]> Cisco

United States fcalabri@cisco.com

Blue Fern Consulting

United States carlos@bluefern.consulting

Huawei

China bill.wu@huawei.com

Huawei

Italy giuseppe.fioccola@huawei.com

Apple

United States sowjredd@gmail.com

Operations and Management BMWG benchmarking AI inference LLM network fabric RDMA KV cache MoE disaggregated serving 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 determines 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 comparison across NIC transport stacks (RoCEv2 and UET) and fabric architectures. This document is a companion to the AI training fabric benchmarking methodology, which addresses training workloads. About This Document The latest revision of this draft can be found at . Status information for this document may be found at . Discussion of this document takes place on the BMWG Working Group mailing list (), which is archived at . Subscribe at . Source for this draft and an issue tracker can be found at .

Introduction Large Language Model (LLM) inference now consumes datacenter network capacity at a scale comparable to training, but with different fabric requirements. Training workloads generate bulk synchronous collective operations – AllReduce, AllGather – at predictable intervals. Inference workloads produce bursty, latency-sensitive request/response patterns with strict Service Level Objectives (SLOs) on per-token latency and time-to-first-token. Disaggregated serving architectures physically separate the prefill phase (prompt processing) from the decode phase (token generation). This separation creates a new category 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. 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.

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 when, and only when, they appear in all capitals, as shown here.

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 (proprietary accelerator interconnects GPU-to-GPU, and PCIe / Compute Express Link (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 ), implementers report intra-node transfer time as a separately labelled component so that the fabric contribution can be isolated. See for DUT boundary diagram. 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.

Relationship to Existing BMWG Work This document builds upon the foundational BMWG benchmarking framework established by , , , and . The test structure follows RFC 2544 conventions for trial duration (minimum 60 seconds), statistical repetition (minimum 20 trials per configuration), 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.

Relationship to Companion Documents This document is a companion to , which defines benchmarking methodologies for AI training network fabrics. Both documents share common terminology, test topology conventions, and reporting formats (). Both documents use the terminology defined in , which provides the common terminology base for AI fabric benchmarking. Training workloads generate bulk synchronous collective communication (AllReduce, AllGather) with high bandwidth utilization and periodic synchronization barriers; inference workloads generate bursty, latency-sensitive point-to-point transfers (KV cache) and fine-grained AllToAll dispatch for MoE expert parallelism. Implementers deploying converged fabrics that serve both training and inference workloads should run both test suites.

Terminology Terminology used in this document is defined in . Readers should consult that document before applying the methodology defined here. Where a term overlaps with or , the terminology document provides AI fabric context extensions; the foundational definitions in those RFCs remain authoritative for general network benchmarking. The following terms are bench-specific extensions used only in this document and are not redefined in : Bench-Specific Terminology Extensions

Term	Definition
TTFT_fabric	The fabric-segment contribution to Time to First Token (TTFT), measured at the DUT-PD boundary. Comprises the KV cache transfer time over the Ethernet fabric only; excludes intra-node (PCIe/CXL/accelerator-interconnect) contributions. Reported alongside SUT-E TTFT to enable fabric/non-fabric decomposition.
ITL_fabric	The fabric-segment contribution to Inter-Token Latency (ITL), measured at the DUT-F boundary. Comprises the per-decode-step EP dispatch round-trip over the fabric; excludes intra-node and compute contributions.
DUT-S	Single-switch DUT configuration; see .
DUT-F	Complete-fabric DUT configuration; see .
DUT-N	NIC-transport DUT configuration; see .
DUT-PD	Prefill-Decode-path DUT configuration; see .
SUT-E	End-to-end inference SUT configuration; see .

The scope of the DUT for the tests defined in this document is the Ethernet fabric segment connecting prefill and decode workers (and, where applicable, expert-parallel groups), consistent with the Fabric DUT Boundary defined in . Worked examples of the S_KV formula and KV cache size computation for representative model architectures are provided in the appendix.

Acronyms Acronyms used in this document are expanded in the Acronyms appendix of . Acronyms unique to the methodology defined herein are expanded on first use in the body of this document.

Test Topology and Architecture

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.

Topology A: 2-Tier Clos (Leaf-Spine) Applicable to inference clusters up to approximately 2,048 accelerators. Prefill and decode worker groups are 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 is confined to a single leaf switch or a minimal number of leaf switches to minimize spine-hop latency.

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, so superspine bandwidth and latency directly affect KV cache transfer performance.

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).

Disaggregated Prefill/Decode Inference Topology

Disaggregated Prefill/Decode Topology The disaggregated topology separates the inference pipeline into physically distinct pools connected by the fabric. The test topology includes 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) . 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 and measure fabric-segment latency and throughput exclusively. When end-to-end measurements are reported (e.g., TTFT decomposition), the intra-node segments are labelled separately. [PCIe/CXL] --> NIC intra-node (out of scope) NIC --> [ETHERNET FABRIC] --> NIC DUT (in scope) NIC --> [PCIe/CXL] --> GPU Memory 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.

Device Under Test (DUT) Identification The following table defines the DUT configurations tested in this document: DUT Configuration Definitions

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 Fabric	End-to-end fabric from prefill NIC 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 Path	The complete data path from prefill GPU memory through NIC, fabric, NIC, to decode GPU memory. Measures end-to-end KV cache transfer including proprietary accelerator-interconnect, PCIe/CXL, and fabric segments.
SUT-E	End-to-End System	Complete inference serving system including inference serving software, RDMA transfer libraries, fabric, and accelerators. Measures TTFT, ITL, TPS as functions of fabric performance.

Traffic Generator and Workload Emulator Requirements Tests in this document require one or both of the following traffic generation modes. The mode used is documented in all test reports.

Hardware Traffic Generator (RT) - Minimum Requirements The hardware traffic generator satisfies 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).
• 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.

Software Workload Emulator (WE) - Minimum Requirements A software workload emulator runs on actual accelerators and generates realistic inference workloads. The WE supports 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 () 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 is documented per , as framework version, RDMA library version, and GPU driver version materially affect results.

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, Primary Throughput KPIs, Fabric-Level KPIs, and Fabric Health Indicators. Each tier is defined in the subsections below.

• NOTE: Per BMWG charter, the definition of acceptance criteria or performance requirements is explicitly outside the scope of this Working Group. The KPI tables in this section define what is measured; they do not set pass/fail criteria. Indicative non-normative reference values reflecting current industry observations are provided in ; those values MUST NOT be used as pass/fail criteria in vendor evaluations.

Primary Latency KPIs Primary Latency KPIs

KPI	Unit	Definition	Measurement Point
TTFT	ms	Time from request arrival to first output token emission	SUT-E request/response boundary
ITL	ms	Time between successive output tokens	SUT-E token emission timestamps
TTFT_fabric	ms	Fabric contribution to TTFT (KV cache transfer latency)	DUT-PD NIC-to-NIC measurement
ITL_fabric	ms	Fabric contribution to ITL (EP dispatch latency per decode step)	DUT-F EP dispatch round-trip
E2E_latency	ms	End-to-end request latency from arrival to completion of all output tokens	SUT-E request/response boundary

Primary Throughput KPIs Primary Throughput KPIs

KPI	Unit	Definition	Measurement Point
TPS_input	tokens/s	Aggregate input (prefill) tokens processed per second across all workers	SUT-E prefill completion events
TPS_output	tokens/s	Aggregate output (decode) tokens generated per second across all workers	SUT-E token emission events
TPS_per_GPU	tokens/s/GPU	Output tokens per second normalized by number of decode GPUs	SUT-E per-worker counters
Goodput	GB/s or tokens/s	See the Goodput definition in . Reports use Inference_Goodput for token-rate measurements and Fabric_Goodput for byte-rate fabric measurements	SUT-E successful completion events
Request_Rate	req/s	Maximum sustained request arrival rate meeting all latency SLOs	SUT-E admission control boundary
Prefix_cache_hit_rate	%	Fraction of requests whose shared prefix KV cache segment is already resident on the assigned worker, avoiding a fabric transfer	SUT-E request router counters
JFI_decode	dimensionless (0-1)	Jain Fairness Index of per-decode-worker load (KV cache receive rate, GPU utilization, output TPS)	SUT-E per-worker counters

Fabric-Level KPIs Fabric-Level KPIs

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

Fabric Health Indicators Fabric Health Indicators

Indicator	Threshold	Description
CPU Utilization (switch)	< 30%	Control plane CPU usage on switches under inference traffic load
Memory Usage (switch)	< 70%	TCAM, buffer, and control plane memory usage
FEC Error Rate	< 1e-12 post-FEC Bit Error Rate (BER)	Forward Error Correction effectiveness on fabric links
CRC Error Count	0	Layer 2 CRC errors on any fabric link
BGP/OSPF Stability	0 flaps	Routing protocol 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 theoretical	PCIe Gen5 x16 bandwidth utilization between GPU and NIC

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.

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 includes: 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 is repeated a minimum of 20 times per configuration and the average reported. Reporting Format: Results are 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 is included.

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 include 64 KB, 1 MB, 16 MB, and 256 MB. Measurement: Report latency at P50, P95, P99, and P99.9 percentiles. The test is repeated a minimum of 20 trials of at least 120 seconds each per configuration. The difference between P99 and P50 (tail latency spread) is reported as a derived metric. Reporting Format: Results are 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 is included.

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 is repeated a minimum of 20 times per value of N. Reporting Format: Results are 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 is annotated.

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 (GPU-direct storage paths for NVMe and GPU-direct RDMA for inter-node, where the implementation provides them). 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 are reported as a table with rows for each tier pair and columns for throughput and latency at each message size.

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.

End-to-End Disaggregated TTFT Objective: To measure TTFT as a function of prompt length in a disaggregated serving configuration, isolating the fabric contribution. This test characterizes the disaggregated-serving KV cache transfer path under normal serving conditions on a configured xPyD cluster; the single-request unloaded TTFT baseline is established separately in . 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) is reported as a derived metric. The test is repeated a minimum of 20 trials per prompt length. Reporting Format: Results are 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 accompanies the chart.

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 Inference_Goodput for each configuration. Measurement: Report all four metrics for each xPyD ratio and request rate. Identify the Pareto-optimal ratio(s) across the TPS_output, TTFT, and ITL trade-off, that is, the ratios for which no other ratio improves one metric without worsening another. For reference, an illustrative interactive-serving objective is TTFT P99 < 500 ms and ITL P99 < 50 ms; these values are provided for context only and are not benchmark requirements or acceptance criteria. Reporting Format: Results are 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 is highlighted.

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.

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.

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.

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 (e.g., BFloat16 (BF16) = 2), N = EP group size Canonical MoE Test Matrix Canonical MoE Test Matrix

Config	E (experts)	k (top-k)	H_model	T_dispatch per GPU pair (B=128, BF16, N=96)
M1	8	2	4096	21.8 KB
M2	64	4	7168	76.5 KB
M3	256	2	7168	38.2 KB
M4	256	8	7168	153 KB
M5	(implementer-defined — report all parameters)

NOTE: T_dispatch values are the per-GPU-pair payload computed from the T_dispatch formula above (e.g., M1: 128 x 2 x 4096 x 2 bytes / 96 = 21,845 bytes = 21.8 KB). The aggregate fabric load per dispatch is T_dispatch multiplied by the number of communicating GPU pairs; see the MoE AllToAll appendix for a worked example. Measurement: Report aggregate bandwidth (GB/s), per-dispatch latency (us) at P50 and P99, and GPU idle time waiting for dispatch completion. The test is repeated a minimum of 20 times per configuration. Reporting Format: Results are 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 is included. Reports state which config row(s) were used. For M5, the values of E, k, H_model, P_bytes, and N are included in the results table. NOTE: When per-accelerator normalized throughput (BusBW) is reported alongside EP_alltoall_bandwidth, BusBW is computed per the BusBW definition in ; algo_factor is fixed per collective type and does not depend on the algorithm the library selects at runtime. The runtime algorithm in use is verified via library tracing and documented as part of the test conditions.

Routing Mode and Dispatch Mode Comparison Objective: To compare fabric performance across dispatch modes and routing policies. Tests cover Normal Dispatch and Low-Latency Dispatch. Tests should additionally cover at least one alternative routing mode from . Routing Mode Taxonomy MoE Routing Mode Taxonomy

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

Procedure: Execute the AllToAll dispatch procedure of the preceding AllToAll Dispatch Throughput test in both Normal Dispatch and Low-Latency Dispatch modes. Repeat the sequence for each selected routing mode from , holding message sizes, EP group size, and iteration count constant across dispatch and routing modes so that results are directly comparable. Measurement: Measure dispatch latency, fabric bandwidth, and routing mode impact on AllToAll traffic distribution and fabric congestion per . Results from different routing modes are reported in separate result tables with the routing mode labelled.

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 . The selected config row is 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).

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.

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.

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). 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.

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.

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.

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. Procedure: Per the PFC Storm and Deadlock Resilience test of , 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 is 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).

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).

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 Inference_Goodput for both KV-aware and round-robin routing.

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.

ECMP and Dynamic Load Balancing Under Inference Traffic Objective: To evaluate fabric-layer load balancing effectiveness under inference traffic patterns that mix large KV cache flows with 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.

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.

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.

TTFT Under Varying Prompt Lengths Objective: To characterize TTFT as a function of prompt length, isolating the fabric-dependent KV cache transfer component. This test establishes the single-request, unloaded-fabric baseline (no concurrent load), complementing , which measures the same decomposition under normal serving conditions on a disaggregated xPyD configuration. 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 reference the following table is provided. Reference Configuration Matrix

Config ID	Model Profile	S_KV @ 4K ctx	S_KV @ 32K ctx	S_KV @ 128K ctx
CFG-A	Small: L=32, H_kv=8 (Grouped-Query Attention, GQA), D=128, BF16	0.54 GB	4.3 GB	17.2 GB
CFG-B	Mid: L=80, H_kv=8 (GQA), D=128, BF16 (~70B-parameter dense class)	1.3 GB	10.7 GB	43.0 GB
CFG-C	Large: L=96, H_kv=64 (Multi-Head Attention, MHA), D=128, BF16	12.9 GB	103 GB	412 GB
CFG-D	Mid INT8: L=80, H_kv=8 (GQA), D=128, INT8 (quantized)	0.67 GB	5.4 GB	21.5 GB
CFG-E (custom)	Implementer-defined: L=, H_kv=, D=, P=	Computed	Computed	Computed

NOTE: S_KV values are computed per the S_KV formula in (S_KV = 2 x L x H_kv x D x C x P_bytes) using binary context lengths (4K = 4,096; 32K = 32,768; 128K = 131,072 tokens) and are expressed in decimal gigabytes (1 GB = 10^9 bytes). Measurement: Report TTFT, T_transfer, and T_transfer/TTFT at P50, P95, P99 for each prompt length. The test is repeated a minimum of 100 times per prompt length. Reporting Format: Results 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 are 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 is shown as a shaded region.

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

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%.

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.

Test Category 7: Throughput Benchmarks Inference throughput determines the cost-effectiveness of the serving deployment.

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 until either TTFT P99 or ITL P99 exceeds the declared SLO pair (e.g., TTFT P99 > 500 ms, ITL P99 > 50 ms). At each rate, measure TPS_output, TPS_input, Inference_Goodput, and all latency KPIs. NOTE: For reference, interactive serving deployments typically target TTFT < 500 ms and ITL < 50 ms P99; these values are informative only and not requirements of this methodology. Measurement: Report TPS_output, TPS_input, Inference_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.

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.

Goodput Under Preemption and Eviction Objective: To measure the Inference_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 Inference_Goodput reduction. Measurement: Report Inference_Goodput, eviction rate (evictions/s), preemption rate (preemptions/s), wasted fabric bandwidth (GB/s), and the Inference_Goodput/TPS_output ratio (efficiency).

Test Category 8: Scale and Autoscaling Inference serving clusters must scale dynamically to match request demand.

Fabric Scale Limits for Inference Clusters Objective: To determine the maximum inference cluster size supportable by the DUT fabric while maintaining the declared SLO pair. 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.

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. This test is conducted under controlled laboratory conditions with a simulated autoscaler. It does not apply to live serving infrastructure.

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 is repeated a minimum of 20 times per failure scenario.

Test Category 9: Soak and Stability Long-running inference serving deployments must maintain performance without degradation over time.

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. Report the NIC QP error count, the routing flap count, and the variation in TTFT P99 over the test duration. Any nonzero QP error or routing flap count, or TTFT P99 variation exceeding 1%, is reported and investigated; these thresholds are reporting triggers for investigation, not pass/fail criteria.

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.

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. 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.

Reporting Format All test results are reported following the conventions established in Section 26. Where BusBW is reported (e.g., in the MoE expert parallelism tests), results MUST follow the BusBW reporting format defined in Section 3 of . In addition, the following inference-specific reporting elements apply:

• System Configuration Report: the report includes: 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 includes: 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 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.

Reporting Format Requirements

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 KPIs	Yes (required)
Latency Distribution Plots	CDF or histogram per test section	Recommended
Throughput vs. Scale Graphs	Line chart per test section	Recommended
Fabric Health Indicators	Table per	Yes (required)
Raw Data Appendix	Machine-readable format (CSV, JSON)	Optional

Security Considerations This document defines benchmarking methodology for controlled laboratory environments and does not specify any protocol mechanism. It therefore introduces no new protocol-level security considerations beyond those of the underlying technologies it references. The considerations below follow the BMWG convention established in and align with the companion terminology document . Benchmarking activities as described in this document are limited to technology characterization of AI inference serving fabrics using controlled stimuli in a laboratory environment, with dedicated address space and the constraints specified herein. The benchmarking network topology will be an independent test setup and MUST NOT be connected to devices that may forward the test traffic into a production network or misroute traffic to the test management network. This isolation requirement is particularly important for AI fabric benchmarking because the lossless transport modes referenced in this document (PFC, DCQCN, CBFC) propagate congestion hop-by-hop and can extend the blast radius of a misconfigured test beyond the immediate DUT. Benchmarking is performed on a "black-box" basis, relying solely on measurements observable external to the DUT as defined in . Special capabilities SHOULD NOT exist in the DUT specifically for benchmarking purposes. Any implications for network security arising from the DUT SHOULD be identical in the lab and in production networks. In particular, RDMA memory-region permissions and KV cache telemetry exposure are properties of the deployed configuration, not of the benchmarking methodology, and SHOULD reflect production posture during testing. Per , the tests defined herein MUST NOT be performed on production networks. The use of dedicated test IP address ranges per Appendix C (198.18.0.0/15 for IPv4; 2001:db8::/32 per for IPv6) is RECOMMENDED to prevent accidental interaction with production infrastructure. The following considerations are specific to inference-serving benchmarking:

• Synthetic prompt inputs: The KV cache contains intermediate state derived from prompt content. Synthetic inputs SHOULD be used for all tests in this document so that no production prompt content is processed in the test environment. KV cache transfer benchmarks use payload patterns that do not reflect real user data.
• One-sided RDMA write semantics: KV cache transfers in this document use one-sided RDMA PUT operations to remote NIC memory. Such operations bypass remote-CPU authorization at the data path; generators that leak onto adjacent fabrics could write arbitrary bytes to remote NICs. Line-rate RDMA traffic generators MUST be confined to the test fabric.
• PFC leakage: PFC PAUSE frames generated under bursty KV cache or AllToAll incast conditions () that escape the test environment can hang adjacent production switches sharing the same priority class. Physical or VLAN-based isolation of the test fabric is required.
• RDMA QP and PDC namespace isolation: when RDMA/RoCEv2 traffic is used, the test environment SHOULD be isolated from production RDMA fabrics to prevent QP number space collisions or inadvertent PFC propagation. When UET traffic is used, the test environment MUST ensure that UDP port 4793 traffic does not leak to production networks and that PDC identifier spaces are isolated.
• UET transport security sub-layer (TSS): SHOULD NOT be enabled during performance benchmarking unless transport security overhead is explicitly being measured.

IANA Considerations This memo includes no request to IANA.

References Normative References This memo discusses and defines a number of terms that are used in describing performance benchmarking tests and the results of such tests. This memo provides information for the Internet community. It does not specify an Internet standard. This document is a republication of RFC 1944 correcting the values for the IP addresses which were assigned to be used as the default addresses for networking test equipment. This memo provides information for the Internet community. This document is intended to provide methodology for the benchmarking of local area network (LAN) switching devices. This memo provides information for the Internet community. This framework describes a practical methodology for measuring end- to-end TCP Throughput in a managed IP network. The goal is to provide a better indication in regard to user experience. In this framework, TCP and IP parameters are specified to optimize TCP Throughput. This document is not an Internet Standards Track specification; it is published for informational purposes. The Benchmarking Methodology Working Group (BMWG) has been developing key performance metrics and laboratory test methods since 1990, and continues this work at present. The methods described in RFC 2544 are intended to generate traffic that overloads network device resources in order to assess their capacity. Overload of shared resources would likely be harmful to user traffic performance on a production network, and there are further negative consequences identified with production application of the methods. This memo clarifies the scope of RFC 2544 and other IETF BMWG benchmarking work for isolated test environments only, and it encourages new standards activity for measurement methods applicable outside that scope. This document is not an Internet Standards Track specification; it is published for informational purposes. The purposes of this informational document are to establish definitions and describe measurement techniques for data center benchmarking, as well as to introduce new terminology applicable to performance evaluations of data center network equipment. This document establishes the important concepts for benchmarking network switches and routers in the data center and is a prerequisite for the test methodology document (RFC 8239). Many of these terms and methods may be applicable to network equipment beyond the scope of this document as the technologies originally applied in the data center are deployed elsewhere. Cisco Blue Fern Consulting Huawei Huawei Apple This document defines benchmarking terminology for evaluating Ethernet-based network fabrics used in distributed Artificial Intelligence (AI) training and inference workloads. It provides a unified vocabulary consolidating and extending terms from "Benchmarking Terminology for Network Interconnect Devices" [RFC1242] and "Data Center Benchmarking Terminology" [RFC8238], and the companion AI fabric methodology documents, establishing precise, vendor-neutral definitions for collective communication primitives, RDMA transport mechanisms (RoCEv2 and Ultra Ethernet Transport), congestion control behaviors, AI-specific Key Performance Indicators (KPIs), and fabric topology concepts. This document is a companion to the AI training fabric benchmarking methodology [I-D.calabria-bmwg-ai-fabric-training-bench] and the AI inference fabric benchmarking methodology [I-D.calabria-bmwg-ai-fabric-inference-bench]. Those documents SHOULD NOT be applied without first consulting the terminology defined herein. Where definitions herein overlap with the foundational benchmarking terminology in [RFC1242] or [RFC8238], this document provides AI fabric context extensions and refinements; the foundational definitions in those RFCs remain authoritative for general network benchmarking. Cisco Blue Fern Consulting Huawei Huawei Apple This document defines benchmarking terminology, methodologies, and Key Performance Indicators (KPIs) for evaluating Ethernet-based AI training network fabrics. As large-scale distributed Artificial Intelligence / Machine Learning (AI/ML) training clusters grow to tens of thousands of accelerators (GPUs or generic accelerator processing units (XPUs)), the backend network fabric becomes the critical bottleneck determining Job Completion Time (JCT), training throughput, and accelerator utilization. This document establishes vendor-independent, reproducible test procedures for benchmarking fabric-level performance under realistic AI training workloads, covering Remote Direct Memory Access (RDMA) over Converged Ethernet version 2 (RoCEv2) transport, the Ultra Ethernet Transport (UET) protocol defined by the Ultra Ethernet Consortium (UEC) Specification 1.0 [UEC-1.0], congestion management (Priority Flow Control (PFC), Explicit Congestion Notification (ECN), Data Center Quantized Congestion Notification (DCQCN), Credit-Based Flow Control (CBFC)), load balancing strategies (Equal-Cost Multi- Path (ECMP), Dynamic Load Balancing (DLB), packet spraying), collective communication patterns (AllReduce, AllToAll, AllGather), and scale/soak testing. The methodology enables direct, reproducible comparison across different switch ASICs, vendor implementations, NIC transport stacks (RoCEv2 vs. UET), and fabric architectures (2-tier Clos, 3-tier Clos, rail-optimized). Ultra Ethernet Consortium In many standards track documents several words are used to signify the requirements in the specification. These words are often capitalized. This document defines these words as they should be interpreted in IETF documents. This document specifies an Internet Best Current Practices for the Internet Community, and requests discussion and suggestions for improvements. RFC 2119 specifies common key words that may be used in protocol specifications. This document aims to reduce the ambiguity by clarifying that only UPPERCASE usage of the key words have the defined special meanings. Informative References To reduce the likelihood of conflict and confusion when relating documented examples to deployed systems, an IPv6 unicast address prefix is reserved for use in examples in RFCs, books, documentation, and the like. Since site-local and link-local unicast addresses have special meaning in IPv6, these addresses cannot be used in many example situations. The document describes the use of the IPv6 address prefix 2001:DB8::/32 as a reserved prefix for use in documentation. This memo provides information for the Internet community.

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

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
TTFT_fabric	6.1, 10.1	DUT-PD
ITL_fabric	10.2	DUT-F
E2E_latency	10.3	SUT-E
TPS_output	6.2, 11.1, 11.2, 11.3	SUT-E
TPS_input	11.1	SUT-E
TPS_per_GPU	11.2	SUT-E
Inference_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	DUT-S
PFC_frame_count	8.2, 8.4	DUT-S
Link_utilization	5.3, 9.3, 12.1	DUT-F
Packet_drop_rate	8.3, 12.2	DUT-F
Request_Rate	11.1	SUT-E
Prefix_cache_hit_rate	9.2	SUT-E
JFI_decode	9.4	SUT-E

Indicative Reference Values (Non-Normative) This appendix provides indicative reference values for the KPIs defined in . The values reflect current industry observations for interactive inference workloads as of 2025-2026. These values are NON-NORMATIVE and do not constitute benchmarking acceptance criteria or performance requirements. Per the BMWG charter, the definition of acceptance criteria or 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 criteria in vendor evaluations. Deployment-specific SLOs will vary by application, model architecture, and operator requirements. Indicative Reference Values for Interactive Inference Serving (Non-Normative)

KPI	Indicative Reference (Interactive)
TTFT	< 500 ms P99
ITL	< 50 ms P99
TTFT_fabric	< 300 ms P99
ITL_fabric	< 5 ms P99
E2E_latency	varies by output length

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) document which of the following capabilities their serving framework supports. Framework Capability Categories

Capability Category	Description	Relevance to Fabric Benchmarking
Disaggregated Prefill/Decode (PD)	Physical separation of prefill and decode execution across different accelerator pools	Determines whether DUT-PD topology tests apply ()
KV Cache Transfer Protocol	Protocol and library used for prefill-to-decode KV state transfer (one-sided PUT, two-sided SEND/RECV, GPU-initiated)	Determines RDMA verb types under test and applicable frame formats ()
MoE Expert Parallelism (EP) Support	Distribution of MoE expert sub-networks across GPUs and AllToAll dispatch mode support	Determines whether MoE EP tests apply ()
Continuous Batching	Dynamic request admission to active inference batches	Affects request arrival rate distributions and load balancing tests in
Prefix / KV Cache Sharing	Reuse of KV cache segments for requests with common prefixes	Determines applicability of the prefix cache hit rate test in
RDMA Transport Support	Underlying transport(s) supported: RoCEv2, UET, or other	Documented in the test report; affects congestion management test interpretation in
GPU-Initiated Networking (GIN) Support	Ability for GPU threads to directly initiate RDMA operations without CPU involvement	Affects RDMA primitive choice in MoE dispatch tests ()
Container Orchestration Integration	Native support for container-based deployment and horizontal scaling	Relevant for autoscaling tests in
Maximum Reported Scale	Maximum cluster scale at which the framework has been validated	Documents applicability of fabric scale tests

NOTE: The specific framework name, version, and configuration are documented in all test reports. Results obtained with different frameworks are not directly comparable; framework identity is a required reporting parameter per .

KV Cache Transfer Frame Format This appendix defines the reference frame format for KV cache transfer benchmarking over RoCEv2 using one-sided RDMA WRITE (PUT) operations. The frame format follows the standard RoCEv2 encapsulation defined in the InfiniBand Architecture Specification Volume 1 Annex A17 (RoCEv2). RoCEv2 KV Cache Transfer Frame (One-Sided RDMA WRITE)

Offset	Field	Size	Value / Description
00	Ethernet Dst MAC	6B	DUT next-hop MAC
06	Ethernet Src MAC	6B	Test equipment MAC
12	EtherType / Tag Protocol Identifier (TPID)	2B	0x0800 (IPv4) or 0x86DD (IPv6) when untagged; 0x8100 (TPID) when 802.1Q-tagged
14	802.1Q Tag (optional)	4B	When tagged: TCI (PCP for RDMA priority class, VID) followed by inner EtherType 0x0800 or 0x86DD. Omit this row when untagged and shift subsequent offsets back by 4B
18	IPv4 / IPv6 Header	20B (IPv4) or 40B (IPv6)	DSCP=26 (AF31), ECN=ECT(0), Proto=17 (UDP)
38 / 58	UDP Header	8B	DstPort=4791 (RoCEv2), SrcPort=entropy for ECMP, UDP Length, UDP Checksum
46 / 66	BTH (Base Transport Header)	12B	OpCode=0x0A (RDMA WRITE Only) or 0x0B (RDMA WRITE with Immediate Data) at the last packet of a PUT-with-signal sequence; SE, M, Pad, TVer flags; PKey; Destination QP Number (24 bits); A flag; PSN (24 bits)
58 / 78	RETH (RDMA Extended Transport Header)	16B	Virtual Address (64 bits), R_Key (32 bits), DMA Length (32 bits). The DMA Length indicates the size of the KV cache block transferred by this WRITE operation
74 / 94	KV Cache Payload	variable, up to MTU	Key/value attention state data
var	ICRC	4B	Invariant CRC
var+4	FCS	4B	Ethernet Frame Check Sequence

Notes:

• The UDP Source Port uses entropy-based values for ECMP load distribution across fabric paths.
• The RETH 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 deployments is 4096 bytes; larger KV cache blocks (e.g., 64 KB pages) are segmented into multiple packets by the NIC. The first packet of a segmented WRITE carries OpCode 0x06 (RDMA WRITE First) and a RETH; intermediate packets carry OpCode 0x07 (RDMA WRITE Middle); the last packet carries OpCode 0x08 (RDMA WRITE Last) or 0x0B (RDMA WRITE Last with Immediate Data) for PUT-with-signal completion signalling.
• For UET-based KV cache transfers, the frame format defined in the UET Frame Format appendix of applies; the UDP destination port is 4793 and the transport service indicator selects between ROD and RUD per test.

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. MoE Dispatch Traffic Characteristics by Mode

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

For a representative MoE configuration (M3: E=256, k=2, H_model=7168, EP=96 across 12 nodes of 8 accelerators, BF16; representative of a large publicly described MoE-class architecture), 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, ~700 MB aggregate across all 96 x 95 = 9,120 pairs.
• Low-Latency Dispatch (decode, batch=8): 8 * 2 * 7168 * 2 bytes / 96 GPUs = ~2.4 KB per GPU pair, ~22 MB aggregate.

With 61 MoE layers (representative of a publicly described large-scale MoE architecture) and a decode iteration time target of ~30 ms, the decode phase requires 61 AllToAll dispatches within 30 ms. This yields 61 dispatches per decode step, or approximately 2,000 dispatches per second, and consumes approximately 45 GB/s aggregate inter-node bandwidth for the Low-Latency Dispatch path.

Model Architecture Parameters This appendix provides a sample calculation for the S_KV formula defined in . It is based on a 70B-parameter dense model at FP16 with 4K context.

Acknowledgments This work has benefited from the discussions that occurred during the joint IPPM and BMWG meeting and on the BMWG mailing list. Thanks to Carsten Rossenhoevel and Mohamed Boucadair for valuable review and comments.