BMWG F. Calabria Internet-Draft Cisco Intended status: Informational C. Pignataro Expires: 7 January 2027 Blue Fern Consulting Q. Wu G. Fioccola Huawei S. Reddy Apple 6 July 2026 Benchmarking Terminology for AI Network Fabrics draft-calabria-bmwg-ai-fabric-terminology-03 Abstract This document defines benchmarking terminology for evaluating Ethernet-based network fabrics used in distributed Artificial Intelligence (AI) training and inference workloads. It consolidates and extends terms from "Benchmarking Terminology for Network Interconnect Devices" (RFC 1242) and "Data Center Benchmarking Terminology" (RFC 8238). Definitions cover 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 and inference fabric benchmarking methodology documents. Those documents are intended to be read together with the terminology defined here. Where definitions herein overlap with the foundational benchmarking terminology in RFC 1242 or RFC 8238, this document provides AI fabric context extensions and refinements; the foundational definitions in those RFCs remain authoritative for general network benchmarking. 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-terminology/draft-calabria- bmwg-ai-fabric-terminology.html. Status information for this document may be found at https://datatracker.ietf.org/doc/draft- calabria-bmwg-ai-fabric-terminology/. Source for this draft and an issue tracker can be found at https://github.com/fcalabri/bmwg-ai-fabric-terminology. Calabria, et al. Expires 7 January 2027 [Page 1] Internet-Draft AI Fabric Benchmarking Terminology July 2026 Status of This Memo This Internet-Draft is submitted in full conformance with the provisions of BCP 78 and BCP 79. Internet-Drafts are working documents of the Internet Engineering Task Force (IETF). Note that other groups may also distribute working documents as Internet-Drafts. The list of current Internet- Drafts is at https://datatracker.ietf.org/drafts/current/. Internet-Drafts are draft documents valid for a maximum of six months and may be updated, replaced, or obsoleted by other documents at any time. It is inappropriate to use Internet-Drafts as reference material or to cite them other than as "work in progress." This Internet-Draft will expire on 7 January 2027. 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/ license-info) in effect on the date of publication of this document. Please review these documents carefully, as they describe your rights and restrictions with respect to this document. Code Components extracted from this document must include Revised BSD License text as described in Section 4.e of the Trust Legal Provisions and are provided without warranty as described in the Revised BSD License. Table of Contents 1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . 3 1.1. Requirements Language . . . . . . . . . . . . . . . . . . 3 1.2. Scope and Purpose . . . . . . . . . . . . . . . . . . . . 3 1.3. Relationship to Existing BMWG Work . . . . . . . . . . . 4 1.4. Relationship to Companion Documents . . . . . . . . . . . 4 2. General Benchmarking Terms . . . . . . . . . . . . . . . . . 4 3. Collective Communication Terms . . . . . . . . . . . . . . . 6 4. Distributed Parallelism Strategy Terms . . . . . . . . . . . 9 5. Network Transport Terms . . . . . . . . . . . . . . . . . . . 10 5.1. RoCEv2 and RDMA Terms . . . . . . . . . . . . . . . . . . 10 5.2. Ultra Ethernet Transport (UET) Terms . . . . . . . . . . 12 5.2.1. UET Transport Services Comparison . . . . . . . . . . 14 6. Congestion Control and Fabric Behavior Terms . . . . . . . . 14 6.1. Load Balancing Strategy Comparison . . . . . . . . . . . 16 7. Fabric Topology and Infrastructure Terms . . . . . . . . . . 17 Calabria, et al. Expires 7 January 2027 [Page 2] Internet-Draft AI Fabric Benchmarking Terminology July 2026 8. Training-Specific Terms . . . . . . . . . . . . . . . . . . . 20 9. Inference-Specific Terms . . . . . . . . . . . . . . . . . . 21 9.1. Inference Phase Characteristics . . . . . . . . . . . . . 25 10. KPI Classification Terms . . . . . . . . . . . . . . . . . . 26 10.1. KPI Tier Summary . . . . . . . . . . . . . . . . . . . . 27 11. Referenced Standards Abbreviations . . . . . . . . . . . . . 28 12. IANA Considerations . . . . . . . . . . . . . . . . . . . . . 30 13. Security Considerations . . . . . . . . . . . . . . . . . . . 30 Acronyms . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31 Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . . . 34 References . . . . . . . . . . . . . . . . . . . . . . . . . . . 34 Normative References . . . . . . . . . . . . . . . . . . . . . 34 Informative References . . . . . . . . . . . . . . . . . . . . 35 Appendix A: Term Cross-Reference to Companion Documents . . . . . 36 Appendix B: Term Taxonomy Summary . . . . . . . . . . . . . . . . 37 Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . 39 1. Introduction 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 Purpose This document defines terminology for benchmarking Ethernet-based AI network fabrics in controlled laboratory environments. The defined terms cover: distributed AI training collective communication patterns, LLM inference serving architectures, RDMA transport semantics (RoCEv2 and UET), congestion control mechanisms, fabric topology characteristics, and performance metric definitions. This document does not define acceptance criteria, performance requirements, or configuration recommendations. It does not address benchmarking of live operational networks, intra-node (NVLink/PCIe) interconnects, or storage networking. Calabria, et al. Expires 7 January 2027 [Page 3] Internet-Draft AI Fabric Benchmarking Terminology July 2026 1.3. Relationship to Existing BMWG Work This document extends the foundational BMWG terminology established in [RFC1242] (network interconnect benchmarking terminology) and [RFC8238] (data center benchmarking terminology). Where terms are defined in those RFCs, this document provides AI fabric context extensions; the core definitions remain as established. This document also extends the test methodology framework of [RFC2544] and [RFC8239] as applied in the companion AI fabric methodology documents. 1.4. Relationship to Companion Documents This document is one of three companion Internet-Drafts addressing AI fabric benchmarking: * This document: Terminology definitions. * [I-D.calabria-bmwg-ai-fabric-training-bench]: Benchmarking methodology for AI training workloads. * [I-D.calabria-bmwg-ai-fabric-inference-bench]: Benchmarking methodology for AI inference serving workloads. Implementers and evaluators SHOULD read this terminology document before applying the companion methodology documents. Terms defined here are used normatively in those documents and are not redefined there unless the specific workload context introduces a substantive difference, which is noted explicitly. 2. General Benchmarking Terms The following terms establish the general measurement framework applicable to all AI fabric benchmarking activities. +=============+==================================================+ | Term | Definition | +=============+==================================================+ | *AI Fabric* | The dedicated Ethernet backend network | | | interconnecting accelerators (GPUs/XPUs) for | | | distributed AI training and inference workloads. | | | Implemented as a non-blocking Clos (fat-tree) | | | topology running RoCEv2 or UET. The AI fabric | | | is separate from the front-end management and | | | storage network. | +-------------+--------------------------------------------------+ | *DUT* | Device Under Test. The network element(s) whose | | | performance characteristics are being measured. | Calabria, et al. Expires 7 January 2027 [Page 4] Internet-Draft AI Fabric Benchmarking Terminology July 2026 | | In AI fabric benchmarking the DUT is one or more | | | fabric elements: leaf switches, spine switches, | | | NICs, or the complete fabric assembly. | +-------------+--------------------------------------------------+ | *SUT* | System Under Test. The complete AI compute | | | system including accelerators, NICs, the fabric | | | DUT, and serving/training software, when end-to- | | | end metrics are the measurement objective. | +-------------+--------------------------------------------------+ | *RT* | Router Tester / Traffic Generator. Test | | | equipment capable of generating and receiving | | | network traffic at specified rates with | | | nanosecond-resolution timestamping sufficient | | | for the measurements defined in the companion | | | methodology documents. | +-------------+--------------------------------------------------+ | *JFI* | Jain's Fairness Index. A scalar measure of | | | flow-level throughput fairness across n flows | | | [Jain1984]: JFI = (Σxᵢ)² / (n · Σxᵢ²) where xᵢ | | | is the throughput of flow i. A value of 1.0 | | | indicates perfect fairness; lower values | | | indicate disparity. *SHOULD* be reported | | | alongside throughput measurements for all multi- | | | flow AI fabric tests. | +-------------+--------------------------------------------------+ | *Offered | The total traffic rate presented to the DUT from | | Load* | test equipment, expressed as a fraction of line | | | rate (0–100%) or as absolute bit/s. Offered | | | load is controlled independently of DUT | | | absorption, enabling characterization of | | | saturation behavior. | +-------------+--------------------------------------------------+ | *Trial | The time interval over which a single | | Duration* | measurement is conducted. For AI fabric tests, | | | the *RECOMMENDED* minimum is 60 seconds for | | | throughput tests and 300 seconds for congestion | | | and stability sub-tests, per the methodology in | | | [RFC2544] as extended in the companion | | | methodology documents. Soak tests use a | | | substantially longer duration (minimum 24 hours) | | | per the Soak Test definition in Table 10. | +-------------+--------------------------------------------------+ | *Warmup | A mandatory pre-measurement interval during | | Period* | which traffic is sent but results are not | | | recorded. Ensures adaptive routing tables, PFC | | | watermarks, and DCQCN/UET congestion controllers | | | reach steady state before measurement begins. | | | *RECOMMENDED* minimum: 10 seconds. | Calabria, et al. Expires 7 January 2027 [Page 5] Internet-Draft AI Fabric Benchmarking Terminology July 2026 +-------------+--------------------------------------------------+ | *Binary | An iterative test procedure for determining the | | Search* | maximum offered load at which a DUT meets a | | | specified acceptance criterion (e.g., zero | | | packet loss). The search halves the candidate | | | load range at each iteration, converging to a | | | resolution of 0.1% offered load within 10 | | | iterations. | +-------------+--------------------------------------------------+ | *Percentile | A latency statistic expressing that the | | Latency* | specified fraction of all measured latency | | | samples fall at or below the reported value. | | | Denoted Pxx (e.g., P50, P95, P99, P99.9). Tail | | | latency (P99 and above) is especially relevant | | | for AI fabric benchmarking because SLO | | | violations are determined by worst-case, not | | | median, performance. | +-------------+--------------------------------------------------+ Table 1: General Benchmarking Terms 3. Collective Communication Terms The following terms define the collective communication operations that are the primary traffic sources in distributed AI workloads. +=================+===============================================+ | Term | Definition | +=================+===============================================+ | *Collective | A coordinated communication pattern executed | | Operation* | simultaneously across all accelerators in a | | | training or inference group. Core | | | collectives: AllReduce (gradient | | | aggregation), AllGather (parameter | | | distribution), ReduceScatter (partial | | | reduction + scatter), and AllToAll (expert | | | dispatch in MoE models). | +-----------------+-----------------------------------------------+ | *AllReduce* | A collective in which each participant | | | contributes a tensor and all participants | | | receive the element-wise sum (or other | | | reduction) of all contributions. The | | | dominant communication primitive in data- | | | parallel and tensor-parallel training. BusBW | | | is the primary KPI. | +-----------------+-----------------------------------------------+ | *AllGather* | A collective in which each participant | | | contributes a shard of a tensor and all | Calabria, et al. Expires 7 January 2027 [Page 6] Internet-Draft AI Fabric Benchmarking Terminology July 2026 | | participants receive the concatenation of all | | | shards. Used in tensor-parallel layer | | | sharding to reconstruct distributed | | | activations or parameters. | +-----------------+-----------------------------------------------+ | *ReduceScatter* | A collective combining an element-wise | | | reduction with a scatter, so each participant | | | receives a distinct slice of the reduced | | | result. Used in ZeRO-stage optimizer | | | strategies and as the first half of a ring- | | | AllReduce. | +-----------------+-----------------------------------------------+ | *AllToAll* | A collective in which each participant sends | | | a distinct payload to every other participant | | | and receives a distinct payload from every | | | other participant. The critical collective | | | for Mixture-of-Experts token dispatch. | | | Generates N(N−1) independent point-to-point | | | flows for N participants. | +-----------------+-----------------------------------------------+ | *Ring | An AllReduce (or AllGather/ReduceScatter) | | Algorithm* | algorithm structured as a logical ring of | | | participants. Each participant sends to its | | | right neighbor and receives from its left | | | neighbor in 2(N−1) steps. Ring AllReduce | | | transfers 2(N−1)/N times the message size per | | | accelerator (the AllReduce algo_factor in the | | | BusBW definition), approaching 2× for large | | | N, and is bandwidth-optimal. Standard | | | baseline for BusBW calculation. | +-----------------+-----------------------------------------------+ | *BusBW* | The effective data throughput per accelerator | | | during a collective operation, computed as: | | | | | | BusBW = (data_size × algo_factor) / time | | | | | | algo_factor is a fixed normalization constant | | | derived from the ideal ring algorithm for | | | each collective type, applied regardless of | | | the algorithm actually selected by the | | | collective library at runtime. This makes | | | BusBW algorithm-invariant: the same hardware | | | moving the same data volume in the same time | | | yields the same BusBW whether the library | | | selects ring, tree, or recursive doubling. | | | The algo_factor calculation MUST conform to | | | the formula specified here. | | | | Calabria, et al. Expires 7 January 2027 [Page 7] Internet-Draft AI Fabric Benchmarking Terminology July 2026 | | Collective algo_factor | | | AllReduce 2 × (n−1) / n | | | AllGather (n−1) / n | | | ReduceScatter (n−1) / n | | | AllToAll (n−1) / n | | | | | | n = number of participating accelerators. | | | | | | Worked example — AllReduce, n=8, data_size=1 | | | GB, time=10 ms: | | | algo_factor = 2 × (8−1) / 8 = 1.75 | | | BusBW = (1 GB × 1.75) / 10 ms = 175 GB/s | | | | | | Reports MUST state: collective type, | | | algo_factor value, collective library name | | | and version, and n. The algorithm actually | | | selected by the library SHOULD be reported as | | | diagnostic information when known. Units: | | | GB/s or Gbps; reports MUST state which. | +-----------------+-----------------------------------------------+ | *CCL* | Collective Communication Library. A software | | | library providing optimized implementations | | | of collective operations (AllReduce, | | | AllGather, etc.) over a specific transport. | | | The CCL implementation *MUST* be documented | | | in the test report. | +-----------------+-----------------------------------------------+ | *SPMD* | Single Program Multiple Data. The execution | | | model underlying bulk-synchronous distributed | | | training, in which all accelerators execute | | | identical computation on distinct data | | | partitions, synchronizing at collective | | | barriers between steps. | +-----------------+-----------------------------------------------+ | *Bulk | A distributed computation model structured as | | Synchronous | alternating compute and communicate phases | | Parallel (BSP)* | with a global synchronization barrier between | | | phases. Standard training workloads follow | | | BSP: forward pass → backward pass → AllReduce | | | gradient sync → optimizer step. | +-----------------+-----------------------------------------------+ Table 2: Collective Communication Terms Calabria, et al. Expires 7 January 2027 [Page 8] Internet-Draft AI Fabric Benchmarking Terminology July 2026 4. Distributed Parallelism Strategy Terms The following terms define the parallelism strategies used in distributed AI model training and inference, which determine traffic patterns and fabric requirements. +=============+==================================================+ | Term | Definition | +=============+==================================================+ | *Data | A distributed training strategy replicating the | | Parallelism | full model on each accelerator, partitioning the | | (DP)* | training dataset across replicas. Gradient | | | synchronization after each backward pass | | | requires an AllReduce across all DP ranks. | | | Memory-efficient for small models; communication | | | overhead scales with parameter count. | +-------------+--------------------------------------------------+ | *Tensor | A distributed training and inference strategy | | Parallelism | partitioning individual weight matrices across | | (TP)* | multiple accelerators. Each rank computes a | | | partial result; AllGather or ReduceScatter | | | collectives are required within each layer to | | | aggregate results. Dominant parallelism within | | | a node (intra-node). | +-------------+--------------------------------------------------+ | *Pipeline | A distributed strategy assigning contiguous | | Parallelism | groups of transformer layers to distinct stages | | (PP)* | (accelerators or nodes). Each stage processes | | | one microbatch and forwards activations to the | | | next stage. Generates point-to-point inter- | | | stage traffic across the fabric (activations and | | | gradients). | +-------------+--------------------------------------------------+ | *Expert | A parallelism strategy for Mixture-of-Experts | | Parallelism | models distributing expert sub-networks across | | (EP)* | accelerators. Each token is routed to its | | | designated experts (typically top-K of E total | | | experts), requiring AllToAll communication for | | | dispatch. Wide EP (e.g., 96-way) generates | | | dense inter-node AllToAll at every MoE layer. | +-------------+--------------------------------------------------+ | *MoE* | Mixture of Experts. A transformer architecture | | | replacing dense feed-forward layers with a set | | | of E expert sub-networks, of which only top-K | | | experts (typically K=2 or K=4) are activated per | | | token via a learned router. MoE enables large | | | model capacity with sub-linear compute, but | | | introduces AllToAll communication requirements | Calabria, et al. Expires 7 January 2027 [Page 9] Internet-Draft AI Fabric Benchmarking Terminology July 2026 | | proportional to E and sequence length. | +-------------+--------------------------------------------------+ | *DP | Data Parallelism applied to the attention | | Attention* | computation, where the KV cache is partitioned | | | across data-parallel ranks. Each rank holds 1/ | | | DP_SIZE of the KV cache; AllToAll communication | | | exchanges attention outputs. Used in inference | | | to reduce per-accelerator memory footprint for | | | long contexts. | +-------------+--------------------------------------------------+ | *ZeRO* | Zero Redundancy Optimizer. A memory | | | optimization strategy for data-parallel training | | | that shards model states (parameters, gradients, | | | optimizer states) across DP ranks instead of | | | replicating them. Stage 1 shards optimizer | | | states; Stage 2 adds gradient sharding; Stage 3 | | | adds parameter sharding. Each stage increases | | | AllGather/ReduceScatter communication. | +-------------+--------------------------------------------------+ Table 3: Distributed Parallelism Strategy Terms 5. Network Transport Terms 5.1. RoCEv2 and RDMA Terms The following terms define RDMA and RoCEv2 transport semantics as used in AI fabric benchmarking. UET, PDC, and ROD are included here for direct comparison with their RoCEv2 counterparts; full UET- specific terms are defined in Section 5.2. +===========+=======================================================+ | Term | Definition | +===========+=======================================================+ | *RDMA* | Remote Direct Memory Access. A transport | | | mechanism enabling direct memory-to-memory data | | | transfer between hosts without involving the | | | destination CPU, providing zero-copy semantics | | | and kernel bypass. Implementations include | | | InfiniBand Verbs (native IB), iWARP (RDMA over | | | TCP), and RoCEv2 (RDMA over Converged Ethernet | | | v2). | +-----------+-------------------------------------------------------+ | *RoCEv2* | RDMA over Converged Ethernet version 2. An RDMA | | | transport encapsulating InfiniBand transport | | | layer (BTH) over UDP/IP, enabling RDMA semantics | | | on standard Ethernet infrastructure. Requires | | | lossless fabric operation (PFC or equivalent) | Calabria, et al. Expires 7 January 2027 [Page 10] Internet-Draft AI Fabric Benchmarking Terminology July 2026 | | for correctness. Standardized in IBTA | | | InfiniBand Architecture Volume 1, Annex A17 | | | (RoCEv2, September 2014) [IBTA-ROCE]; | | | transported over UDP destination port 4791. | +-----------+-------------------------------------------------------+ | *QP* | Queue Pair. The fundamental RDMA communication | | | endpoint comprising a Send Queue (SQ) and | | | Receive Queue (RQ). QPs are connection-oriented | | | in Reliable Connected (RC) mode. Multiple QPs | | | per source-destination pair are used to increase | | | ECMP entropy in fabric load balancing. | +-----------+-------------------------------------------------------+ | *Reliable | An RDMA QP transport service type providing | | Connected | reliable, in-order delivery between exactly two | | (RC)* | endpoints. The primary QP type for AI | | | collective operations via RoCEv2. Requires | | | connection setup before data transfer and | | | maintains per-QP state for retransmission. | +-----------+-------------------------------------------------------+ | *RDMA | An operation primitive of the RDMA programming | | Verb* | model. Key verbs: SEND/RECV (two-sided, | | | receiver must post a buffer), WRITE (one-sided, | | | target memory written directly), READ (one- | | | sided, remote memory read), and Atomic (compare- | | | and-swap, fetch-and-add). AI collectives | | | predominantly use WRITE and SEND. | +-----------+-------------------------------------------------------+ | *UET* | Ultra Ethernet Transport. A transport protocol | | | defined by the Ultra Ethernet Consortium (UEC) | | | Specification 1.0 as a next-generation AI/HPC | | | fabric transport. UET is connectionless, | | | supports native packet spraying (RUD), and | | | integrates multipath load balancing and | | | congestion control. Transported over UDP | | | destination port 4793 (IANA registration | | | pending). | +-----------+-------------------------------------------------------+ | *PDC* | Packet Delivery Context. The ephemeral, | | | lightweight transport endpoint in UET, analogous | | | to but distinct from an RDMA Queue Pair. PDCs | | | are connectionless (no setup handshake), | | | enabling low-latency initiation and reduced per- | | | flow state in the NIC and switch. | +-----------+-------------------------------------------------------+ | *ROD* | Reliable Ordered Delivery. A UET transport | | | service providing reliable, in-order packet | | | delivery, semantically equivalent to RoCEv2 RC | | | mode. Suitable for legacy RDMA applications | Calabria, et al. Expires 7 January 2027 [Page 11] Internet-Draft AI Fabric Benchmarking Terminology July 2026 | | requiring strict ordering guarantees. | +-----------+-------------------------------------------------------+ Table 4: RoCEv2 and RDMA Terms 5.2. Ultra Ethernet Transport (UET) Terms The following terms define UET-specific concepts introduced by the Ultra Ethernet Consortium (UEC) Specification 1.0 [UEC-1.0]. +===========+=======================================================+ | Term | Definition | +===========+=======================================================+ | *RUD* | Reliable Unordered Delivery. A UET transport | | | service providing reliable delivery without | | | maintaining packet order across paths. Enables | | | native packet spraying across ECMP paths without | | | reorder-buffer overhead at the receiver NIC. The | | | preferred UET service class for AI training | | | collectives. | +-----------+-------------------------------------------------------+ | *RUDI* | Reliable Unordered Delivery for Idempotent | | | operations. A UET transport service optimized for | | | operations safe to execute more than once (e.g., | | | RDMA Writes to non-accumulating targets), allowing | | | simplified retransmission logic with reduced state | | | overhead. | +-----------+-------------------------------------------------------+ | *UUD* | Unreliable Unordered Delivery. A UET transport | | | service providing best-effort, unordered packet | | | delivery with minimal overhead. Suitable for | | | telemetry, speculative operations, or workloads | | | with application-layer loss tolerance. | +-----------+-------------------------------------------------------+ | *UEC | A defined subset of UET features targeting a | | Profile* | specific use case: AI Base (core AI training/ | | | inference, mandatory feature set), AI Full (AI Base | | | plus deferred send, exact-match tagging, extended | | | atomics), or HPC (latency-optimized for traditional | | | HPC workloads with fine-grained synchronization). | +-----------+-------------------------------------------------------+ | *LLR* | Link Layer Retry. An optional UEC link-layer | | | enhancement providing fast per-hop error recovery | | | at the Ethernet link layer. LLR detects symbol | | | errors at the FEC level and retransmits the | | | affected frame before it is dropped, reducing the | | | frequency of transport-layer retransmission and | | | improving tail latency. | Calabria, et al. Expires 7 January 2027 [Page 12] Internet-Draft AI Fabric Benchmarking Terminology July 2026 +-----------+-------------------------------------------------------+ | *Packet | An optional UEC link-layer behavior in which a | | Trimming* | congested switch, rather than dropping the full | | | packet, transmits only the packet header (trimmed | | | packet) to the receiver. Trimming enables the | | | receiver to detect loss and initiate selective | | | retransmission more rapidly, reducing bandwidth | | | waste versus silent drop. | +-----------+-------------------------------------------------------+ | *PRI* | Packet Rate Improvement. An optional UEC link- | | | layer feature that compresses redundant Ethernet | | | and IP header fields on a link, reducing per-packet | | | overhead and increasing the effective packet rate, | | | particularly for the small packets characteristic | | | of AI/HPC synchronization traffic. | +-----------+-------------------------------------------------------+ | *CBFC* | Credit-Based Flow Control. An optional UEC link- | | | layer buffer management mechanism using explicit | | | credit grants from downstream to upstream devices. | | | CBFC provides backpressure without transmitting PFC | | | PAUSE frames, eliminating the head-of-line blocking | | | and storm propagation risks associated with PFC. | +-----------+-------------------------------------------------------+ | *Entropy | A per-packet field in the UET header used to | | Value* | distribute packets of a single message across | | | available ECMP paths, providing explicit spray | | | entropy independent of the IP 5-tuple. Enables | | | hardware-assisted packet spraying without requiring | | | transport-layer state in the switch. | +-----------+-------------------------------------------------------+ | *GIN* | GPU-Initiated Networking. A communication paradigm | | | in which GPU threads directly initiate network RDMA | | | operations (sends, one-sided writes/reads) to the | | | NIC hardware without CPU involvement, eliminating | | | the CPU-GPU synchronization round-trip. Reduces | | | effective latency by several microseconds for fine- | | | grained operations. | +-----------+-------------------------------------------------------+ | *KVCXL* | KV Cache Transfer Library. A software library | | | providing standardized point-to-point data transfer | | | primitives (register, transfer, notify) for | | | inference engines, abstracting underlying transport | | | mechanisms (intra-node interconnect, RDMA, PCIe, | | | storage interfaces). Enables transport-agnostic KV | | | cache migration in disaggregated serving | | | architectures. | +-----------+-------------------------------------------------------+ Calabria, et al. Expires 7 January 2027 [Page 13] Internet-Draft AI Fabric Benchmarking Terminology July 2026 Table 5: Ultra Ethernet Transport (UET) Terms 5.2.1. UET Transport Services Comparison +=========+=========+==========+================+================+ | Service | Ordered | Reliable | Retransmission | Primary Use | | | | | Complexity | Case | +=========+=========+==========+================+================+ | *ROD* | Yes | Yes | Full per-QP | Legacy RDMA / | | | | | state | ordered AI ops | +---------+---------+----------+----------------+----------------+ | *RUD* | No | Yes | Reduced | AI training | | | | | (unordered) | collectives | | | | | | with spray | +---------+---------+----------+----------------+----------------+ | *RUDI* | No | Yes | Minimal | RDMA Writes; | | | | | (idempotent) | simple | | | | | | retransmit | +---------+---------+----------+----------------+----------------+ | *UUD* | No | No | None | Telemetry, | | | | | | speculative | | | | | | ops | +---------+---------+----------+----------------+----------------+ Table 6: UET Transport Services Comparison 6. Congestion Control and Fabric Behavior Terms The following terms define congestion management mechanisms and associated fabric behaviors critical to AI workload performance. +===========+=======================================================+ | Term | Definition | +===========+=======================================================+ | *PFC* | Priority Flow Control (IEEE 802.1Qbb). A | | | lossless Ethernet mechanism in which a receiver | | | transmits a PAUSE frame to its upstream | | | neighbor on a specific priority class when its | | | ingress buffer approaches a configured | | | threshold, temporarily halting transmission of | | | that priority. Required for lossless RoCEv2 | | | operation. PFC operates hop-by-hop and can | | | propagate congestion upstream (PFC storm risk). | +-----------+-------------------------------------------------------+ | *PFC | A pathological condition in which PFC PAUSE | | Storm* | frames propagate across multiple hops, causing | | | widespread throughput degradation or deadlock | | | unrelated to the original congestion source. | Calabria, et al. Expires 7 January 2027 [Page 14] Internet-Draft AI Fabric Benchmarking Terminology July 2026 | | Detection and mitigation *SHOULD* be part of | | | soak test evaluation per the companion | | | methodology documents. | +-----------+-------------------------------------------------------+ | *PFC | A circular PFC dependency in which sets of | | Deadlock* | flows mutually pause each other indefinitely, | | | resulting in zero progress for affected traffic | | | classes. Deadlock risk is elevated in non-tree | | | topologies and *MUST* be evaluated in fabric- | | | level soak tests. | +-----------+-------------------------------------------------------+ | *ECN* | Explicit Congestion Notification ([RFC3168]). | | | An IP-layer mechanism in which a congested | | | router marks packets with the Congestion | | | Experienced (CE) codepoint in the IP ECN field | | | instead of dropping them. The receiver echoes | | | congestion feedback to the sender via the | | | transport protocol, triggering rate reduction. | | | Used with RoCEv2 as part of DCQCN. | +-----------+-------------------------------------------------------+ | *DCQCN* | Data Center Quantized Congestion Notification. | | | An end-to-end congestion control algorithm for | | | RoCEv2 flows, combining ECN marking at | | | congested switches with rate-based sender | | | reduction using an AIMD scheme. PFC and DCQCN | | | are distinct mechanisms. PFC prevents packet | | | loss during DCQCN convergence; it is *not* part | | | of the DCQCN algorithm. | +-----------+-------------------------------------------------------+ | *ECN | The fraction of packets (expressed as a | | Marking | percentage) that are marked with the CE | | Ratio* | codepoint in the IP ECN field over a | | | measurement interval. A high ECN Marking Ratio | | | indicates persistent congestion and is a | | | primary Fabric Health Indicator. | +-----------+-------------------------------------------------------+ | *Incast* | A traffic pattern in which multiple sources | | | simultaneously send to a single destination, | | | potentially overwhelming the destination's NIC | | | receive buffer and the switch's egress port | | | buffer. Incast is a dominant congestion | | | mechanism in AllReduce and collective | | | operations. | +-----------+-------------------------------------------------------+ | *Incast | The ratio of concurrent senders to receivers in | | Ratio* | an incast communication pattern (N:1). The | | | incast ratio determines the oversubscription | | | factor at the destination port and is a primary | Calabria, et al. Expires 7 January 2027 [Page 15] Internet-Draft AI Fabric Benchmarking Terminology July 2026 | | test parameter for congestion characterization. | +-----------+-------------------------------------------------------+ | *Packet | A load balancing strategy distributing | | Spray* | individual packets of a single RDMA message | | | across all available ECMP paths, maximizing | | | link utilization at the cost of potential out- | | | of-order delivery at the receiver. Native in | | | UET (RUD mode); requires NIC reorder buffering | | | for RoCEv2 RC mode. | +-----------+-------------------------------------------------------+ | *DLB / | Dynamic Load Balancing using flowlet detection. | | Flowlet* | A per-flow rerouting mechanism that reassigns a | | | flow to a new ECMP path when the flow has been | | | idle longer than the flowlet gap threshold | | | (typically 500 ns–2 µs), reducing out-of-order | | | packet risk compared to packet spray while | | | improving utilization over static per-flow | | | ECMP. | +-----------+-------------------------------------------------------+ | *ECMP* | Equal-Cost Multi-Path routing. A forwarding | | | mechanism distributing traffic across multiple | | | equal-cost paths, typically via hash of the IP | | | 5-tuple (or entropy field in UET). ECMP | | | imbalance (MMR > 1.0) is a primary fabric | | | efficiency metric for AI traffic. | +-----------+-------------------------------------------------------+ | *MMR* | Max-Mean Ratio. The ratio of the flow count | | | (or traffic load) on the most heavily utilized | | | link to the average flow count per link across | | | all fabric links. MMR = 1.0 indicates perfect | | | ECMP balance; MMR > 1.0 quantifies imbalance | | | that degrades effective fabric bandwidth. | +-----------+-------------------------------------------------------+ Table 7: Congestion Control and Fabric Behavior Terms 6.1. Load Balancing Strategy Comparison +============+=============+============+=============+============+ | Strategy | Granularity | Reorder | Utilization | Complexity | | | | Risk | | | +============+=============+============+=============+============+ | *ECMP | Per-flow | None | Low | Low | | (5-tuple | | | (elephant | | | hash)* | | | flow bias) | | +------------+-------------+------------+-------------+------------+ | *DLB / | Per-flowlet | Low | Medium | Medium | | Flowlet* | | | | | Calabria, et al. Expires 7 January 2027 [Page 16] Internet-Draft AI Fabric Benchmarking Terminology July 2026 +------------+-------------+------------+-------------+------------+ | *Packet | Per-packet | High | High | High (NIC | | Spray | | | | reorder | | (RoCEv2)* | | | | buffer) | +------------+-------------+------------+-------------+------------+ | *Packet | Per-packet | None | High | Low | | Spray (UET | | (transport | | | | RUD)* | | tolerates | | | | | | OOO) | | | +------------+-------------+------------+-------------+------------+ Table 8: Load Balancing Strategy Comparison 7. Fabric Topology and Infrastructure Terms The following terms define fabric topology architectures and infrastructure components referenced in the companion methodology documents. +===================+=============================================+ | Term | Definition | +===================+=============================================+ | *Fabric DUT | The precise measurement boundary for BMWG | | Boundary* | AI fabric benchmarks. Defined as the NIC | | | Ethernet port (transmit side at source, | | | receive side at destination). All | | | benchmarked metrics (throughput, latency, | | | loss, congestion) are measured at or | | | between NIC Ethernet ports. Intra-node | | | segments (NVLink, PCIe Gen4/5, CXL) are | | | outside the DUT boundary and MUST NOT be | | | included in fabric benchmark results | | | without explicit labelling as a separate | | | measurement component. | +-------------------+---------------------------------------------+ | *Intra-Node | The latency and bandwidth consumed by data | | Transfer | movement within a single server node: | | Overhead* | specifically, the GPU-to-NIC path via PCIe | | | or CXL, and GPU-to-GPU communication via | | | NVLink. Intra-node transfer overhead is a | | | contextual measurement reported alongside | | | fabric benchmarks in end-to-end | | | decomposition tests but is not itself the | | | benchmarked entity in any test in this | | | document | +-------------------+---------------------------------------------+ | *Clos / Fat-Tree | A multi-stage switch topology providing | | Topology* | non-blocking or oversubscribed connectivity | Calabria, et al. Expires 7 January 2027 [Page 17] Internet-Draft AI Fabric Benchmarking Terminology July 2026 | | between all leaf-to-leaf pairs. In AI | | | fabric deployments, a two-tier (leaf-spine) | | | or three-tier (leaf-spine-superspine) Clos | | | is standard. Full bisection bandwidth | | | (1:1) is the target for training fabrics; | | | 2:1 or 4:1 oversubscription may be | | | acceptable for inference fabrics. | +-------------------+---------------------------------------------+ | *Rail-Optimized | A topology in which the NIC ports of each | | Topology* | server are distributed across multiple ToR | | | switches (one NIC port per switch), such | | | that collective traffic between adjacent | | | servers traverses different physical paths. | | | Minimizes switch-to-switch traffic during | | | ring AllReduce, maximizing effective BusBW. | | | Requires ECMP-aware collective placement. | +-------------------+---------------------------------------------+ | *Bisection | The aggregate bandwidth across the minimum | | Bandwidth* | cut that divides the fabric into two equal | | | halves. Non-blocking fabrics provide | | | bisection bandwidth equal to half the total | | | edge (server-facing) bandwidth. Limits | | | worst-case all-to-all communication | | | throughput. | +-------------------+---------------------------------------------+ | *Oversubscription | The ratio of total edge (server-facing) | | Ratio* | bandwidth to total bisection bandwidth in a | | | Clos fabric. A 1:1 ratio is non-blocking; | | | higher ratios (e.g., 2:1, 4:1) reduce | | | fabric cost but may bottleneck all-to-all | | | and AllReduce patterns when all server | | | ports are active simultaneously. | +-------------------+---------------------------------------------+ | *ToR Switch* | Top-of-Rack switch. The first-hop | | | aggregation switch connecting accelerator | | | servers in a rack to the spine layer of the | | | fabric. In rail-optimized topologies, | | | multiple ToR switches serve a single rack, | | | with each server's NICs distributed across | | | ToRs. | +-------------------+---------------------------------------------+ | *Spine / | Intermediate and top-layer switches in a | | Superspine* | multi-tier Clos fabric, providing inter- | | | rack and inter-pod connectivity | | | respectively. Spine switches aggregate | | | multiple ToR switches; superspine switches | | | aggregate multiple spine pods. | +-------------------+---------------------------------------------+ Calabria, et al. Expires 7 January 2027 [Page 18] Internet-Draft AI Fabric Benchmarking Terminology July 2026 | *NIC* | Network Interface Controller. The hardware | | | device providing network connectivity for | | | an accelerator host. AI fabric NICs | | | support RDMA (RoCEv2 or UET), hardware | | | offload for collective operations, and, | | | optionally, GPU-Initiated Networking (GIN). | | | NIC model and firmware version *MUST* be | | | documented in all benchmark reports. | +-------------------+---------------------------------------------+ | *Buffer | The instantaneous or time-averaged fill | | Occupancy* | level of a switch port's packet buffer, | | | expressed in bytes or as a fraction of | | | total buffer capacity. Elevated sustained | | | buffer occupancy indicates congestion. P99 | | | buffer occupancy is a Fabric Health | | | Indicator in the companion methodology | | | documents. | +-------------------+---------------------------------------------+ | *Zero-Impact | A failover event during which no | | Failover* | statistically significant increase in JCT | | | or TTFT is observed, within the measurement | | | tolerance specified by the companion | | | methodology. The term denotes the measured | | | outcome, not a specific mechanism. | | | | | | NOTE: This outcome is typically achieved | | | via pre-programmed alternate paths and | | | hardware-level fast reroute (FRR) with sub- | | | microsecond detection, rather than routing- | | | protocol convergence. The mechanism is | | | informative and not part of the definition. | +-------------------+---------------------------------------------+ | *Link | The fraction of the nominal link capacity | | Utilization* | actually used for data transmission over a | | | measurement interval, expressed as a | | | percentage. Reported as mean, P95, and P99 | | | per link. High asymmetric link utilization | | | (low average but high peak) is | | | characteristic of bursty AI inference | | | traffic. | +-------------------+---------------------------------------------+ Table 9: Fabric Topology and Infrastructure Terms Calabria, et al. Expires 7 January 2027 [Page 19] Internet-Draft AI Fabric Benchmarking Terminology July 2026 8. Training-Specific Terms The following terms are specific to AI training workload benchmarking and are used normatively in [I-D.calabria-bmwg-ai-fabric-training-bench]. +==================+================================================+ | Term | Definition | +==================+================================================+ | *JCT* | Job Completion Time. The wall-clock | | | elapsed time from the start of a training | | | job (or benchmark iteration) until all | | | participating accelerators complete their | | | work, inclusive of all forward pass, | | | backward pass, and collective communication | | | phases. JCT is the primary end-to-end | | | training efficiency KPI. | +------------------+------------------------------------------------+ | *Roofline JCT* | The theoretical minimum JCT under ideal | | | network conditions, namely: load balancing | | | across all paths, zero contention and | | | queuing, no retransmissions, and no fabric | | | failures. Computed as Roofline JCT = | | | computation_time + serialization_delay, | | | where serialization_delay = (8 × S × | | | algo_factor) / B_acc, with S = message size | | | in bytes, algo_factor = the fixed per- | | | collective normalization constant from the | | | BusBW definition, and B_acc = aggregate | | | per-accelerator NIC line rate in bits/ | | | second; the factor 8 converts bytes to | | | bits. Stating these assumptions explicitly | | | ensures the reference is reproducible | | | across implementations. Provides a | | | baseline for evaluating fabric overhead. | +------------------+------------------------------------------------+ | *JCT Ratio* | The ratio of measured JCT to Roofline JCT. | | | A value of 1.0 indicates no network-induced | | | overhead. Values > 1.0 quantify fabric | | | inefficiency: JCT Ratio = JCT_measured / | | | JCT_roofline. The JCT Ratio is the primary | | | comparative metric for AI training fabric | | | benchmarking. | +------------------+------------------------------------------------+ | *Gradient | The AllReduce collective operation | | Synchronization* | performed after the backward pass of each | | | training step to sum the locally computed | | | gradients across all data-parallel | Calabria, et al. Expires 7 January 2027 [Page 20] Internet-Draft AI Fabric Benchmarking Terminology July 2026 | | replicas. The dominant communication event | | | in data-parallel training, occurring once | | | per training step per layer. | +------------------+------------------------------------------------+ | *Step Time* | The wall-clock duration of a single | | | training iteration (forward pass + backward | | | pass + gradient synchronization + optimizer | | | step). Step time = computation time + | | | communication time, where the communication | | | time is dominated by the AllReduce | | | collective. | +------------------+------------------------------------------------+ | *Soak Test* | A sustained-load test run for an extended | | | period (minimum 24 hours for stability | | | evaluation) at a defined offered load | | | fraction (e.g., 70% or 90% of maximum | | | throughput). Soak tests detect buffer | | | leaks, ECMP imbalance drift, PFC storm | | | initiation, and long-tail error | | | accumulation not visible in short-duration | | | tests. | +------------------+------------------------------------------------+ Table 10: Training-Specific Terms 9. Inference-Specific Terms The following terms are specific to AI inference serving workload benchmarking and are used normatively in [I-D.calabria-bmwg-ai-fabric-inference-bench]. +==================+================================================+ | Term | Definition | +==================+================================================+ | *TTFT* | Time to First Token. The elapsed time from | | | receipt of an inference request by the | | | serving system to emission of the first | | | output token. Encompasses prompt | | | processing (prefill), KV cache generation, | | | optional KV cache transfer (in | | | disaggregated architectures), and the | | | initial decode step. Interactive serving | | | deployments typically target TTFT < 500 ms | | | at P99 (informative; not a requirement of | | | this document). | +------------------+------------------------------------------------+ | *ITL* | Inter-Token Latency. The elapsed time | | | between successive output tokens during the | Calabria, et al. Expires 7 January 2027 [Page 21] Internet-Draft AI Fabric Benchmarking Terminology July 2026 | | autoregressive decode phase. Measured at | | | P50, P95, P99, and P99.9 to characterize | | | tail latency behavior. Interactive serving | | | deployments typically target ITL < 50 ms at | | | P99 (informative; not a requirement of this | | | document). | +------------------+------------------------------------------------+ | *TPS* | Tokens Per Second. Aggregate throughput of | | | the inference serving system, measured as | | | the total number of output tokens generated | | | per second across all concurrent requests. | | | Reported separately for input-side | | | (prefill) TPS and output-side (decode) TPS. | +------------------+------------------------------------------------+ | *KV Cache* | Key-Value Cache. The intermediate | | | attention state (key and value projection | | | matrices from multi-head attention layers) | | | computed during the prefill phase and | | | reused during each decode step to avoid | | | redundant recomputation. KV cache size | | | scales with: layers × KV_attention_heads | | | (H_kv) × head_dim × sequence_length × | | | precision. Under GQA/MQA the number of KV | | | heads (H_kv) differs from the total number | | | of attention heads (see the S_KV | | | definition). The attention head | | | configuration *MUST* be reported in all | | | benchmark results. | +------------------+------------------------------------------------+ | *Prefill Phase* | The compute-bound phase of LLM 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 accelerator utilization | | | (90–95%), and large activation tensors. | | | Prefill latency dominates TTFT for long | | | prompts. | +------------------+------------------------------------------------+ | *Decode Phase* | The memory-bandwidth-bound phase of LLM | | | inference in which output tokens are | | | generated autoregressively, one token per | | | forward pass, by reading the KV cache. | | | Characterized by low arithmetic intensity | | | (60–80 ops/byte), lower accelerator | | | utilization (20–40%), and memory-bandwidth- | | | limited KV cache reads. Decode throughput | | | limits TPS. | Calabria, et al. Expires 7 January 2027 [Page 22] Internet-Draft AI Fabric Benchmarking Terminology July 2026 +------------------+------------------------------------------------+ | *Disaggregated | An inference serving architecture in which | | Serving* | the prefill phase and decode phase are | | | executed on physically separate groups of | | | accelerators (workers), connected by a | | | network fabric. Allows independent scaling | | | of prefill and decode resources (xPyD) but | | | introduces KV cache transfer as a fabric- | | | critical data movement. | +------------------+------------------------------------------------+ | *xPyD Ratio* | The allocation ratio of x prefill workers | | | to y decode workers in a disaggregated | | | serving cluster. Example: 3P9D denotes 3 | | | prefill nodes and 9 decode nodes. The | | | optimal xPyD ratio depends on model size, | | | prompt/output length distributions, and | | | TTFT/ITL SLO targets. | +------------------+------------------------------------------------+ | *Continuous | A dynamic inference scheduling technique | | Batching* | that inserts new requests into an active | | | decode batch as slots become available | | | (without waiting for the current batch to | | | complete), improving accelerator | | | utilization compared to static batching. | | | Generates variable batch sizes that affect | | | fabric traffic burstiness. | +------------------+------------------------------------------------+ | *PagedAttention* | A KV cache memory management technique | | | storing attention keys and values in fixed- | | | size, non-contiguous virtual pages | | | (typically 16–64 KB), inspired by OS | | | virtual memory management. Reduces memory | | | fragmentation and enables efficient KV | | | cache sharing across requests with common | | | prefixes. | +------------------+------------------------------------------------+ | *Prefix Caching* | Reuse of previously computed KV cache | | | segments for inference requests sharing a | | | common prompt prefix (e.g., a fixed system | | | prompt), eliminating redundant prefill | | | computation. Prefix cache hit rate is a | | | secondary KPI for inference serving | | | efficiency. | +------------------+------------------------------------------------+ | *Normal | An AllToAll MoE dispatch communication mode | | Dispatch* | optimized for the prefill phase. Payload | | | sizes are variable (depending on token-to- | | | expert routing), generating dynamic tensor | Calabria, et al. Expires 7 January 2027 [Page 23] Internet-Draft AI Fabric Benchmarking Terminology July 2026 | | shapes incompatible with static graph | | | capture. Maximizes throughput for large | | | batches at the cost of higher per-dispatch | | | latency. | +------------------+------------------------------------------------+ | *Low-Latency | An AllToAll MoE dispatch communication mode | | Dispatch* | optimized for the decode phase. Payload | | | sizes are padded to fixed maximum | | | dimensions (compatible with static graph | | | capture), enabling lower kernel-launch | | | overhead at the cost of slight bandwidth | | | inefficiency. Target: < 200 µs per | | | dispatch round trip. | +------------------+------------------------------------------------+ | *Expert Choice | A token routing strategy in which experts | | Routing* | select which tokens to process, rather than | | | tokens selecting experts. Each expert | | | accepts its top-C tokens by affinity score, | | | producing perfect load balance but non- | | | uniform AllToAll message sizes across EP | | | ranks. | +------------------+------------------------------------------------+ | *Auxiliary Loss | A top-k routing variant that adds a load- | | Top-k* | balancing auxiliary loss during training to | | | encourage uniform token distribution across | | | experts. Produces near-uniform AllToAll | | | traffic in inference and reduces hot-spot | | | risk on the fabric. | +------------------+------------------------------------------------+ | *Top-k with | A top-k routing variant in which tokens | | Token Drop* | destined for overloaded experts are dropped | | | or redirected to a fallback. Reduces | | | worst-case dispatch traffic volume at the | | | cost of model output quality under load. | +------------------+------------------------------------------------+ | *T_dispatch* | The dispatch payload per accelerator per | | | MoE layer, computed as: 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 = bytes | | | per element (BF16=2, FP8=1), N = EP group | | | size. Used as the canonical traffic volume | | | parameter in the MoE test matrix (see | | | Section 7.1 of the companion inference | | | benchmarking draft). | +------------------+------------------------------------------------+ | *SLO* | Service Level Objective. A quantitative | | | target for an inference serving KPI. AI | Calabria, et al. Expires 7 January 2027 [Page 24] Internet-Draft AI Fabric Benchmarking Terminology July 2026 | | inference SLOs typically specify maximum | | | TTFT (e.g., < 500 ms P99) and maximum ITL | | | (e.g., < 50 ms P99) under a specified | | | request arrival rate. | +------------------+------------------------------------------------+ | *Speculative | An inference acceleration technique using a | | Decoding* | small draft model to generate candidate | | | token sequences verified in parallel by the | | | target model. Reduces effective ITL but | | | generates bursty, variable-length KV cache | | | traffic; noted as a future benchmarking | | | area not fully specified in the current | | | companion documents. | +------------------+------------------------------------------------+ | *S_KV* | 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; H_kv = number | | | of KV attention heads per layer (H_kv <= | | | H_total for GQA/MQA); D = per-head key/ | | | value dimension (head_dim), typically | | | model_dim / H_total; C = context length in | | | tokens (prompt + generated tokens); P_bytes | | | = precision in bytes per element (FP16/BF16 | | | = 2, FP8/INT8 = 1); Factor 2 accounts for | | | both K and V tensors, each of shape [H_kv, | | | D] per layer per token. | +------------------+------------------------------------------------+ Table 11: Inference-Specific Terms See Section 7.1 of [I-D.calabria-bmwg-ai-fabric-inference-bench] for the MoE test matrix referenced by T_dispatch above. 9.1. Inference Phase Characteristics +===========+===============+============+=============+=========+ | Phase | Compute Bound | Arithmetic | Accelerator | Primary | | | | Intensity | Util. | KPI | +===========+===============+============+=============+=========+ | *Prefill* | Yes | 200–400 | 90–95% | TTFT | | | | ops/byte | | | +-----------+---------------+------------+-------------+---------+ | *Decode* | No (memory BW | 60–80 ops/ | 20–40% | ITL, | | | bound) | byte | | TPS | +-----------+---------------+------------+-------------+---------+ Calabria, et al. Expires 7 January 2027 [Page 25] Internet-Draft AI Fabric Benchmarking Terminology July 2026 Table 12: Inference Phase Characteristics 10. KPI Classification Terms The following terms define the three-tier KPI taxonomy used across both companion methodology documents. +============+=====================================================+ | Term | Definition | +============+=====================================================+ | *Primary | A top-level performance indicator directly | | KPI* | representing end-user experience or training | | | efficiency. In training: JCT Ratio and BusBW. In | | | inference: TTFT and ITL. Primary KPIs are the | | | principal reporting metric and the basis for | | | comparative benchmarking across DUT | | | implementations. | +------------+-----------------------------------------------------+ | *Secondary | A fabric-level performance indicator providing | | KPI* | mechanistic explanation for primary KPI values. | | | Examples: collective operation throughput (BusBW), | | | KV cache transfer goodput, AllToAll dispatch | | | latency, ECMP imbalance (MMR), and link | | | utilization. Secondary KPIs enable root-cause | | | analysis of Primary KPI deviations. | +------------+-----------------------------------------------------+ | *Fabric | An operational metric characterizing fabric | | Health | stability and anomaly conditions rather than peak | | Indicator | performance. FHIs include: PFC event rate, PFC | | (FHI)* | storm occurrence, ECN marking ratio, packet loss | | | rate, buffer occupancy (P99), and retransmission | | | rate. FHIs *SHOULD* be continuously monitored and | | | reported throughout all test categories. | +------------+-----------------------------------------------------+ | *Goodput* | The application-useful data delivered per unit | | | time, excluding retransmissions, protocol overhead, | | | and padding. Benchmark reports MUST specify the | | | qualified Goodput metric (e.g., Inference_Goodput | | | or Fabric_Goodput) to avoid ambiguity. | | | *Fabric_Goodput:* RDMA message payload bytes | | | successfully delivered per unit time at the DUT | | | boundary, excluding transport headers, framing | | | overhead, padding, and retransmitted bytes. This | | | is the numerator quantity in KV_xfer_bandwidth and | | | EP_alltoall_bandwidth. Units: GB/s or Gbps; | | | reports MUST state which. | | | *Inference_Goodput:* Output tokens successfully | | | delivered per unit time, counting only requests | Calabria, et al. Expires 7 January 2027 [Page 26] Internet-Draft AI Fabric Benchmarking Terminology July 2026 | | that complete without preemption, eviction, or | | | error. Corresponds to TPS_output over successfully | | | completed requests only. Units: tokens/second. | | | The two planes MUST NOT be conflated. | | | KV_xfer_bandwidth measures Fabric_Goodput; it does | | | not measure Inference_Goodput. | +------------+-----------------------------------------------------+ | *Zero | A test acceptance criterion requiring that no | | Packet | packets are dropped by the DUT during the | | Loss* | measurement interval. For RoCEv2 and UET | | | transports, zero packet loss is the target | | | operating condition. The binary search procedure | | | in the companion methodology documents determines | | | the maximum offered load satisfying this criterion. | +------------+-----------------------------------------------------+ Table 13: KPI Classification Terms 10.1. KPI Tier Summary The examples below are illustrative and non-exhaustive; the companion methodology documents may add KPIs at each tier as appropriate to their specific workload focus, provided the tier semantics described above are preserved. +============+=================+==================+=================+ | Tier | Training | Inference | Purpose | | | Examples | Examples | | +============+=================+==================+=================+ | *Primary | JCT Ratio, | TTFT, ITL, TPS | Direct end-user | | KPI* | BusBW | | experience / | | | | | business impact | +------------+-----------------+------------------+-----------------+ | *Secondary | AllReduce | AllToAll | Root cause | | KPI* | BusBW, MMR, | dispatch | analysis of | | | Link | latency, KV | Primary KPI | | | Utilization | transfer goodput | deviations | +------------+-----------------+------------------+-----------------+ | *Fabric | PFC events, | PFC events, ECN | Ongoing fabric | | Health | ECN ratio, | ratio, packet | stability and | | Indicator | packet loss, | loss, buffer P99 | anomaly | | (FHI)* | buffer P99, | | detection | | | retx rate | | | +------------+-----------------+------------------+-----------------+ Table 14: KPI Tier Summary Calabria, et al. Expires 7 January 2027 [Page 27] Internet-Draft AI Fabric Benchmarking Terminology July 2026 11. Referenced Standards Abbreviations The following abbreviations refer to normative and informative IETF documents referenced throughout this document and the companion methodology documents. Expansions for technical acronyms used across the companion documents are listed in the Acronyms appendix (Table 16). Calabria, et al. Expires 7 January 2027 [Page 28] Internet-Draft AI Fabric Benchmarking Terminology July 2026 +===========+=====================================================+ | Reference | Definition | +===========+=====================================================+ | *RFC | "Benchmarking Terminology for Network Interconnect | | 1242* | Devices" (Bradner, 1991). Defines foundational | | | benchmarking terms (throughput, latency, frame loss | | | rate, back-to-back frames). The baseline | | | terminology reference for BMWG work. Where terms | | | in this document overlap with RFC 1242 definitions, | | | this document contextualizes and extends those | | | definitions for AI fabric benchmarking. | +-----------+-----------------------------------------------------+ | *RFC | "Benchmarking Methodology for Network Interconnect | | 2544* | Devices" (Bradner & McQuaid, 1999). Defines test | | | methodologies for throughput, latency, frame loss | | | rate, and back-to-back measurements. The AI fabric | | | methodology documents extend RFC 2544 procedures | | | for AI-specific traffic patterns and test | | | durations. | +-----------+-----------------------------------------------------+ | *RFC | "Data Center Benchmarking Terminology" (Avramov & | | 8238* | Rapp, 2017). Extends RFC 1242 with data-center | | | benchmarking terminology, including latency and | | | jitter definitions, physical-layer calibration, | | | line rate, buffering, microburst, and application | | | throughput. Incast, ECN, and buffer occupancy | | | concepts in this document align with RFC 8238 | | | definitions. | +-----------+-----------------------------------------------------+ | *RFC | "Data Center Benchmarking Methodology" (Avramov & | | 8239* | Rapp, 2017). Defines test methodologies for data | | | center network functions including incast, ECN | | | marking, and lossless behavior. The AI fabric | | | companion methodology documents extend RFC 8239 for | | | distributed AI collective traffic patterns. | +-----------+-----------------------------------------------------+ | *RFC 2119 | "Key words for use in RFCs to Indicate Requirement | | / RFC | Levels" (Bradner, 1997; Leiba, 2017). Define the | | 8174* | normative requirement language: MUST, MUST NOT, | | | REQUIRED, SHALL, SHALL NOT, SHOULD, SHOULD NOT, | | | RECOMMENDED, MAY, and OPTIONAL. RFC 8174 clarifies | | | that these terms are normative only when in | | | uppercase; lowercase uses are not normative. | +-----------+-----------------------------------------------------+ Table 15: Referenced Standards Abbreviations Calabria, et al. Expires 7 January 2027 [Page 29] Internet-Draft AI Fabric Benchmarking Terminology July 2026 12. IANA Considerations This document is a terminology document and has no IANA actions. Note that IANA registration of the UET UDP destination port 4793 referenced in Table 4, specified in the Ultra Ethernet Specification [UEC-1.0], is pending; this document does not request any IANA assignment. 13. Security Considerations This document defines terminology 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 [RFC8238] and apply to any benchmarking activity conducted using the terms defined herein. Benchmarking activities as described in the companion methodology documents are limited to technology characterization of AI fabrics using controlled stimuli in a laboratory environment, with dedicated address space and the constraints specified in those documents. 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 Table 7 (PFC, DCQCN) and in Table 5 (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 or SUT as defined in Table 1. 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. Synthetic inputs SHOULD be used for the inference benchmarks referencing the KV Cache and S_KV terms in Table 11 so that no production prompt content is processed in the test environment. Calabria, et al. Expires 7 January 2027 [Page 30] Internet-Draft AI Fabric Benchmarking Terminology July 2026 Acronyms The following acronyms are used in this document and in the companion methodology documents ([I-D.calabria-bmwg-ai-fabric-training-bench] and [I-D.calabria-bmwg-ai-fabric-inference-bench]). Substantive definitions for protocol- and benchmarking-relevant terms are provided in the body of this document; the table below provides expansions only. Acronyms specific to only one companion methodology document are expanded on first use within that document and are not duplicated here. +=========+=======================================================+ | Acronym | Expansion | +=========+=======================================================+ | AI | Artificial Intelligence | +---------+-------------------------------------------------------+ | AIMD | Additive Increase Multiplicative Decrease | +---------+-------------------------------------------------------+ | API | Application Programming Interface | +---------+-------------------------------------------------------+ | ASIC | Application-Specific Integrated Circuit | +---------+-------------------------------------------------------+ | BGP | Border Gateway Protocol | +---------+-------------------------------------------------------+ | BTH | Base Transport Header | +---------+-------------------------------------------------------+ | BusBW | Bus Bandwidth | +---------+-------------------------------------------------------+ | CBFC | Credit-Based Flow Control | +---------+-------------------------------------------------------+ | CCL | Collective Communication Library | +---------+-------------------------------------------------------+ | CDF | Cumulative Distribution Function | +---------+-------------------------------------------------------+ | CMS | Congestion Management Sub-layer (UET) | +---------+-------------------------------------------------------+ | CRC | Cyclic Redundancy Check | +---------+-------------------------------------------------------+ | CV | Coefficient of Variation | +---------+-------------------------------------------------------+ | DCQCN | Data Center Quantized Congestion Notification | +---------+-------------------------------------------------------+ | DLB | Dynamic Load Balancing | +---------+-------------------------------------------------------+ | DMA | Direct Memory Access | +---------+-------------------------------------------------------+ Calabria, et al. Expires 7 January 2027 [Page 31] Internet-Draft AI Fabric Benchmarking Terminology July 2026 | DP | Data Parallelism | +---------+-------------------------------------------------------+ | DSCP | Differentiated Services Code Point | +---------+-------------------------------------------------------+ | DUT | Device Under Test | +---------+-------------------------------------------------------+ | ECMP | Equal-Cost Multi-Path | +---------+-------------------------------------------------------+ | ECN | Explicit Congestion Notification | +---------+-------------------------------------------------------+ | EP | Expert Parallelism | +---------+-------------------------------------------------------+ | FEC | Forward Error Correction | +---------+-------------------------------------------------------+ | FHI | Fabric Health Indicator | +---------+-------------------------------------------------------+ | GIN | GPU-Initiated Networking | +---------+-------------------------------------------------------+ | GQA | Grouped-Query Attention | +---------+-------------------------------------------------------+ | HBM | High Bandwidth Memory | +---------+-------------------------------------------------------+ | HOL | Head-of-Line | +---------+-------------------------------------------------------+ | HPC | High-Performance Computing | +---------+-------------------------------------------------------+ | ICRC | Invariant CRC | +---------+-------------------------------------------------------+ | INT | In-band Network Telemetry | +---------+-------------------------------------------------------+ | ITL | Inter-Token Latency | +---------+-------------------------------------------------------+ | JCT | Job Completion Time | +---------+-------------------------------------------------------+ | JFI | Jain's Fairness Index | +---------+-------------------------------------------------------+ | KPI | Key Performance Indicator | +---------+-------------------------------------------------------+ | KVCXL | KV Cache Transfer Library | +---------+-------------------------------------------------------+ | LLR | Link Layer Retry | +---------+-------------------------------------------------------+ | MAC | Media Access Control | +---------+-------------------------------------------------------+ | ML | Machine Learning | +---------+-------------------------------------------------------+ | MMR | Max-Mean Ratio | +---------+-------------------------------------------------------+ Calabria, et al. Expires 7 January 2027 [Page 32] Internet-Draft AI Fabric Benchmarking Terminology July 2026 | MoE | Mixture of Experts | +---------+-------------------------------------------------------+ | MQA | Multi-Query Attention | +---------+-------------------------------------------------------+ | MTU | Maximum Transmission Unit | +---------+-------------------------------------------------------+ | NIC | Network Interface Controller | +---------+-------------------------------------------------------+ | NOS | Network Operating System | +---------+-------------------------------------------------------+ | OFED | OpenFabrics Enterprise Distribution | +---------+-------------------------------------------------------+ | OOO | Out-of-Order | +---------+-------------------------------------------------------+ | OSPF | Open Shortest Path First | +---------+-------------------------------------------------------+ | PDC | Packet Delivery Context | +---------+-------------------------------------------------------+ | PDS | Packet Delivery Sub-layer (UET) | +---------+-------------------------------------------------------+ | PFC | Priority Flow Control | +---------+-------------------------------------------------------+ | PP | Pipeline Parallelism | +---------+-------------------------------------------------------+ | PRI | Packet Rate Improvement | +---------+-------------------------------------------------------+ | PSN | Packet Sequence Number | +---------+-------------------------------------------------------+ | QP | Queue Pair | +---------+-------------------------------------------------------+ | RDMA | Remote Direct Memory Access | +---------+-------------------------------------------------------+ | RoCEv2 | RDMA over Converged Ethernet version 2 | +---------+-------------------------------------------------------+ | ROD | Reliable Ordered Delivery | +---------+-------------------------------------------------------+ | RTT | Round-Trip Time | +---------+-------------------------------------------------------+ | RUD | Reliable Unordered Delivery | +---------+-------------------------------------------------------+ | RUDI | Reliable Unordered Delivery for Idempotent operations | +---------+-------------------------------------------------------+ | SES | Semantic Sub-layer (UET) | +---------+-------------------------------------------------------+ | SLO | Service Level Objective | +---------+-------------------------------------------------------+ | SUT | System Under Test | +---------+-------------------------------------------------------+ Calabria, et al. Expires 7 January 2027 [Page 33] Internet-Draft AI Fabric Benchmarking Terminology July 2026 | TCAM | Ternary Content-Addressable Memory | +---------+-------------------------------------------------------+ | TP | Tensor Parallelism | +---------+-------------------------------------------------------+ | TPS | Tokens Per Second | +---------+-------------------------------------------------------+ | TSS | Transport Security Sub-layer (UET) | +---------+-------------------------------------------------------+ | TTFT | Time to First Token | +---------+-------------------------------------------------------+ | UEC | Ultra Ethernet Consortium | +---------+-------------------------------------------------------+ | UET | Ultra Ethernet Transport | +---------+-------------------------------------------------------+ | UUD | Unreliable Unordered Delivery | +---------+-------------------------------------------------------+ | VLAN | Virtual LAN | +---------+-------------------------------------------------------+ | VOQ | Virtual Output Queue | +---------+-------------------------------------------------------+ | XPU | accelerator processing unit (generic) | +---------+-------------------------------------------------------+ | xPyD | x Prefill workers : y Decode workers (disaggregated | | | serving ratio) | +---------+-------------------------------------------------------+ Table 16: Acronyms 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 reviews and comments. References Normative References [RFC1242] Bradner, S., "Benchmarking Terminology for Network Interconnection Devices", RFC 1242, DOI 10.17487/RFC1242, July 1991, . [RFC2119] Bradner, S., "Key words for use in RFCs to Indicate Requirement Levels", BCP 14, RFC 2119, DOI 10.17487/RFC2119, March 1997, . Calabria, et al. Expires 7 January 2027 [Page 34] Internet-Draft AI Fabric Benchmarking Terminology July 2026 [RFC2544] Bradner, S. and J. McQuaid, "Benchmarking Methodology for Network Interconnect Devices", RFC 2544, DOI 10.17487/RFC2544, March 1999, . [RFC3168] Ramakrishnan, K., Floyd, S., and D. Black, "The Addition of Explicit Congestion Notification (ECN) to IP", RFC 3168, DOI 10.17487/RFC3168, September 2001, . [RFC8174] Leiba, B., "Ambiguity of Uppercase vs Lowercase in RFC 2119 Key Words", BCP 14, RFC 8174, DOI 10.17487/RFC8174, May 2017, . [RFC8238] Avramov, L. and J. Rapp, "Data Center Benchmarking Terminology", RFC 8238, DOI 10.17487/RFC8238, August 2017, . [RFC8239] Avramov, L. and J. Rapp, "Data Center Benchmarking Methodology", RFC 8239, DOI 10.17487/RFC8239, August 2017, . [UEC-1.0] Ultra Ethernet Consortium, "Ultra Ethernet Transport (UET) Specification 1.0", June 2025, . Informative References [I-D.calabria-bmwg-ai-fabric-inference-bench] Calabria, F., Pignataro, C., Wu, Q., Fioccola, G., and S. Reddy, "Benchmarking Methodology for AI Inference Serving Network Fabrics", Work in Progress, Internet-Draft, draft- calabria-bmwg-ai-fabric-inference-bench-02, 4 June 2026, . [I-D.calabria-bmwg-ai-fabric-training-bench] Calabria, F., Pignataro, C., Wu, Q., Fioccola, G., and S. Reddy, "Benchmarking Methodology for AI Training Network Fabrics", Work in Progress, Internet-Draft, draft- calabria-bmwg-ai-fabric-training-bench-02, 4 June 2026, . [IBTA-ROCE] InfiniBand Trade Association, "InfiniBand Architecture Specification Volume 1, Annex A17: RoCEv2", September 2014, . Calabria, et al. Expires 7 January 2027 [Page 35] Internet-Draft AI Fabric Benchmarking Terminology July 2026 [Jain1984] Jain, R., Chiu, D., and W. Hawe, "A Quantitative Measure of Fairness and Discrimination for Resource Allocation in Shared Computer Systems", DEC Technical Report TR-301, September 1984, . Appendix A: Term Cross-Reference to Companion Documents The following table identifies which terms from this document are used in each companion methodology document. +====================+====================+=======================+ | Term Category | Used in Training | Used in Inference | | | Bench | Bench | +====================+====================+=======================+ | General | All terms | All terms | | Benchmarking Terms | | | | (§2) | | | +--------------------+--------------------+-----------------------+ | Collective | AllReduce, | AllToAll, BusBW | | Communication (§3) | AllGather, | | | | AllToAll, BusBW, | | | | CCL | | +--------------------+--------------------+-----------------------+ | Parallelism | DP, TP, PP, EP, | EP, MoE, DP Attention | | Strategies (§4) | MoE, ZeRO | | +--------------------+--------------------+-----------------------+ | RDMA / RoCEv2 | RDMA, RoCEv2, QP, | RDMA, RoCEv2, QP, RC | | (§5.1) | RC mode, RDMA Verb | mode | +--------------------+--------------------+-----------------------+ | UET Terms (§5.2) | UET, PDC, ROD, | UET, RUD, GIN | | | RUD, RUDI, UUD, | | | | LLR, Packet | | | | Trimming, PRI, | | | | CBFC, UEC Profile, | | | | Entropy Value | | +--------------------+--------------------+-----------------------+ | Congestion Control | PFC, PFC Storm, | PFC, ECN, DCQCN, | | (§6) | PFC Deadlock, ECN, | Incast, Packet Spray, | | | DCQCN, ECN Marking | ECMP | | | Ratio, Incast, | | | | Incast Ratio, | | | | Packet Spray, DLB/ | | | | Flowlet, ECMP, MMR | | +--------------------+--------------------+-----------------------+ | Fabric Topology | Clos, Rail- | Clos, Bisection BW, | | (§7) | Optimized, | ToR, NIC, Buffer | | | Bisection BW, | Occupancy, Link | Calabria, et al. Expires 7 January 2027 [Page 36] Internet-Draft AI Fabric Benchmarking Terminology July 2026 | | Oversubscription, | Utilization | | | ToR, Spine, NIC, | | | | Buffer Occupancy, | | | | Zero-Impact | | | | Failover, Link | | | | Utilization | | +--------------------+--------------------+-----------------------+ | Training-Specific | JCT, Roofline JCT, | Soak Test | | (§8) | JCT Ratio, | | | | Gradient Sync, | | | | Step Time, Soak | | | | Test | | +--------------------+--------------------+-----------------------+ | Inference-Specific | — | TTFT, ITL, TPS, KV | | (§9) | | Cache, Prefill, | | | | Decode, Disaggregated | | | | Serving, xPyD, | | | | Continuous Batching, | | | | PagedAttention, | | | | Prefix Caching, | | | | Normal/Low-Latency | | | | Dispatch, SLO | +--------------------+--------------------+-----------------------+ | KPI Classification | Primary KPI (JCT | Primary KPI (TTFT, | | (§10) | Ratio, BusBW), | ITL), Secondary KPI, | | | Secondary KPI, | FHI, Goodput, Zero | | | FHI, Goodput, Zero | Packet Loss | | | Packet Loss | | +--------------------+--------------------+-----------------------+ Table 17: Term Cross-Reference to Companion Documents Appendix B: Term Taxonomy Summary The following table provides a concise summary of all defined terms organized by category, with the section reference for the full definition. +=========+====================================+====================+ | Section | Term(s) | Category | +=========+====================================+====================+ | 2 | DUT, SUT, RT, JFI, Offered Load, | General | | | Trial Duration, Warmup Period, | Benchmarking | | | Binary Search, Percentile | | | | Latency, AI Fabric | | +---------+------------------------------------+--------------------+ | 3 | Collective Operation, AllReduce, | Collective | | | AllGather, ReduceScatter, | Communication | Calabria, et al. Expires 7 January 2027 [Page 37] Internet-Draft AI Fabric Benchmarking Terminology July 2026 | | AllToAll, Ring Algorithm, BusBW, | | | | CCL, SPMD, BSP | | +---------+------------------------------------+--------------------+ | 4 | Data Parallelism, Tensor | Parallelism | | | Parallelism, Pipeline | Strategies | | | Parallelism, Expert Parallelism, | | | | MoE, DP Attention, ZeRO | | +---------+------------------------------------+--------------------+ | 5.1 | RDMA, RoCEv2, QP, Reliable | Transport — RDMA / | | | Connected (RC), RDMA Verb, UET, | RoCEv2 | | | PDC, ROD | | +---------+------------------------------------+--------------------+ | 5.2 | RUD, RUDI, UUD, UEC Profile, | Transport — UET | | | LLR, Packet Trimming, PRI, CBFC, | | | | Entropy Value, GIN, KVCXL | | +---------+------------------------------------+--------------------+ | 6 | PFC, PFC Storm, PFC Deadlock, | Congestion Control | | | ECN, DCQCN, ECN Marking Ratio, | | | | Incast, Incast Ratio, Packet | | | | Spray, DLB/Flowlet, ECMP, MMR | | +---------+------------------------------------+--------------------+ | 7 | Fabric DUT Boundary, Intra-Node | Fabric Topology | | | Transfer Overhead, Clos/Fat- | | | | Tree, Rail-Optimized, Bisection | | | | Bandwidth, Oversubscription | | | | Ratio, ToR Switch, Spine/ | | | | Superspine, NIC, Buffer | | | | Occupancy, Zero-Impact Failover, | | | | Link Utilization | | +---------+------------------------------------+--------------------+ | 8 | JCT, Roofline JCT, JCT Ratio, | Training-Specific | | | Gradient Synchronization, Step | | | | Time, Soak Test | | +---------+------------------------------------+--------------------+ | 9 | TTFT, ITL, TPS, KV Cache, | Inference-Specific | | | Prefill Phase, Decode Phase, | | | | Disaggregated Serving, xPyD | | | | Ratio, Continuous Batching, | | | | PagedAttention, Prefix Caching, | | | | Normal Dispatch, Low-Latency | | | | Dispatch, Expert Choice Routing, | | | | Auxiliary Loss Top-k, Top-k with | | | | Token Drop, T_dispatch, SLO, | | | | Speculative Decoding, S_KV | | +---------+------------------------------------+--------------------+ | 10 | Primary KPI, Secondary KPI, | KPI Classification | | | Fabric Health Indicator, | | | | Goodput, Zero Packet Loss | | Calabria, et al. Expires 7 January 2027 [Page 38] Internet-Draft AI Fabric Benchmarking Terminology July 2026 +---------+------------------------------------+--------------------+ | 11 | RFC 1242, RFC 2544, RFC 8238, | Referenced | | | RFC 8239, RFC 2119/8174 | Standards | +---------+------------------------------------+--------------------+ Table 18: Complete Term Taxonomy Authors' Addresses Fernando Calabria Cisco United States Email: fcalabri@cisco.com Carlos Pignataro Blue Fern Consulting United States Email: carlos@bluefern.consulting Qin Wu Huawei China Email: bill.wu@huawei.com Giuseppe Fioccola Huawei Italy Email: giuseppe.fioccola@huawei.com Sowjanya Reddy Apple United States Email: sowjredd@gmail.com Calabria, et al. Expires 7 January 2027 [Page 39]