.  The intermediate attention state (key
      and value projection matrices) computed during the prefill phase
      and reused during each decode step.  Size scales with model
      dimension, number of layers, number of attention heads, sequence
      length, and numerical precision.  For a 70B parameter model at
      FP16 with 4K context: approximately 1.34 GB per request.

   Prefill Phase:  The compute-bound phase of inference in which the
      entire input prompt is processed in parallel to generate the KV
      cache and the first output token.  Characterized by high
      arithmetic intensity (200-400 ops/byte), high GPU utilization
      (90-95%), and large activation tensors.

   Decode Phase:  The memory-bound phase of inference in which output
      tokens are generated autoregressively, one token per forward pass.
      Characterized by low arithmetic intensity (60-80 ops/byte), lower
      GPU utilization (20-40%), and memory-bandwidth-limited KV cache
      reads.

   Disaggregated Serving:  An inference serving architecture in which
      prefill and decode computations are executed on physically
      separate groups of accelerators (workers), connected by a network
      fabric.  The KV cache generated by prefill workers are transferred
      over the fabric to decode workers.

   xPyD Ratio:  The allocation ratio of prefill (x) to decode (y)
      resources in a disaggregated serving cluster.  For example, 3P9D
      indicates 3 prefill nodes and 9 decode nodes.  The optimal ratio
      depends on model size, prompt length distribution, output length
      distribution, and SLO targets.

   EP:  Expert Parallelism.  A parallelism strategy for Mixture-of-



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      Experts (MoE) models in which expert sub-networks are distributed
      across multiple GPUs.  Token routing to the appropriate experts
      requires AllToAll communication.

   Wide EP:  Expert Parallelism spanning many GPUs (e.g., 96-way EP
      across 12 nodes), requiring inter-node AllToAll communication for
      every MoE layer forward pass.

   DP Attention:  Data Parallelism applied to the attention computation,
      where the KV cache is partitioned across data-parallel ranks.
      Each rank holds 1/DP_SIZE of the KV cache, and AllToAll
      communication is used to exchange attention outputs.

   MoE:  Mixture of Experts.  A model architecture that activates only a
      subset of expert sub-networks for each token, enabling larger
      model capacity with sub-linear compute scaling.

   Normal Dispatch:  A communication mode for AllToAll MoE dispatch
      optimized for the prefill phase.  Maximizes throughput for long
      input sequences but generates dynamic (symbolic) shapes
      incompatible with CUDA Graph.

   Low-Latency Dispatch:  A communication mode for AllToAll MoE dispatch
      optimized for the decode phase.  Uses fixed input shapes
      compatible with CUDA Graph, reducing kernel launch overhead at the
      cost of slightly lower peak throughput.

   RDMA:  Remote Direct Memory Access.  A transport mechanism enabling
      direct memory-to-memory data transfer between hosts without CPU
      involvement.  Implementations include InfiniBand Verbs and RoCEv2
      (RDMA over Converged Ethernet v2).

   RoCEv2:  RDMA over Converged Ethernet version 2.  An RDMA transport
      that encapsulates InfiniBand transport over UDP/IP, enabling RDMA
      semantics on standard Ethernet fabrics.

   UET:  Ultra Ethernet Transport.  A transport protocol defined by the
      Ultra Ethernet Consortium (UEC) Specification 1.0, offering
      ordered/unordered reliable delivery, multipath packet spraying,
      and integrated congestion control for AI/HPC workloads.

   KVCXL:  KV Cache Transfer Library.  A library providing standardized
      point-to-point data transfer primitives (register, transfer,
      notify) for inference engines, abstracting underlying transports
      (intra-node interconnect, RDMA, PCIe, and storage interfaces).
      Multiple open-source and vendor implementations exist.

   GIN:  GPU-Initiated Networking.  A communication paradigm where GPU



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      threads directly initiate network operations (RDMA sends, one-
      sided puts) without CPU involvement, reducing latency by
      eliminating CPU-GPU synchronization.

   PagedAttention:  A memory management technique for KV caches that
      stores attention keys and values in fixed-size pages (typically,
      16-64 KB), enabling non-contiguous allocation and reducing memory
      fragmentation.

   Continuous Batching:  A scheduling technique that dynamically adds
      new requests to an active inference batch as decode slots become
      available, improving GPU utilization compared to static batching.

   Prefix Caching:  Reuse of previously computed KV cache segments for
      prompts that share a common prefix (e.g., system prompt), avoiding
      redundant prefill computation.

   DUT:  Device Under Test.  In this document, the DUT is one or more
      network fabric elements (switches, NICs, or the complete fabric)
      whose performance impact on inference serving is being
      characterized.

   SUT:  System Under Test.  The complete inference serving system
      including accelerators, NICs, fabric, and serving software, when
      end-to-end metrics are being measured.

   RT:  Router Tester / Traffic Generator.  Test equipment capable of
      generating and receiving network traffic at specified rates with
      timestamping accuracy sufficient for the measurements defined
      herein.

   S_KV:  S_KV (KV Cache Transfer Size) The total size in bytes of the
      KV cache state generated by a single inference request across all
      transformer layers and all context tokens, computed as:

      S_KV = 2 x L x H_kv x D x C x P_bytes

      Where:

      L = number of transformer layers











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        H_kv     = number of KV attention heads per layer
                     (H_kv <= H_total for GQA/MQA; see note below)

        D        = per-head key/value dimension (head_dim)
                     Typically: head_dim = model_dim / H_total

        C        = context length in tokens (prompt tokens + generated tokens)

        P_bytes  = precision in bytes per element
                     (FP16 / BF16 = 2,  FP8 / INT8 = 1)

        Factor 2 = accounts for both the K (key) and V (value) tensors,
                   each of shape \[H_kv, D\] per layer per token

        Attention variant mapping for  H_kv:

        MHA (Multi-Head Attention):    H_kv = H_total

        GQA (Grouped-Query Attention): H_kv = H_total / GQA_ratio
                                       (e.g., H_total=64, GQA_ratio=8 -> H_kv=8)

        MQA (Multi-Query Attention):   H_kv = 1

      This formula yields the total KV cache bytes for one complete
      inference request.  The per-layer, per-token contribution is:

      s_kv_unit = 2 x H_kv x D x P_bytes (bytes per layer per token)

      and S_KV = s_kv_unit x L x C.

      Assumption: all layers share identical H_kv and D values.  Hybrid
      architectures (e.g., sliding-window + full-attention layers) MUST
      substitute per-layer values and sum across layers.

3.  Test Topology and Architecture

3.1.  Reference Fabric Topologies

   The reference topologies from the companion training document (2-Tier
   Clos, 3-Tier Clos, Rail-Optimized) remain applicable.  Inference
   serving introduces additional topology considerations related to
   disaggregated prefill/decode placement and MoE expert distribution.









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3.1.1.  Topology A: 2-Tier Clos (Leaf-Spine)

   Applicable to inference clusters up to approximately 2,048
   accelerators.  Prefill and decode worker groups SHOULD be placed on
   separate leaf switches (or separate leaf switch groups) to isolate KV
   cache transfer traffic from decode-to-client response traffic.
   Expert parallelism (EP) traffic within a single MoE dispatch group
   SHOULD be confined to a single leaf switch or a minimal number of
   leaf switches to minimize spine-hop latency.

3.1.2.  Topology B: 3-Tier Clos (Leaf-Spine-Superspine)

   Required for inference clusters exceeding 2,048 accelerators or for
   multi-model serving deployments where different model instances
   occupy different fabric pods.  KV cache transfer traffic between
   prefill and decode workers in different pods traverses the superspine
   tier, making superspine bandwidth and latency critical.

3.1.3.  Topology C: Disaggregated Prefill/Decode Placement

   A topology variant specific to inference serving in which prefill
   workers and decode workers are placed in distinct physical locations
   within the fabric, connected by a dedicated KV cache transfer network
   segment.  This topology enables independent scaling of prefill and
   decode resources and allows heterogeneous hardware (e.g., high-
   compute GPUs for prefill, high-memory-bandwidth GPUs for decode).

             +----------------------+
             |   Request Router     |
             |   (KV-Aware LB)      |
             +--------+-------------+
                      |
         +------------+--------------+
         |                           |
   +-----v-------+         +---------v-----+
   | Prefill Pool|         |  Decode Pool  |
   | (xP workers)|         |  (yD workers) |
   | High Compute|         | High Mem BW   |
   | TP=8, DP=N/8|         | TP=8, DP=M/8  |
   +------+------+         +-------+-------+
          |                        |
          |  KV Cache RDMA Transfer|
          |  (One-sided PUT/Signal) |
          +------------------------+

         Figure 1: Disaggregated Prefill/Decode Inference Topology





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3.2.  Disaggregated Prefill/Decode Topology

   The disaggregated topology separates the inference pipeline into
   physically distinct pools connected by the fabric.  The test topology
   MUST include the following components:

   *  *Prefill Worker Pool:* N Prefill nodes, each containing G
      accelerators with high-compute capability.  These workers execute
      the prefill phase and generate KV cache state.  Tensor Parallelism
      (TP) is applied within each node; Data Parallelism (DP) is applied
      across nodes.  Each prefill worker communicates with one or more
      decode workers via RDMA-based KV cache transfer.

   *  *Decode Worker Pool:* M Decode nodes, each containing G
      accelerators with high memory bandwidth.  These workers receive KV
      cache state from prefill workers and execute the autoregressive
      decode phase.  DP Attention may partition the KV cache across DP
      ranks within the decode pool, requiring AllToAll communication
      during decode.

   *  *KV Cache Transfer Network:* The Ethernet fabric segment
      connecting prefill and decode worker pools.  This segment carries
      one-sided RDMA PUT operations (or PUT-with-signal) transferring KV
      cache blocks from prefill GPU memory to decode GPU memory via RDMA
      over Converged Ethernet (RoCEv2) or Ultra Ethernet Transport
      (UET).

      NOTE ON TRANSFER PATH DECOMPOSITION: The end-to-end transfer from
      GPU memory to remote GPU memory traverses three segments: (1) GPU-
      to-NIC: PCIe/CXL (intra-node, out of scope as DUT); (2) NIC-to-
      NIC: Ethernet fabric (THE DUT — in scope); (3) NIC-to-GPU: PCIe/
      CXL at destination (intra-node, out of scope as DUT).
      Benchmarking procedures in Sections 5 and 6 measure fabric-segment
      latency and throughput exclusively.  When end-to-end measurements
      are reported (e.g., TTFT decomposition), the intra-node segments
      MUST be labelled separately.

   The end-to-end transfer from GPU memory to remote GPU memory
   traverses three segments: (1) GPU-to-NIC: PCIe/CXL (intra-node, out
   of scope as DUT); (2) NIC-to-NIC: Ethernet fabric (the DUT, in
   scope); (3) NIC-to-GPU: PCIe/CXL at destination (intra-node, out of
   scope as DUT).  Benchmarking procedures in Sections 5 and 6 measure
   fabric-segment latency and throughput exclusively.  When end-to-end
   measurements are reported (e.g., TTFT decomposition), the intra-node
   segments MUST be labelled separately.






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   ~~~~ GPU Memory --> [PCIe/CXL] --> NIC --> [ETHERNET FABRIC] --> NIC
   --> [PCIe/CXL] --> GPU Memory <---intra-node (out of
   scope)--->|<------DUT (in scope)------->|<---intra-node (out of
   scope)---> ~~~~

   *  *Request Router:* A network-layer or application-layer load
      balancer that assigns incoming inference requests to prefill
      workers and subsequently routes KV cache to the appropriate decode
      workers.  KV-aware routing and prefix-aware caching policies are
      under test.

3.3.  Device Under Test (DUT) Identification

   The following table defines the DUT configurations tested in this
   document:




































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   +========+================+=========================================+
   | DUT ID | Description    | Components Under Test                   |
   +========+================+=========================================+
   | DUT-S  | Single Switch  | Individual leaf or spine switch         |
   |        |                | forwarding inference traffic.           |
   |        |                | Measures per-hop latency, buffer        |
   |        |                | absorption, ECN marking accuracy.       |
   +--------+----------------+-----------------------------------------+
   | DUT-F  | Complete       | End-to-end fabric from prefill NIC      |
   |        | Fabric         | egress to decode NIC ingress.           |
   |        |                | Measures fabric-level KV cache          |
   |        |                | transfer latency, throughput, and       |
   |        |                | congestion behavior.                    |
   +--------+----------------+-----------------------------------------+
   | DUT-N  | NIC Transport  | NIC RDMA transport stack processing KV  |
   |        |                | cache transfer operations.  Measures    |
   |        |                | RDMA verb completion latency, one-      |
   |        |                | sided PUT bandwidth, QP scaling.        |
   +--------+----------------+-----------------------------------------+
   | DUT-PD | Prefill-Decode | The complete data path from prefill     |
   |        | Path           | GPU memory through NIC, fabric, NIC,    |
   |        |                | to decode GPU memory.  Measures end-    |
   |        |                | to-end KV cache transfer including      |
   |        |                | NVLink, PCIe, and fabric segments.      |
   +--------+----------------+-----------------------------------------+
   | SUT-E  | End-to-End     | Complete inference serving system       |
   |        | System         | including inference serving software,   |
   |        |                | RDMA transfer libraries, fabric, and    |
   |        |                | accelerators.  Measures TTFT, ITL, TPS  |
   |        |                | as functions of fabric performance.     |
   +--------+----------------+-----------------------------------------+

                   Table 1: DUT Configuration Definitions

3.4.  Traffic Generator and Workload Emulator Requirements

   Tests in this document require one or both of the following traffic
   generation modes.  The mode used MUST be documented in all test
   reports.

3.4.1.  Hardware Traffic Generator (RT) - Minimum Requirements

   The hardware traffic generator MUST satisfy all of the following:

   *  RDMA traffic generation supporting RoCEv2 and, where tested, UET
      transport; configurable RDMA verb types (one-sided PUT, PUT-with-
      signal, two-sided SEND/RECV).




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   *  Configurable message sizes from 4 KB (minimum KV cache page) to
      256 MB (large KV cache block).

   *  Configurable QP counts from 1 QP to a minimum of 256 QPs per
      source-destination port pair.

3.4.2.  Software Workload Emulator (WE) - Minimum Requirements

   A software workload emulator runs on actual accelerators and
   generates realistic inference workloads.  The WE MUST support all of
   the following:

   *  Configurable prompt length distributions: uniform, Zipf, and
      trace-replay modes.

   *  Configurable output length distributions and configurable request
      arrival rates: Poisson, bursty, and trace-replay.

   *  Disaggregated prefill/decode execution with actual RDMA-based KV
      cache transferring between prefill and decode worker pools.

   *  MoE expert parallelism with actual AllToAll dispatch where MoE-
      specific tests (Section 7) are performed.

   *  Measurement instrumentation providing per-request TTFT and ITL
      with timestamp accuracy <= 1 millisecond.

   When a software workload emulator is used, the complete software
   configuration MUST be documented per Section 3.3, as framework
   version, RDMA library version, and GPU driver version materially
   affect results.

4.  KPI Framework and Metrics Taxonomy

   This section defines the Key Performance Indicators measured across
   all test categories.  KPIs are organized into four tiers: Primary
   Latency KPIs (end-user-facing response time metrics), Primary
   Throughput KPIs (system-level capacity metrics), Fabric-Level KPIs
   (network-specific measurements), and Fabric Health Indicators
   (operational monitoring metrics).

      NOTE: Where numerical reference values appear in the Target column
      of the KPI tables below (including TTFT, ITL, and other latency
      targets), these values are non-normative informational reference
      points reflecting current industry observations for interactive
      inference workloads as of 2025-2026.  They do NOT constitute
      benchmarking acceptance criteria or performance requirements.  Per
      the BMWG charter, the definition of acceptance criteria or



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      performance requirements is explicitly outside the scope of this
      Working Group.  Implementers MAY use these values as contextual
      references when interpreting results; they MUST NOT be used as
      pass/fail thresholds in vendor evaluations.  Deployment-specific
      SLOs will vary by application, model architecture, and operator
      requirements.

4.1.  Primary Latency KPIs

   +=============+======+===============+===============+=============+
   | KPI         | Unit | Definition    | Target        | Measurement |
   |             |      |               | (Interactive) | Point       |
   +=============+======+===============+===============+=============+
   | TTFT        | ms   | Time from     | < 500 ms P99  | SUT-E       |
   |             |      | request       |               | request/    |
   |             |      | arrival to    |               | response    |
   |             |      | first output  |               | boundary    |
   |             |      | token         |               |             |
   |             |      | emission      |               |             |
   +-------------+------+---------------+---------------+-------------+
   | ITL         | ms   | Time between  | < 50 ms P99   | SUT-E token |
   |             |      | successive    |               | emission    |
   |             |      | output tokens |               | timestamps  |
   +-------------+------+---------------+---------------+-------------+
   | TTFT_fabric | ms   | Fabric        | < 300 ms P99  | DUT-PD NIC- |
   |             |      | contribution  |               | to-NIC      |
   |             |      | to TTFT (KV   |               | measurement |
   |             |      | cache         |               |             |
   |             |      | transfer      |               |             |
   |             |      | latency)      |               |             |
   +-------------+------+---------------+---------------+-------------+
   | ITL_fabric  | ms   | Fabric        | < 5 ms P99    | DUT-F EP    |
   |             |      | contribution  |               | dispatch    |
   |             |      | to ITL (EP    |               | round-trip  |
   |             |      | dispatch      |               |             |
   |             |      | latency per   |               |             |
   |             |      | decode step)  |               |             |
   +-------------+------+---------------+---------------+-------------+
   | E2E_latency | ms   | End-to-end    | Varies by     | SUT-E       |
   |             |      | request       | output length | request/    |
   |             |      | latency from  |               | response    |
   |             |      | arrival to    |               | boundary    |
   |             |      | completion of |               |             |
   |             |      | all output    |               |             |
   |             |      | tokens        |               |             |
   +-------------+------+---------------+---------------+-------------+

                      Table 2: Primary Latency KPIs



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4.2.  Primary Throughput KPIs

   +==============+===========+=========================+=============+
   | KPI          | Unit      | Definition              | Measurement |
   |              |           |                         | Point       |
   +==============+===========+=========================+=============+
   | TPS_input    | tokens/s  | Aggregate input         | SUT-E       |
   |              |           | (prefill) tokens        | prefill     |
   |              |           | processed per second    | completion  |
   |              |           | across all workers      | events      |
   +--------------+-----------+-------------------------+-------------+
   | TPS_output   | tokens/s  | Aggregate output        | SUT-E token |
   |              |           | (decode) tokens         | emission    |
   |              |           | generated per second    | events      |
   |              |           | across all workers      |             |
   +--------------+-----------+-------------------------+-------------+
   | TPS_per_GPU  | tokens/s/ | Output tokens per       | SUT-E per-  |
   |              | GPU       | second normalized by    | worker      |
   |              |           | number of decode GPUs   | counters    |
   +--------------+-----------+-------------------------+-------------+
   | Goodput      | GB/s or   | See the Goodput         | SUT-E       |
   |              | tokens/s  | definition in           | successful  |
   |              |           | [TERMINOLOGY]           | completion  |
   |              |           | Reports MUST use        | events      |
   |              |           | Inference_Goodput for   |             |
   |              |           | token-rate measurements |             |
   |              |           | and Fabric_Goodput for  |             |
   |              |           | byte-rate fabric        |             |
   |              |           | measurements            |             |
   +--------------+-----------+-------------------------+-------------+
   | KV_BW        | GB/s      | Aggregate KV cache      | DUT-PD RDMA |
   |              |           | transfer bandwidth      | counters    |
   |              |           | between prefill and     |             |
   |              |           | decode pools            |             |
   +--------------+-----------+-------------------------+-------------+
   | Request_Rate | req/s     | Maximum sustained       | SUT-E       |
   |              |           | request arrival rate    | admission   |
   |              |           | meeting all latency     | control     |
   |              |           | SLOs                    | boundary    |
   +--------------+-----------+-------------------------+-------------+

                     Table 3: Primary Throughput KPIs









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4.3.  Fabric-Level KPIs

   +=======================+========+=========================+=======+
   | KPI                   | Unit   | Definition              | DUT   |
   +=======================+========+=========================+=======+
   | KV_xfer_latency       | us     | One-sided RDMA PUT      | DUT-N |
   |                       |        | completion time for a   |       |
   |                       |        | single KV cache block   |       |
   |                       |        | transfer                |       |
   +-----------------------+--------+-------------------------+-------+
   | KV_xfer_bandwidth     | GB/s   | Sustained               | DUT-N |
   |                       |        | unidirectional KV cache |       |
   |                       |        | transfer throughput per |       |
   |                       |        | NIC port                |       |
   +-----------------------+--------+-------------------------+-------+
   | EP_alltoall_latency   | us     | Round-trip time for a   | DUT-F |
   |                       |        | complete MoE expert     |       |
   |                       |        | parallelism AllToAll    |       |
   |                       |        | dispatch                |       |
   +-----------------------+--------+-------------------------+-------+
   | EP_alltoall_bandwidth | GB/s   | Aggregate AllToAll      | DUT-F |
   |                       |        | bandwidth across all EP |       |
   |                       |        | ranks during dispatch   |       |
   +-----------------------+--------+-------------------------+-------+
   | Fabric_FCT            | us     | Flow completion time    | DUT-F |
   |                       |        | for a KV cache transfer |       |
   |                       |        | flow through the fabric |       |
   +-----------------------+--------+-------------------------+-------+
   | Buffer_utilization    | %      | Peak switch buffer      | DUT-S |
   |                       |        | utilization during KV   |       |
   |                       |        | cache transfer bursts   |       |
   +-----------------------+--------+-------------------------+-------+
   | ECN_marking_rate      | %      | Fraction of packets     | DUT-S |
   |                       |        | marked with ECN-CE      |       |
   |                       |        | during inference        |       |
   |                       |        | traffic                 |       |
   +-----------------------+--------+-------------------------+-------+
   | PFC_frame_count       | frames | Number of PFC PAUSE     | DUT-S |
   |                       |        | frames generated per    |       |
   |                       |        | unit time               |       |
   +-----------------------+--------+-------------------------+-------+
   | Link_utilization      | %      | Average and peak link   | DUT-F |
   |                       |        | utilization on fabric   |       |
   |                       |        | links carrying          |       |
   |                       |        | inference traffic       |       |
   +-----------------------+--------+-------------------------+-------+
   | Packet_drop_rate      | ppm    | Packets dropped per     | DUT-F |
   |                       |        | million due to buffer   |       |



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   |                       |        | overflow or transport   |       |
   |                       |        | error                   |       |
   +-----------------------+--------+-------------------------+-------+

                        Table 4: Fabric-Level KPIs

4.4.  Fabric Health Indicators

       +=================+=============+==========================+
       | Indicator       | Threshold   | Description              |
       +=================+=============+==========================+
       | CPU Utilization | < 30%       | Control plane CPU usage  |
       | (switch)        |             | on switches under        |
       |                 |             | inference traffic load   |
       +-----------------+-------------+--------------------------+
       | Memory Usage    | < 70%       | TCAM, buffer, and        |
       | (switch)        |             | control plane memory     |
       |                 |             | usage                    |
       +-----------------+-------------+--------------------------+
       | FEC Error Rate  | < 1e-12     | Forward Error Correction |
       |                 | post-FEC    | effectiveness on fabric  |
       |                 | BER         | links                    |
       +-----------------+-------------+--------------------------+
       | CRC Error Count | 0           | Layer 2 CRC errors on    |
       |                 |             | any fabric link          |
       +-----------------+-------------+--------------------------+
       | BGP/OSPF        | 0 flaps     | Routing protocol         |
       | Stability       |             | adjacency stability      |
       |                 |             | under inference load     |
       +-----------------+-------------+--------------------------+
       | NIC QP State    | 100% active | All RDMA Queue Pairs in  |
       |                 |             | active state (no error/  |
       |                 |             | reset)                   |
       +-----------------+-------------+--------------------------+
       | GPU-NIC PCIe BW | > 90% of    | PCIe Gen5 x16 bandwidth  |
       |                 | theoretical | utilization between GPU  |
       |                 |             | and NIC                  |
       +-----------------+-------------+--------------------------+

                    Table 5: Fabric Health Indicators

5.  Test Category 1: RDMA KV Cache Transfer Benchmarks

   KV cache transfer between disaggregated prefill and decode workers is
   the defining fabric workload for inference serving.  Unlike training
   collectives (AllReduce, AllGather) which are periodic and
   predictable, KV cache transfers are event-driven (triggered by
   prefill completion) and bursty.



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5.1.  Point-to-Point KV Cache Transfer Throughput

   *Objective:* To determine the maximum sustained KV cache transfer
   throughput between a single prefill worker NIC and a single decode
   worker NIC across the DUT fabric.

   *Procedure:* Configure a single RDMA connection (QP) between the
   prefill and decode endpoints.  Send a sequence of one-sided RDMA PUT
   operations with message sizes corresponding to KV cache block sizes.
   The message size sequence MUST include: 64 KB (single attention
   page), 256 KB, 1 MB, 4 MB, 16 MB, 64 MB, 256 MB (large prompt KV
   cache), and 1 GB.  For each message size, transmit at the maximum
   rate sustainable by the NIC for a minimum of 60 seconds per trial.
   Repeat for 1, 4, 8, 16, 32, 64, and 128 concurrent QPs.  The DUT is
   the fabric path from NIC to NIC.

   *Measurement:* Record throughput (GB/s), CPU utilization on both
   endpoints, GPU memory-to-NIC transfer overhead, and NIC hardware
   offload utilization.  The test MUST be repeated a minimum of 20 times
   per configuration and the average reported.

   *Reporting Format:* Results SHOULD be reported as a multi-line graph
   with message size (log scale) on the X axis and throughput (GB/s) on
   the Y axis.  Separate lines for each QP count.  A reference line
   showing theoretical NIC line rate MUST be included.

5.2.  KV Cache Transfer Latency

   *Objective:* To determine the latency of individual KV cache block
   transfers across the DUT fabric under varying load conditions.

   *Procedure:* Using the same endpoint configuration as Test 5.1,
   measure the completion time of individual RDMA PUT operations.
   Latency is measured from the initiation of the PUT verb on the
   prefill NIC to receipt of the completion signal on the decode NIC
   (for PUT-with-signal) or to polling of the remote completion queue.
   Measure latency under unloaded conditions (single outstanding
   operation) and under loaded conditions (background traffic at 25%,
   50%, 75%, and 90% of fabric capacity).  Message sizes MUST include 64
   KB, 1 MB, 16 MB, and 256 MB.

   *Measurement:* Report latency at P50, P95, P99, and P99.9
   percentiles.  The test MUST be repeated a minimum of 20 trials of at
   least 120 seconds each per configuration.  The difference between P99
   and P50 (tail latency spread) SHOULD be reported as a derived metric.






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   *Reporting Format:* Results SHOULD be reported as a table with
   columns for message size, background load level, and latency at each
   percentile.  A complementary CDF plot of latency distribution for
   selected configurations SHOULD be included.

5.3.  Concurrent KV Cache Transfer Scaling

   *Objective:* To characterize how aggregate KV cache transfer
   performance scales as the number of concurrent prefill-to-decode
   transfer pairs increases.

   *Procedure:* Configure N concurrent prefill-decode endpoint pairs,
   where N ranges from 1 to the maximum supported by the fabric (e.g.,
   1, 2, 4, 8, 16, 32, 64, 128 pairs).  Each pair executes continuous KV
   cache transfers of 16 MB messages (representative of a medium-length
   prompt).  Measure aggregate throughput and per-pair latency as N
   increases.

   *Measurement:* Report aggregate throughput (GB/s), per-pair median
   latency (us), per-pair P99 latency (us), Jain Fairness Index across
   pairs, and maximum fabric link utilization observed.  The test MUST
   be repeated a minimum of 20 times per value of N.

   *Reporting Format:* Results SHOULD be reported as a dual-axis graph
   with N (concurrent pairs) on the X axis, aggregate throughput on the
   left Y axis, and P99 latency on the right Y axis.  The JFI value for
   each N SHOULD be annotated.

5.4.  Multi-Tier Storage Transfer Characterization

   *Objective:* To characterize KV cache transfer performance across the
   memory/storage hierarchy: GPU HBM to GPU HBM (inter-node RDMA), GPU
   HBM to remote CPU DRAM (offload), CPU DRAM to GPU HBM (reload), and
   GPU HBM to NVMe/SSD (persistent cache).

   *Procedure:* For each tier pair, measure unidirectional transfer
   throughput and latency for message sizes of 1 MB, 16 MB, and 256 MB.
   Use zero-copy transfers where supported (GDS for NVMe, GPUDirect RDMA
   for inter-node).

   *Measurement:* Report throughput (GB/s) and latency (P50, P99) for
   each tier pair and message size.  Report the tier throughput ratio
   relative to GPU-to-GPU RDMA as a derived metric.

   *Reporting Format:* Results SHOULD be reported as a table with rows
   for each tier pair and columns for throughput and latency at each
   message size.




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6.  Test Category 2: Prefill/Decode Disaggregation Benchmarks

   Disaggregated prefill/decode serving separates the two phases onto
   distinct hardware pools to enable independent optimization and
   scaling.  This section benchmarks the fabric's ability to support the
   resulting KV cache transfer traffic patterns and their impact on end-
   to-end inference metrics.

6.1.  End-to-End Disaggregated TTFT

   *Objective:* To measure TTFT as a function of prompt length in a
   disaggregated serving configuration, isolating the fabric
   contribution.

   *Procedure:* Configure a disaggregated serving system (SUT-E) with a
   specified xPyD ratio (e.g., 3P9D for a 12-node cluster).  Submit
   inference requests with prompt lengths of 128, 512, 1024, 2048, 4096,
   8192, and 16384 tokens.  For each prompt length, measure the total
   TTFT and decompose it into: T_prefill (prefill compute time),
   T_transfer (KV cache fabric transfer time, measured at DUT-PD), and
   T_decode_init (first decode step time).

   *Measurement:* Report TTFT (ms) and its decomposition at P50, P95,
   and P99 percentiles.  The ratio T_transfer/TTFT (fabric fraction)
   SHOULD be reported as a derived metric.  The test MUST be repeated a
   minimum of 20 trials per prompt length.

   *Reporting Format:* Results SHOULD be reported as a stacked bar chart
   with prompt length on the X axis and TTFT (ms) on the Y axis, with
   bars decomposed into T_prefill, T_transfer, and T_decode_init.  A
   table of numerical values MUST accompany the chart.

6.2.  xPyD Ratio Optimization

   *Objective:* To determine the optimal prefill-to-decode resource
   ratio for a given model, prompt distribution, and latency SLO, as
   limited by fabric transfer capacity.

   *Procedure:* For a fixed total number of nodes N (e.g., 12), iterate
   over xPyD ratios: 1P11D, 2P10D, 3P9D, 4P8D, 6P6D, 8P4D, 10P2D, 11P1D.
   For each ratio, submit a sustained request stream matching a target
   request rate with a specified prompt length distribution (e.g., Zipf
   with alpha=1.0 over [128, 8192] tokens).  Measure TTFT P99, ITL P99,
   TPS_output, and Goodput for each configuration.

   *Measurement:* Report all four metrics for each xPyD ratio and
   request rate.  Identify the Pareto-optimal ratio(s) that maximize
   TPS_output while meeting TTFT P99 < 500 ms and ITL P99 < 50 ms.



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   *Reporting Format:* Results SHOULD be reported as a multi-panel
   figure with one panel per request rate, each showing xPyD ratio on
   the X axis and metrics on dual Y axes (TTFT/ITL on left, TPS on
   right).  The Pareto frontier SHOULD be highlighted.

6.3.  Heterogeneous Parallelism Configuration

   *Objective:* To evaluate the fabric impact of using different
   parallelism strategies on prefill vs. decode pools in a disaggregated
   configuration.

   *Procedure:* Test the following parallelism configurations:

   *  Prefill TP=8, Decode TP=8 (baseline, same parallelism)

   *  Prefill TP=8, Decode TP=4 with DP_Attention=2 (reduced TP, added
      DP)

   *  Prefill TP=4 with DP=2, Decode TP=2 with DP_Attention=4
      (aggressive DP)

   *Measurement:* Report the number of concurrent RDMA flows, aggregate
   bandwidth (GB/s), TTFT (ms), and ITL (ms) at P50 and P99 for each
   configuration.

6.4.  Prefill Queue Depth Impact on Transfer Latency

   *Objective:* To measure how queuing of prefill requests (due to
   compute contention) affects KV cache transfer burstiness and fabric
   congestion.

   *Procedure:* Oversubscribe the prefill pool by submitting requests at
   a rate exceeding prefill capacity.  Measure the resulting KV cache
   transfer burst characteristics: burst size, burst duration, inter-
   burst gap, and peak fabric bandwidth demand.  Vary the
   oversubscription ratio from 1.0x (saturated) to 2.0x in 0.25x
   increments.

   *Measurement:* Report burst size distribution, peak and average
   fabric bandwidth, KV transfer latency P99, and ECN/PFC event counts
   as functions of oversubscription ratio.










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7.  Test Category 3: MoE Expert Parallelism Benchmarks

   Mixture-of-Experts models distribute expert sub-networks across GPUs
   and route tokens to the appropriate experts via AllToAll
   communication.  This section benchmarks the fabric's ability to
   support the resulting fine-grained, latency-sensitive inter-GPU
   traffic patterns.

7.1.  AllToAll Dispatch Throughput

   *Objective:* To determine the maximum AllToAll dispatch throughput
   for MoE expert parallelism across the DUT fabric.

   *Procedure:* Generate a synthetic MoE dispatch workload where each
   GPU sends token embeddings to the experts selected by a top-k routing
   function.  The dispatch payload per GPU per MoE layer is:

   T_dispatch = (B * k * H_model * P_bytes) / N. where B = batch size
   (tokens), k = top-k routing count, H_model = hidden dimension,
   P_bytes = precision bytes (BF16=2), N = EP group size

   *Canonical MoE Test Matrix*

    +========+======================+=========+=========+=============+
    | Config | E (experts)          | k (top- | H_model | T_dispatch  |
    |        |                      | k)      |         | (B=128,     |
    |        |                      |         |         | BF16, N=96) |
    +========+======================+=========+=========+=============+
    | M1     | 8                    | 2       | 4096    | 2.1 MB/GPU  |
    +--------+----------------------+---------+---------+-------------+
    | M2     | 64                   | 4       | 7168    | 29 MB/GPU   |
    +--------+----------------------+---------+---------+-------------+
    | M3     | 256                  | 2       | 7168    | 14 MB/GPU   |
    +--------+----------------------+---------+---------+-------------+
    | M4     | 256                  | 8       | 7168    | 58 MB/GPU   |
    +--------+----------------------+---------+---------+-------------+
    | M5     | (implementer-defined |         |         |             |
    |        | — report all         |         |         |             |
    |        | parameters)          |         |         |             |
    +--------+----------------------+---------+---------+-------------+

                     Table 6: Canonical MoE Test Matrix

   *Measurement:* Report aggregate bandwidth (GB/s), per-dispatch
   latency (us) at P50 and P99, and GPU idle time waiting for dispatch
   completion.  The test MUST be repeated a minimum of 20 times per
   configuration.




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   *Reporting Format:* Results SHOULD be reported as a heatmap with EP
   group size on the Y axis, batch size on the X axis, and throughput
   (GB/s) as the color dimension.  A companion latency table MUST be
   included.  Reports MUST state which config row(s) were used.  M5 MUST
   include E, k, H_model, P_bytes, and N in the results table.

   NOTE: If per-accelerator normalized throughput (BusBW) is reported
   alongside EP_alltoall_bandwidth, the algo_factor for AllToAll is
   (n-1)/n where n is the number of EP ranks.  See the BusBW definition
   in [TERMINOLOGY].

7.2.  Routing Mode and Dispatch Mode Comparison.

   *Objective:* To compare fabric performance across dispatch modes and
   routing policies.  Tests MUST cover Normal Dispatch and Low-Latency
   Dispatch.  Tests SHOULD additionally cover at least one alternative
   routing mode from Table 7.

   *Routing Mode Taxonomy*

    +=============+=========================+=========================+
    | Mode        | Description             | Traffic Impact          |
    +=============+=========================+=========================+
    | Standard    | Each token routed to k. | Fixed, uniform AllToAll |
    | Top-k       | highest-scoring experts | dispatch volume         |
    +-------------+-------------------------+-------------------------+
    | Expert      | Experts select tokens;  | Non-uniform message     |
    | Choice (EC) | ensures load balance    | sizes; tests HOL-       |
    |             |                         | blocking resilience     |
    +-------------+-------------------------+-------------------------+
    | Top-k with  | Overloaded experts drop | Lower peak traffic;     |
    | Token Drop  | excess tokens           | unpredictable under     |
    |             |                         | load                    |
    +-------------+-------------------------+-------------------------+
    | Auxiliary   | Load-balanced top-k via | Near-uniform AllToAll;  |
    | Loss Top-k  | training loss           | lower hot-spot risk     |
    +-------------+-------------------------+-------------------------+

                     Table 7: MoE Routing Mode Taxonomy

   *Measurement:* Measure dispatch latency, fabric bandwidth, and
   routing mode impact on AllToAll traffic distribution and fabric
   congestion per Table 7 . Results from different routing modes MUST be
   reported in separate result tables with the routing mode labelled.







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7.3.  Wide Expert Parallelism Scaling

   *Objective:* To characterize AllToAll dispatch performance as EP
   group size scales beyond a single node (wide EP), requiring inter-
   node fabric communication.

   *Procedure:* Scale the EP group from intra-node only (EP=8) to wide
   EP (EP=16, 32, 48, 64, 96 spanning 2, 4, 6, 8, 12 nodes).  Use a
   fixed batch size of 128 tokens and at least one configuration from
   the canonical MoE test matrix Table 6.  The selected config row MUST
   be identified in the results.

   *Measurement:* Report total dispatch latency (us), inter-node
   bandwidth (GB/s), and latency decomposition (intra-node vs. inter-
   node fraction).  Report the scaling efficiency: (EP=8 latency) /
   (EP=N latency) * (N/8).

7.4.  Expert Parallelism and KV Cache Transfer Contention

   *Objective:* To measure the mutual interference between EP AllToAll
   dispatch traffic and KV cache transfer traffic when both share the
   same fabric links.

   *Procedure:* On a shared fabric, simultaneously execute: (a)
   continuous KV cache transfers at a sustained rate (e.g., 50%, 75% of
   fabric capacity), and (b) periodic EP AllToAll dispatches (one per
   MoE layer forward pass).

   *Measurement:* Report KV_xfer_latency P99 (us) and
   EP_alltoall_latency P99 (us) for the isolated and contended cases.
   Report the contention penalty as the ratio of contended P99 to
   isolated P99 for each traffic class.  Report ECN/PFC event counts
   during contention.

8.  Test Category 4: Congestion Management Benchmarks

   Inference traffic patterns differ from training in their burstiness,
   heterogeneity (mixed KV cache transfers and EP dispatches), and
   latency sensitivity.

8.1.  ECN Marking Under Inference Incast

   *Objective:* To verify that ECN marking thresholds are correctly
   applied when multiple prefill workers simultaneously transfer KV
   cache blocks to a single decode worker (incast pattern).






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   *Procedure:* Configure M prefill workers (M = 2, 4, 8, 16, 32) to
   simultaneously transfer 16 MB KV cache blocks to a single decode
   worker port.  Repeat for ECN marking thresholds of 100 KB, 500 KB, 1
   MB, and 5 MB.  The DUT is the individual leaf switch (DUT-S).

   *Measurement:* Report the ECN marking rate (fraction of marked
   packets), the onset of marking, queue depth at marking onset, and
   aggregate throughput achieved.  Repeat a minimum of 20 times per
   configuration.

8.2.  PFC Behavior Under Bursty KV Cache Transfers

   *Objective:* To characterize PFC PAUSE frame generation and
   propagation under bursty KV cache transfer patterns typical of
   disaggregated serving.

   *Procedure:* Generate KV cache transfer bursts: N_burst concurrent
   transfers (N_burst = 4, 8, 16, 32), each of size 16 MB, arriving
   within a window of T_arrival (100 us, 1 ms, 10 ms).  Vary the PFC
   threshold from 10 KB to 1 MB.

   *Measurement:* Report PFC frame count, total PAUSE duration (us),
   head-of-line blocking delay imposed on other traffic classes (us),
   and KV cache transfer completion time.

8.3.  Congestion Control Convergence for Mixed Traffic

   *Objective:* To measure the convergence time of DCQCN (or UET
   congestion control) when KV cache transfer traffic and EP AllToAll
   dispatch traffic share fabric capacity.

   *Procedure:* Establish a sustained KV cache transfer at 80% of fabric
   capacity.  Introduce EP AllToAll dispatch traffic on the same fabric
   links.  Measure the convergence time to stable rate allocation.
   Repeat with the roles reversed.

   *Measurement:* Report convergence time (ms) to within 5% of steady-
   state rates, steady-state bandwidth allocation between traffic
   classes, packet loss during convergence, and Jain Fairness Index of
   the steady-state allocation.

8.4.  PFC Storm and Deadlock Resilience

   *Objective:* To verify that the fabric does not enter a PFC storm or
   deadlock condition under adversarial inference traffic patterns.






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   *Procedure:* Per the companion training document, generate a PFC
   storm scenario by creating circular buffer dependency across multiple
   switches.  Simultaneously inject KV cache transfer traffic on all
   affected paths.  Monitor for PFC storm propagation, deadlock, and
   recovery time.  The test duration MUST be at least 300 seconds.

   *Measurement:* Report whether PFC storm occurred (yes/no), deadlock
   occurred (yes/no), maximum PAUSE propagation depth (number of hops),
   maximum zero-throughput duration (ms), and recovery time (ms).

9.  Test Category 5: Request Routing and Load Balancing

   Inference serving introduces application-layer routing decisions that
   interact with fabric-layer load balancing (ECMP, flowlet, packet
   spray).

9.1.  KV-Aware Request Routing Efficacy

   *Objective:* To measure the effectiveness of KV-aware request
   routing, where the request router considers decode worker KV cache
   memory occupancy and fabric path congestion when assigning requests.

   *Procedure:* Configure a request router with KV-aware routing
   enabled.  Submit a sustained request stream at rates of 10, 50, 100,
   and 200 req/s.  Compare against round-robin routing (baseline).

   *Measurement:* Report the coefficient of variation (CV) of decode
   worker memory utilization, P99 TTFT, P99 ITL, KV cache eviction rate,
   and Goodput for both KV-aware and round-robin routing.

9.2.  Prefix-Aware Cache Hit Rate

   *Objective:* To measure the fabric bandwidth savings achieved by
   prefix-aware caching, where requests with common prefixes are routed
   to workers that already hold the corresponding KV cache segment.

   *Procedure:* Generate a request workload where P% of requests share a
   common prefix of L tokens (P = 25%, 50%, 75%, 90%; L = 256, 512,
   1024, 2048).  Compare against non-prefix-aware routing.

   *Measurement:* Report cache hit rate (%), fabric bandwidth reduction
   (%), TTFT reduction (ms), and TPS improvement (%) for each (P, L)
   combination.








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9.3.  ECMP and Dynamic Load Balancing Under Inference Traffic

   *Objective:* To evaluate fabric-layer load balancing effectiveness
   under inference traffic patterns characterized by a mix of large KV
   cache flows and small EP dispatch flows.

   *Procedure:* Measure link utilization uniformity under: (a) KV cache
   transfers only (large flows, 16 MB+), (b) EP AllToAll dispatches only
   (small flows, < 1 MB), (c) mixed KV cache and EP traffic.

   *Measurement:* Report JFI, maximum link utilization (%), minimum link
   utilization (%), and the oversubscription ratio for each scenario and
   load balancing algorithm.

9.4.  Jain Fairness Index for Decode Worker Utilization

   *Objective:* To measure how evenly the fabric distributes KV cache
   transfer load across decode workers.

   *Procedure:* With N_D decode workers (N_D = 8, 16, 32, 64), submit a
   sustained request stream and measure per-worker KV cache receive
   rate, GPU utilization, and output TPS.

   *Measurement:* Report JFI for KV cache receive rate, GPU utilization,
   and output TPS.  Report the max/min ratio for each metric.

10.  Test Category 6: Latency Benchmarks

   Inference latency is the primary user-facing quality metric.  This
   section defines benchmarks that isolate the fabric's contribution to
   end-to-end inference latency.

10.1.  TTFT Under Varying Prompt Lengths

   *Objective:* To characterize TTFT as a function of prompt length,
   isolating the fabric-dependent KV cache transfer component.

   *Procedure:* Submit single requests (no concurrent load) with prompt
   lengths of 128, 256, 512, 1024, 2048, 4096, 8192, and 16384 tokens.
   Measure TTFT and decompose into T_prefill, T_transfer, and
   T_decode_init.  As a refernce the following table is provided.










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   +==========+======================+==========+==========+==========+
   | Config   | Model Profile        | S_KV @   | S_KV @   | S_KV @   |
   | ID       |                      | 4K ctx   | 32K ctx  | 128K ctx |
   +==========+======================+==========+==========+==========+
   | CFG-A    | Small: L=32, H_kv=8  | 0.25 GB  | 2.0 GB   | 8.0 GB   |
   |          | (GQA), D=128, BF16   |          |          |          |
   +----------+----------------------+----------+----------+----------+
   | CFG-B    | Mid: L=80, H_kv=8    | 1.3 GB   | 10.5 GB  | 42.0 GB  |
   |          | (GQA), D=128, BF16   |          |          |          |
   |          | (e.g., Llama-3 70B)  |          |          |          |
   +----------+----------------------+----------+----------+----------+
   | CFG-C    | Large MHA: L=96,     | 12.3 GB  | 98.6 GB  | >300 GB  |
   |          | H_kv=64 (MHA),       |          |          |          |
   |          | D=128, BF16          |          |          |          |
   +----------+----------------------+----------+----------+----------+
   | CFG-D    | Mid INT8: L=80,      | 0.67 GB  | 5.4 GB   | 21.5 GB  |
   |          | H_kv=8 (GQA), D=128, |          |          |          |
   |          | INT8 (quantized)     |          |          |          |
   +----------+----------------------+----------+----------+----------+
   | CFG-E    | Implementer-defined: | Computed | Computed | Computed |
   | (custom) | L=*_, H_kv=_*, D=*_, |          |          |          |
   |          | P=_*                 |          |          |          |
   +----------+----------------------+----------+----------+----------+

                 Table 8: Reference Configuration Matrix

   *Measurement:* Report TTFT, T_transfer, and T_transfer/TTFT at P50,
   P95, P99 for each prompt length.  The test MUST be repeated a minimum
   of 100 times per prompt length

   *Reporting Format:* Results MUST specify the configuration ID (CFG-A
   through CFG-E) or provide complete values for L, H_kv, D, C, and
   P_bytes for any test that specifies KV cache message sizes.  Results
   SHOULD be reported as a line graph with prompt length on the X axis
   and TTFT (ms) on the Y axis, with separate lines for P50 and P99.
   The T_transfer component SHOULD be shown as a shaded region.

10.2.  ITL Characterization and Tail Latency

   *Objective:* To characterize inter-token latency distribution and
   identify fabric-induced tail latency during the decode phase.

   *Procedure:* Submit a single long-output request (e.g., 2048 output
   tokens) and record the timestamp of each emitted token.  Repeat
   under: (a) unloaded fabric, (b) loaded fabric (50% of capacity), and
   (c) heavily loaded fabric (90% of capacity plus concurrent EP
   dispatches).




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   *Measurement:* Report ITL at P50, P95, P99, P99.9, and maximum for
   each load condition.  Report the number of tokens exhibiting ITL >
   100 ms (stall events).  The test MUST generate at least 10,000 ITL
   samples per condition.

10.3.  End-to-End Latency Under Multi-Tenant Load

   *Objective:* To measure inference latency when multiple models or
   model instances share the same fabric.

   *Procedure:* Deploy two or more model instances on separate worker
   pools sharing the same fabric.  Submit requests to both instances
   concurrently.

   *Measurement:* Report per-instance TTFT P99, ITL P99, and the
   interference penalty: (multi-tenant metric - single-tenant metric) /
   single-tenant metric * 100%.

10.4.  Latency Sensitivity to Fabric Congestion

   *Objective:* To establish the relationship between fabric congestion
   level and inference latency degradation.

   *Procedure:* Inject controlled background traffic on the fabric at
   levels from 0% to 95% of capacity in 5% increments.  At each level,
   submit inference requests at a fixed rate and measure TTFT and ITL.

   *Measurement:* Report TTFT P99 and ITL P99 as functions of background
   traffic level.  Identify the inflection point at which latency begins
   to degrade significantly.  Report the latency degradation factor at
   50%, 75%, and 90% background load.

11.  Test Category 7: Throughput Benchmarks

   Inference throughput determines the cost-effectiveness of the serving
   deployment.

11.1.  Aggregate Tokens Per Second

   *Objective:* To determine the maximum sustained aggregate TPS
   achievable while meeting latency SLOs.

   *Procedure:* Increase the request arrival rate from 1 req/s to the
   point where either TTFT P99 exceeds 500 ms or ITL P99 exceeds 50 ms.
   At each rate, measure TPS_output, TPS_input, Goodput, and all latency
   KPIs.





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   *Measurement:* Report TPS_output, TPS_input, Goodput, TTFT P99, ITL
   P99, and fabric utilization at the SLO-bounded throughput.  Report
   the fabric utilization at the SLO boundary as a key efficiency
   metric.

11.2.  Batch Size Scaling and Continuous Batching Impact

   *Objective:* To measure the interaction between inference batch size,
   continuous batching, and fabric transfer patterns.

   *Procedure:* Configure the serving system with varying maximum batch
   sizes (1, 4, 8, 16, 32, 64, 128).  For each batch size, measure: (a)
   the number of concurrent KV cache transfers, (b) aggregate fabric
   bandwidth consumed, (c) TPS_output, and (d) TTFT P99.  Enable
   continuous batching and repeat.

   *Measurement:* Report TPS_output, TTFT P99, fabric bandwidth (GB/s),
   and peak concurrent transfers for each batch size, with and without
   continuous batching.

11.3.  Goodput Under Preemption and Eviction

   *Objective:* To measure the Goodput loss when fabric congestion
   forces KV cache eviction or request preemption.

   *Procedure:* Oversubscribe the system beyond the SLO-bounded
   throughput (at 110%, 125%, 150%, and 200% of the rate found in Test
   11.1).  Measure the rate of KV cache evictions, request preemptions,
   and the resulting Goodput reduction.

   *Measurement:* Report Goodput, eviction rate (evictions/s),
   preemption rate (preemptions/s), wasted fabric bandwidth (GB/s), and
   the Goodput/TPS_output ratio (efficiency).

12.  Test Category 8: Scale and Autoscaling

   Inference serving clusters must scale dynamically to match request
   demand.

12.1.  Fabric Scale Limits for Inference Clusters

   *Objective:* To determine the maximum inference cluster size
   supportable by the DUT fabric while meeting performance requirements.








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   *Procedure:* Progressively scale the cluster from a minimal
   configuration (e.g., 2 nodes, 16 GPUs) to the fabric's capacity
   (e.g., 1024 nodes, 8192 GPUs).  At each scale point (following powers
   of two), measure KV cache transfer throughput and latency, EP
   AllToAll dispatch latency, fabric control plane convergence time,
   routing table size, and end-to-end TTFT and TPS.

   *Measurement:* Report all KPIs at each scale point.  Identify the
   scale limit as the point where any KPI degrades by more than 10% from
   the minimal-configuration baseline.

12.2.  Dynamic Autoscaling Response Time

   *Objective:* To measure the time required for the fabric to
   accommodate dynamic scaling of inference worker pools (adding/
   removing prefill or decode workers).

   *Procedure:* Starting from a stable serving state, trigger a scale-up
   event (e.g., adding 4 decode nodes).  Measure: (a) fabric convergence
   time, (b) time from fabric convergence to first KV cache transfer on
   new nodes, (c) time to reach steady-state throughput.  Repeat for
   scale-down events.

   *Measurement:* Report fabric convergence time (ms), first-transfer
   time (ms), and time to steady-state (ms) for scale-up and scale-down
   events.  Report any packet loss or latency spikes during the scaling
   transition.

12.3.  Link Failure Convergence Impact on Serving

   *Objective:* To measure the impact of fabric link failures on
   inference serving performance and the convergence time to restore
   full service.

   *Procedure:* During sustained inference serving at 80% of SLO-bounded
   throughput, fail a single fabric link on: (a) a leaf-spine link
   carrying KV cache traffic, (b) a spine-spine link, (c) a link on the
   decode worker's leaf switch.  Measure traffic disruption and recovery
   time.  Repeat for dual link failures.

   *Measurement:* Report traffic disruption duration (ms), convergence
   time (ms), TTFT degradation during convergence (ms above baseline
   P99), TPS reduction during convergence (%), and time to full recovery
   (ms).  The test MUST be repeated a minimum of 20 times per failure
   scenario.






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13.  Test Category 9: Soak and Stability

   Long-running inference serving deployments must maintain performance
   without degradation over time.

13.1.  24-Hour Sustained Inference Load

   *Objective:* To verify that the fabric maintains performance under
   continuous inference serving load for 24 hours.

   *Procedure:* Configure the SUT-E at 80% of the SLO-bounded throughput
   determined in Test 11.1.  Run a continuous request stream for 24
   hours with a realistic prompt length distribution.  Sample the
   following metrics every 15 minutes: TTFT P99, ITL P99, TPS_output,
   KV_xfer_latency P99, fabric link utilization, switch CPU/memory
   usage, NIC counters (RDMA retransmissions, QP errors), and PFC/ECN
   event counts.

   *Measurement:* Report the trend of all sampled metrics over the
   24-hour period.  There SHOULD be zero NIC QP errors, zero routing
   flaps, and less than 1% variation in TTFT P99 over the test duration.

13.2.  KV Cache Memory Leak Detection

   *Objective:* To detect memory leaks in the KV cache management
   subsystem that may manifest as fabric performance degradation over
   time.

   *Procedure:* Monitor GPU memory, CPU memory, NIC registered memory
   regions, and RDMA memory region counts on all prefill and decode
   workers during the 24-hour soak test.  Record the number of active KV
   cache pages, RDMA memory registrations, and pinned memory at each
   sampling interval.

   *Measurement:* Report the trend of each monitored metric.  Flag any
   monotonic increase as a potential leak.  Report the maximum observed
   memory usage and the usage at the end of the 24-hour period.

13.3.  Long-Running Serving Stability

   *Objective:* To verify that fabric-dependent components remain stable
   under continuous inference serving.

   *Procedure:* During the 24-hour soak test, monitor: NIC QP state
   transitions, switch buffer utilization trend, FEC error rate trend,
   BGP/OSPF adjacency stability, and RDMA retransmission rate.  At the
   12-hour mark, trigger a controlled perturbation (single link flap)
   and verify recovery.



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   *Measurement:* Report the count of any QP state transitions, maximum
   switch buffer utilization, FEC error trend, adjacency flap count, and
   RDMA retransmission count.  Report the recovery time from the 12-hour
   link flap perturbation.

14.  Reporting Format

   All test results MUST be reported following the conventions
   established in RFC 2544 Section 26.  In addition, the following
   inference-specific reporting requirements apply:

   *  *System Configuration Report:* The report MUST include: model name
      and parameter count, parallelism strategy (TP, DP, EP, PP
      configuration for both prefill and decode pools), xPyD ratio,
      inference serving framework name and version, KV cache transfer
      library name and version, accelerator type and count, NIC type and
      firmware version, switch ASIC and software version, fabric
      topology, and link speeds.

   *  *Workload Characterization Report:* The report MUST include:
      prompt length distribution (mean, P50, P99, distribution type),
      output length distribution, request arrival rate and distribution,
      number of concurrent requests, and prefix sharing percentage.

   *  *Results Reporting:* For each test, results MUST include: the
      specific test identifier (e.g., Test 5.1), the DUT/SUT
      configuration tested, the number of trials, all measured KPI
      values with confidence intervals, and any anomalies observed.























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    +======================+============================+=============+
    | Report Element       | Format                     | Required?   |
    +======================+============================+=============+
    | System Configuration | Structured table per above | Yes         |
    |                      |                            | (required)  |
    +----------------------+----------------------------+-------------+
    | Workload Parameters  | Structured table per above | Yes         |
    |                      |                            | (required)  |
    +----------------------+----------------------------+-------------+
    | KPI Summary Table    | Table with all measured    | Yes         |
    |                      | KPIs                       | (required)  |
    +----------------------+----------------------------+-------------+
    | Latency Distribution | CDF or histogram per test  | Recommended |
    | Plots                | section                    |             |
    +----------------------+----------------------------+-------------+
    | Throughput vs. Scale | Line chart per test        | Recommended |
    | Graphs               | section                    |             |
    +----------------------+----------------------------+-------------+
    | Fabric Health        | Table per Section 4.4      | Yes         |
    | Indicators           |                            | (required)  |
    +----------------------+----------------------------+-------------+
    | Raw Data Appendix    | Machine-readable format    | Optional    |
    |                      | (CSV, JSON)                |             |
    +----------------------+----------------------------+-------------+

                   Table 9: Reporting Format Requirements

15.  Security Considerations

   This document defines benchmarking methodologies for controlled
   laboratory testing.  All tests MUST be conducted in isolated test
   environments that are not connected to production networks or the
   public Internet.  The security considerations from [RFC2544] and
   [RFC6815] apply.

   Additionally, implementers SHOULD be aware that RDMA-based KV cache
   transfer provides direct memory access between hosts; all RDMA
   connections in the test environment MUST use authenticated QPs where
   supported.  The test results themselves may reveal performance
   characteristics that could inform denial-of-service attack vectors;
   results SHOULD be treated as sensitive when applicable.

16.  IANA Considerations

   This memo includes no request to IANA.

17.  References




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17.1.  Normative References

   [RFC1242]  Bradner, S., "Benchmarking Terminology for Network
              Interconnection Devices", RFC 1242, DOI 10.17487/RFC1242,
              July 1991, <https://www.rfc-editor.org/rfc/rfc1242>.

   [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/rfc/rfc2119>.

   [RFC2544]  Bradner, S. and J. McQuaid, "Benchmarking Methodology for
              Network Interconnect Devices", RFC 2544,
              DOI 10.17487/RFC2544, March 1999,
              <https://www.rfc-editor.org/rfc/rfc2544>.

   [RFC2889]  Mandeville, R. and J. Perser, "Benchmarking Methodology
              for LAN Switching Devices", RFC 2889,
              DOI 10.17487/RFC2889, August 2000,
              <https://www.rfc-editor.org/rfc/rfc2889>.

   [RFC6349]  Constantine, B., Forget, G., Geib, R., and R. Schrage,
              "Framework for TCP Throughput Testing", RFC 6349,
              DOI 10.17487/RFC6349, August 2011,
              <https://www.rfc-editor.org/rfc/rfc6349>.

   [RFC8174]  Leiba, B., "Ambiguity of Uppercase vs Lowercase in RFC
              2119 Key Words", BCP 14, RFC 8174, DOI 10.17487/RFC8174,
              May 2017, <https://www.rfc-editor.org/rfc/rfc8174>.

   [TERMINOLOGY]
              Calabria, F., Pignataro, C., Wu, Q., and G. Fioccola,
              "Benchmarking Terminology for AI Network Fabrics", Work in
              Progress, Internet-Draft, draft-calabria-bmwg-ai-fabric-
              terminology-01, 21 April 2026,
              <https://datatracker.ietf.org/doc/html/draft-calabria-
              bmwg-ai-fabric-terminology-01>.

17.2.  Informative References

   [DISTSERVE]
              Zhong, Y., "DistServe: Disaggregating Prefill and Decoding
              for Goodput-optimized Large Language Model Serving", OSDI
              2024, 2024.

   [EP-COMM]  "DeepEP: An Efficient Expert-Parallel Communication
              Library", 2025.




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   [K8S-INF]  "llm-d: Kubernetes-Native Distributed LLM Inference",
              2025.

   [LMCACHE]  LMCache Project, "LMCache: Hierarchical KV Cache
              Management for Inference", 2025.

   [RFC6815]  Bradner, S., Dubray, K., McQuaid, J., and A. Morton,
              "Applicability Statement for RFC 2544: Use on Production
              Networks Considered Harmful", RFC 6815,
              DOI 10.17487/RFC6815, November 2012,
              <https://www.rfc-editor.org/rfc/rfc6815>.

   [RFC7432]  Sajassi, A., Ed., Aggarwal, R., Bitar, N., Isaac, A.,
              Uttaro, J., Drake, J., and W. Henderickx, "BGP MPLS-Based
              Ethernet VPN", RFC 7432, DOI 10.17487/RFC7432, February
              2015, <https://www.rfc-editor.org/rfc/rfc7432>.

   [SGLANG]   "SGLang: Efficient Execution of Structured Language Model
              Programs", 2024.

   [TRAINING-BENCH]
              "Benchmarking Methodology for AI Training Network
              Fabrics", Work in Progress, Internet-Draft, draft-
              calabria-bmwg-ai-fabric-training-bench, 2026,
              <https://datatracker.ietf.org/doc/html/draft-calabria-
              bmwg-ai-fabric-training-bench>.

   [UEC-SPEC] Ultra Ethernet Consortium, "UEC Specification 1.0", 2024.

   [VLLM]     Kwon, W., "Efficient Memory Management for Large Language
              Model Serving with PagedAttention", Proceedings of the ACM
              SIGOPS 29th Symposium on Operating Systems Principles,
              2023.

Appendix A.  KPI-to-Test Mapping Summary

   The following table provides a cross-reference from each KPI defined
   in Section 4 to the test(s) in which it is measured.













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     +=======================+=======================+===============+
     | KPI                   | Primary Test(s)       | DUT/SUT       |
     +=======================+=======================+===============+
     | TTFT                  | 6.1, 6.2, 10.1, 10.3  | SUT-E         |
     +-----------------------+-----------------------+---------------+
     | ITL                   | 10.2, 10.3, 10.4      | SUT-E         |
     +-----------------------+-----------------------+---------------+
     | TPS_output            | 6.2, 11.1, 11.2, 11.3 | SUT-E         |
     +-----------------------+-----------------------+---------------+
     | TPS_input             | 11.1                  | SUT-E         |
     +-----------------------+-----------------------+---------------+
     | Goodput               | 11.1, 11.3            | SUT-E         |
     +-----------------------+-----------------------+---------------+
     | KV_xfer_latency       | 5.2, 5.3, 6.1, 6.4    | DUT-N, DUT-PD |
     +-----------------------+-----------------------+---------------+
     | KV_xfer_bandwidth     | 5.1, 5.3, 5.4         | DUT-N, DUT-PD |
     +-----------------------+-----------------------+---------------+
     | EP_alltoall_latency   | 7.1, 7.2, 7.3, 7.4    | DUT-F         |
     +-----------------------+-----------------------+---------------+
     | EP_alltoall_bandwidth | 7.1, 7.3              | DUT-F         |
     +-----------------------+-----------------------+---------------+
     | Fabric_FCT            | 5.2, 5.3              | DUT-F         |
     +-----------------------+-----------------------+---------------+
     | Buffer_utilization    | 8.1, 8.2              | DUT-S         |
     +-----------------------+-----------------------+---------------+
     | ECN_marking_rate      | 8.1, 8.3              | DUT-S         |
     +-----------------------+-----------------------+---------------+
     | PFC_frame_count       | 8.2, 8.4              | DUT-S         |
     +-----------------------+-----------------------+---------------+
     | Link_utilization      | 5.3, 9.3, 12.1        | DUT-F         |
     +-----------------------+-----------------------+---------------+
     | Packet_drop_rate      | 8.1, 8.2, 12.3        | DUT-F         |
     +-----------------------+-----------------------+---------------+
     | Request_Rate          | 11.1                  | SUT-E         |
     +-----------------------+-----------------------+---------------+
     | Prefix Cache Hit Rate | 9.2                   | SUT-E         |
     +-----------------------+-----------------------+---------------+
     | JFI (Decode Worker)   | 9.4                   | SUT-E         |
     +-----------------------+-----------------------+---------------+

                       Table 10: KPI-to-Test Mapping










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Appendix B.  Inference Serving Framework Capability Categories
             (Informational)

   This appendix describes the inference serving framework capability
   categories relevant to AI fabric benchmarking.  This appendix is
   intended to guide documentation of SUT-E configurations and is NOT
   normative.  Implementers using a Software Workload Emulator (SUT-E
   tests) SHOULD document which of the following capabilities their
   serving framework supports.

   +===============+============================+=====================+
   | Capability    | Description                | Relevance to Fabric |
   | Category      |                            | Benchmarking        |
   +===============+============================+=====================+
   | Disaggregated | Physical separation of     | Determines whether  |
   | Prefill/      | prefill and decode         | DUT-PD topology     |
   | Decode (PD)   | execution across different | tests apply         |
   |               | accelerator pools          | (Section 6)         |
   +---------------+----------------------------+---------------------+
   | KV Cache      | Protocol and library used  | Determines RDMA     |
   | Transfer      | for prefill-to-decode KV   | verb types under    |
   | Protocol      | state transfer (one-sided  | test and applicable |
   |               | PUT, two-sided SEND/RECV,  | frame formats       |
   |               | GPU-initiated)             | (Appendix C)        |
   +---------------+----------------------------+---------------------+
   | MoE Expert    | Distribution of MoE expert | Determines whether  |
   | Parallelism   | sub-networks across GPUs   | MoE EP tests apply  |
   | (EP) Support  | and AllToAll dispatch mode | (Section 7)         |
   |               | support                    |                     |
   +---------------+----------------------------+---------------------+
   | Continuous    | Dynamic request admission  | Affects request     |
   | Batching      | to active inference        | arrival rate        |
   |               | batches                    | distributions and   |
   |               |                            | load balancing      |
   |               |                            | tests (Section 9)   |
   +---------------+----------------------------+---------------------+
   | Prefix / KV   | Reuse of KV cache segments | Determines          |
   | Cache Sharing | for requests with common   | applicability of    |
   |               | prefixes                   | prefix cache hit    |
   |               |                            | rate test           |
   |               |                            | (Section 9.2)       |
   +---------------+----------------------------+---------------------+
   | RDMA          | Underlying transport(s)    | Must be documented; |
   | Transport     | supported: RoCEv2, UET, or | affects congestion  |
   | Support       | other                      | management test     |
   |               |                            | interpretation      |
   |               |                            | (Section 8)         |
   +---------------+----------------------------+---------------------+



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   | GPU-Initiated | Ability for GPU threads to | Affects RDMA        |
   | Networking    | directly initiate RDMA     | primitive choice in |
   | (GIN) Support | operations without CPU     | MoE dispatch tests  |
   |               | involvement                | (Section 7)         |
   +---------------+----------------------------+---------------------+
   | Kubernetes /  | Native support for         | Relevant for        |
   | Orchestration | container-based deployment | autoscaling tests   |
   | Integration   | and horizontal scaling     | (Section 12.2)      |
   +---------------+----------------------------+---------------------+
   | Maximum       | Maximum cluster scale at   | Documents           |
   | Reported      | which the framework has    | applicability of    |
   | Scale         | been validated             | fabric scale tests  |
   +---------------+----------------------------+---------------------+

                Table 11: Framework Capability Categories

   NOTE: Implementers MUST document the specific framework name,
   version, and configuration in all test reports.  Results obtained
   with different frameworks are not directly comparable; framework
   identity is a required reporting parameter per Section 14.

Appendix C.  KV Cache Transfer Frame Format

   This appendix defines the reference frame formats for KV cache
   transfer benchmarking over RoCEv2.  The frame format follows the
   standard RoCEv2 encapsulation with one-sided RDMA WRITE (PUT)
   operations.
























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       0                   1                   2                   3
       0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9 0 1
      +-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+
      |             Destination MAC Address (bytes 0-3)               |
      +-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+
      |  Destination MAC (bytes 4-5)  |  Source MAC Address (0-1)     |
      +-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+
      |                 Source MAC Address (bytes 2-5)                |
      +-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+
      |   EtherType = 0x0800/0x86DD   |  DSCP  |ECN|      ...        |
      +-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+
      |                IPv4/IPv6 Header (20 or 40 bytes)              |
      +-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+
      |       Src Port (entropy)      |  Dst Port = 4791 (RoCEv2)    |
      +-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+
      |          UDP Length           |          UDP Checksum         |
      +-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+
      | OpCode=RDMA_WRITE(0x0A) |SE|M| Pad |TVer|       PKey        |
      +-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+
      |         Destination QP Number (24 bits)         |A| Reserved |
      +-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+
      |             Packet Sequence Number (PSN, 24 bits)             |
      +-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+
      |                                                               |
      |                 RETH: Virtual Address (64 bits)               |
      |                                                               |
      +-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+
      |                   Remote Key (R_Key, 32 bits)                 |
      +-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+
      |                     DMA Length (32 bits)                      |
      +-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+
      |                                                               |
      |           KV Cache Payload (variable, up to MTU)              |
      |            (key/value attention state data)                   |
      |                                                               |
      +-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+
      |                        ICRC (4 bytes)                         |
      +-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+

      Figure 2: RoCEv2 KV Cache Transfer Frame (One-Sided RDMA WRITE)

   Notes: The UDP Source Port SHOULD use entropy-based values for ECMP
   load distribution.  The RETH (RDMA Extended Transport Header) carries
   the remote virtual address, remote key, and DMA length for the one-
   sided WRITE operation.  For KV cache transfers, the DMA Length field
   indicates the size of the KV cache block being transferred.  Typical
   MTU for RoCEv2 is 4096 bytes; larger KV cache blocks (e.g., 64 KB
   pages) are segmented into multiple packets by the NIC.  For PUT-with-



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   signal operations, the last packet in the transfer includes an RDMA
   WRITE with Immediate Data (OpCode 0x0B) to signal completion to the
   decode worker.

Appendix D.  MoE AllToAll Communication Pattern

   This appendix describes the AllToAll communication pattern used for
   MoE expert parallelism dispatch and its fabric-level traffic
   characteristics.  In a Mixture-of-Experts model with M total experts
   distributed across N GPUs (each GPU holds M/N experts), a single MoE
   layer forward pass generates an AllToAll communication pattern where
   each GPU sends a variable-size payload to every other GPU.

     +===============+=====================+========================+
     | Parameter     | Normal Dispatch     | Low-Latency Dispatch   |
     |               | (Prefill)           | (Decode)               |
     +===============+=====================+========================+
     | Batch Size    | 128 - 512 tokens    | 1 - 16 tokens          |
     +---------------+---------------------+------------------------+
     | Payload per   | Variable (depends   | Fixed (padded to max)  |
     | GPU pair      | on routing)         |                        |
     +---------------+---------------------+------------------------+
     | Shape         | Dynamic (symbolic)  | Static (CUDA Graph)    |
     | Compatibility |                     |                        |
     +---------------+---------------------+------------------------+
     | QP            | 24 QPs per          | 8 - 16 QPs per         |
     | Parallelism   | connection          | connection             |
     +---------------+---------------------+------------------------+
     | RDMA          | Two-sided SEND/RECV | One-sided PUT (GPU-    |
     | Primitive     | or one-sided PUT    | direct RDMA, GIN)      |
     +---------------+---------------------+------------------------+
     | GPU           | CPU-initiated or    | GIN (device-initiated, |
     | Initiation    | GIN                 | GPU-to-NIC direct)     |
     +---------------+---------------------+------------------------+
     | Typical per-  | 1 - 10 MB aggregate | 10 KB - 1 MB aggregate |
     | dispatch size |                     |                        |
     +---------------+---------------------+------------------------+
     | Dispatch      | Once per MoE layer  | Once per MoE layer per |
     | Frequency     | (prefill)           | token (decode)         |
     +---------------+---------------------+------------------------+
     | Latency       | < 1 ms per dispatch | < 200 us per dispatch  |
     | Target        |                     |                        |
     +---------------+---------------------+------------------------+

          Table 12: MoE Dispatch Traffic Characteristics by Mode






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   For a representative dense MoE configuration (M3: E=256, k=2,
   H_model=7168, EP=96 across 12 nodes, BF16), the inter-node traffic
   per MoE layer dispatch using T_dispatch = (B * k * H_model * 2) / N
   is approximately

   *  Normal Dispatch (prefill, batch=256): 256 * 2 * 7168 * 2 bytes /
      96 GPUs = ~76 KB per GPU pair, ~870 MB aggregate across all pairs.

   *  Low-Latency Dispatch (decode, batch=8): 8 * 2 * 7168 * 2 bytes /
      96 GPUs = ~2.4 KB per GPU pair, ~27 MB aggregate.

   With 61 MoE layers and a decode iteration time target of ~30 ms, the
   decode phase requires 61 AllToAll dispatches within 30 ms, yielding
   ~2,000 dispatches per second per decode step, consuming approximately
   54 GB/s aggregate inter-node bandwidth for the Low-Latency Dispatch
   path.

Appendix E.  Model Architecture Parameters

   This appendix provides a sample calculation for the S_KV formula
   already provided.  It's based on a '70B parameter model at FP16 with
   4K context' model

Parameter                      Symbol   Value   Source
Transformer layers             L        80      Published architecture
KV attention heads (GQA-8)     H_kv     8       H_total=64 / GQA_ratio=8
Per-head dimension             D        128     model_dim(8192) / H_total(64)
Context length                 C        4,096   Given
Precision                      P_bytes  2       FP16 = 2 bytes/element

Step-by-Step Calculation

S_KV = 2  ×  L  ×  H_kv  ×   D   ×    C    × P_bytes

 = 2  ×  80  ×   8   ×  128  ×  4,096  ×    2

Step 1:  2  × 80           =         160   (K + V tensors × layers)

Step 2:  160 × 8           =       1,280   (× KV heads)

Step 3:  1,280 × 128       =     163,840   (× head dimension)

Step 4:  163,840 × 4,096   = 671,088,640   (× context tokens)

Step 5:  671,088,640 × 2   = 1,342,177,280 bytes






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Acknowledgements

   Contributions and review are solicited from the BMWG mailing list
   (bmwg@ietf.org) and the broader AI networking community.  The BMWG
   chairs and Area Director are identified at
   https://datatracker.ietf.org/group/bmwg/about/.

Authors' Addresses

   Fernando Calabria
   Cisco
   Email: fcalabri@cisco.com


   Carlos Pignataro
   Blue Fern Consulting
   Email: carlos@bluefern.consulting


   Qin Wu
   Huawei
   Email: bill.wu@huawei.com


   Giuseppe Fioccola
   Huawei
   Email: giuseppe.fioccola@huawei.com
























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