2026年AI网络前沿趋势报告：从AI集群到边缘的连接体系 Top Trends in Networking for AI 2026 Connectivity from AI Clusters to the Edge

CloudMarketTrendReport

FUTURIOM

FUTUREOFCLOUDTECH

TopTrendsinNetworkingfor

AI2026:Connectivityfrom

AIClusterstotheEdge

April2026

Sponsoredby:

DRIV≠NETS

NO<IA

ARRCUS

NETWORKDIFFERENT”

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FUTURIOMTopTrendsinNetworkingforAl|2026

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HighlightsandKeyFindings

·NetworkingforAlisunprecedentedinitscomplexity,challengingeventhemosttechnicallyseasonedofenterpriseusers.ButFuturiom'sresearchindicatesthatdespitethechallenges,hyperscalers,largeenterprises,andaltscalersare

aggressivelydevelopingtheirownAlstrategiesandapplications.

●Ethernetremainsthepreferreddatacenternetwork.WhileInfiniBandremainspopular,EthernetintheformofRDMAoverConvergedEthernetversion2

(RoCEv2)istheincreasinglypreferredapproachforhyperscalerfabrics.

·UltraEthernetremainsanemergingstandard.TheUltraEthernetConsortium(UEC)releasedits1.0specificationinJune2025.Theindustrysupportsthe

technologyasalong-termreplacementforInfiniBand.

●AddingtothedifficultiesofnetworkingforAlarethedistinctrequirementsoftrainingversusinference.Trainingandinferencesharesimilaritiesbutdivergeinkeyaspectsofnetworkdesign,leadingtoaslewofnewproductsandservices.

●Alfornetworkingisanestablishedapproach.AddingAlOpsandintelligentagentstonetworkshasbecomeasellingpointforenterprisesolutions.

●ThemarketisexpandingtoboostAluse.ProductsareemergingtohelpenterpriseswithnetworkingforAlinferenceprojects.

●NetworkingforAlisincreasinglyfocusedonopticaltechnology.Facedwithpowerandspaceconstraints,researchersandvendorsareinnovatingwithoptical

technologiesinallareasofnetworkingforAl.

●ThechallengesofnetworkingforAlareforcingenterprisecustomerstolook

outward.Hyperscalers,neoclouds,andaltscalersarebenefitingfromdemandforAlhelp.

●Thenetworkedgeiskeytoinferencedevelopment.ServicesandsolutionsthataddresstheneedtodeployAlatthenetworkedgeareontherise.

●Telcosareplayingarole.Servicestohandleinferenceatthenetworkedge,fueledbyAl-RAN,arepoppingupfromtelcosworldwide.

·Someofthecompanieshighlightedinthisreport:Alkira,Arista,Arrcus,Aryaka

Networks,AsteraLabs,AvizNetworks,AWS,Broadcom,Ciena,Charter

Communications,Cisco,Cloudflare,Comcast,CoreWeave,Crusoe,Dell

Technologies,DriveNets,Equinix,Eridu,Fastly,Fluidstack,Google,HPE,LambdaLabs,Meta,Microsoft,Napatech,Nebius,Netris,Nokia,Nscale,NVIDIA,Resolight,Runpod,TensorWave,T-Mobile,TogetherAI,Vultr,WhiteFiber,ZEDEDA

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CloudMarketTrendReport2

DRIVZNETS

DriveNetsAlFabric-highest-performance

EthernetalternativetoInfiniBand

DriveNetsAlFabric

DriveNetsNetworkOperatingSystem(DNOS)

ScaleUpScaleOutScaleAcrossFront-end/storage

Full-stack,high-performancenetworkingsolutionforanyGPU,NIC,andOptics

Alnetworkingsolutionforscale-up,scale-out,scale-across,front-end,&storagenetworks

DeployedbyNeoClouds,foundationmodelproviders,Hyperscalers,andEnterprises

Highestperformancemulti-tenancy-isolatingtrafficflows&eliminatingnoisyneighborissues

Highest-performancemulti-sitescale-acrossconnectivityforwidelydistributedGPUclusters

FastestAlfabricdeploymentwithminimalfine-tuning

ARRCUS

AINF

InferenceDeliveryNetwork

ArrcusInferenceNetworkFabric

Inference,Delivered

Faster

time-to-first-token

Morethroughput

perinference

request

Lower

costperinference

IntegrateswithvLLM|SGLang|Triton|NVIDIADynamo|BlueField3|Spectrum4

Network

Different”"

NO<IA

DatacenternetworksfortheAlera

·ReliabledatacenterswitchingbuiltforAl

·Highperformanceinterconnectivity

·Al-enhancednetworkautomation

Learnmore

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TableofContents

1.Introduction:AISpawnsNetworkComplexity4

2.NetworkingforAl:BasicParameters6

Trainingvs.Inference6

TheAscendanceofEthernet7

OtherEthernetEfforts10

TheResourcesQuestion11

Al-AssistedNetworking12

3.VendorSupportforToday'sAlNetworks13

SolutionsfromIncumbentNetworkingVendors13

SolutionsfromAlternativeProvidersandStartups16

4.TheOpticalAngle18

5.ServicesandEdgeSolutions20

SD-WAN,SASE,andNaaSAdaptforAl22

CDNsOfferEdgeConnectivityServices23

OrchestrationattheEdge23

TelcosAddressEdgeServices24

OtherInterconnectionServices25

6.Conclusion25

Appendix:CompaniestoWatch27

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CloudMarketTrendReport4

1.Introduction:AISpawnsNetworkComplexity

Thedemandsofartificialintelligencearerevolutionizingeveryaspectofthedatacenter

environment.Yetarguably,nopartofAlinfrastructureisaschallengingasnetworking.Thestreamlinedthroughput,distribution,andintelligencerequiredforAlmakethenetworkingtechniquesofjustacoupleofyearsbackoutmoded.

Indeed,networkingforAlcallsfornothingshortofarevolutionaryshiftformostenterprise

customers.Whilethefundamentalelementsofnetworking,compute,andstorageremainfromcloudandclient/serverenvironments,inAlnetworkstheyarearrangeddifferently,connecteddifferently,andusenewprotocolsandobservabilitytools.

Today'sAlnetworksareworksinprogress.InfiniBand,theHPCnetworkingtechnologyprofferedbyNVIDIA,persistsinlargedatacenters.ButdemandforEthernet,thelongstandingprotocolfor

datacenterconnectivity,continuestobuild.AlterationstoEthernetbyhyperscalersandvendorsworkingwithintheUltraEthernetConsortium(UEC)andothergroupsshowconsiderablepromiseincorrectingthecongestionandlatencysensitivitiesofnetworksforAI.Still,mostspecsremaininevolutionarymode.AndthereisnoassurancethatsomesignificantproprietaryEthernet

innovationswillfindtheirwayintostandards.

Meanwhile,enterprisesstrugglealong,sometryingtofitAlworkloadsontoexistingnetworks,withdiscouragingresults.ComplicatingthesituationarethecontrastingrequirementsofAl,dependingonwhethertheworkcallsfortrainingorinference.

Trainingoflargelanguagemodels(LLMs)requiresfast,congestion-controllednetworkingfabricstoorchestratemultipleGPUsrunninginparallel.Networkingforinference,ortheprocessofapplyingthemodeltospecificcontextandqueries,alsorequiresspeedandprecision,butitdiffersin

requirementsforcomputeintensity,streaming,andKVcachemanagement.

VendorsinthenetworkingforAlmarketclaimthatwhilemostoftheirAlnetworkingbusinesscomesfromhyperscalers,neoclouds,andaltscalerstrainingmodelsontheirinfrastructure,

enterpriseslookingtodeployinferencemakeupagrowingsegmentthatisexpectedtoexpandsubstantiallyincomingquarters.

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InFuturiom'sresearch,we'vefoundthatthedifficultiesofAlnetworkinghaven'tdimmedtheambitionsofenterprisesworldwide.Whilemanycompaniesstrugglewiththebasics,othersareengagedbothindevelopingtheirownmodelsanddeployingthemininferencetocreate

applications.Indeed,inoursamples,proprietarymodelsaresecondonlytoChatGPTandarecommonlyusedbytheverticalindustriesmostengagedinAl,includingfinancialservices,

healthcare,retail,andmanufacturing.

PopularAlModels:TheToolsofChoice

Whileenterprisesoftenbuildonproprietaryplatforms,theyfrequently

integratewidelyadoptedAlmodelsforspecificapplications.

FUTURIOM

WeseeChatGPTleading,showcasingits

widespreadadoptionforversatileapplications.

Crucially,'ProprietaryModels'remainhighly

significant,ascompaniesdevelopcustomAlto

meetunique,internalneeds,oftenforsensitive

dataorspecializedtasks.

Theremainingmodels,includingGoogleGemini,

Claude,andLama,demonstratearichdiversity

inthemarket.Thisshowsthatenterprisesare

strategicallychoosingthebest-fitmodelforeach

specificusecase,ensuringarobustandadaptable

Alarchitecture.

Source:FuturiomEnterpriseAlIndex

Inshort,Alisbecominguniversallycrucialtobusinessoperations.AndthetrendisonlygrowingasagenticAltakeshold.Still,networkingtheseAlworkloadschallengeseventhemost

seasonedoftechnicalexperts.AndinenterpriseswheretopmanagementinsistsonafasttracktoAl-enabledbusinessprocessesandstrategies,thelearningcurveisshortandfullofchoices.

Inthisreport,weexaminethosechoices,keepinginviewthedistinctrequirementsof

networkingfortrainingorinference,orboth.Wepinpointthemajorproblemareas,includingpowerconsumption,anddescribehowsolutionsfromvendorsare—oraren't—addressing

them.WeexamineemergingstandardsandtechnologiesforAlnetworks,includingintherealmofopticalnetworking,whereinnovationspresagefuturedirections.Andweexplorehow

servicesfromsourcesincludingtelcosarespurringAlprogressatthenetworkedge.

CoveringtheentirelandscapeofnetworkingforAlisn'tpossibleinonereport.SoFuturiomhasbrokenour2026updateonnetworkingforAlintotwoparts.Thefirst,thesubjectofthis

report,is"ConnectivityfromAIClusterstotheEdge."Wearefocusedonwhatiscommonly

termedscaleoutandscaleacross:theconnectionsbetweenclustersinAldatacenters,aswellastheinterconnectionsbetweendatacenters,andbetweendatacentersandthenetworkedge,whereusersareprofitingfromAlevenastheystrugglewithit.

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2.NetworkingforAl:BasicParameters

NetworkingbetweenAlclusters,acrossmultiplesites,andoutwardtotheedgeispresently

governedbyaseriesofbasiccharacteristics,whichareinastateofrapidadjustmentand

change.Inthissection,welookatthefundamentalsofnetworkingforAlasitissetupinmostenvironmentstoday.

Trainingvs.Inference

Whilethere'smuchtalkinthemediaaboutAlinfrastructure,thetermactuallyhasmultiplemeanings.AposteronRedditillustratedtheprobleminarecentnote:

"Acertainnetworkvendorkeepsinvitingmetowebinarstodiscussnetworkingfordatacenter

A/workloads,buteverythingIveseensofarisjusthighthroughoutswitching[100/400g].For

myorg'sverylimitedMLfootprint,25ghasbeenfineandotherthanloadingthecomputeup

withGPUs,it'sjustanotherserver."

Clearly,noteveryorganizationrequiresthesameinfrastructureforAI.Still,thereisadangerinrunningAlonlegacyclient/servernetworks.Evenseemingly"simple"AlapplicationscanstallorfailunlessthebasictenetsofAlnetworkingareunderstood.

AfundamentaltruthofAlnetworkingisthattrainingandinferencerequiredifferentapproaches.Let'slookatthespecifics.

Training.Asnotedearlier,trainingofLLMsrequiresmassiveamountsofnetworkingexpertiseandequipment,allofitgearedtoorchestratinghigh-bandwidth,east-westcommunicationsamongGPUsprocessingdatainparallel.ThisistherealmofAlnetworkingforpubliccloud

providers,neocloudandaltscalerproviders,andafewverylargeandwealthyenterprises.

AnessentialelementofnetworkingforAltrainingistheneedtomaintainpredictable,

congestion-controlledtrafficflowsbetweenGPUs,ascongestioninonepathstallstheentireworkload.GiventhehighcostofGPUsandassociatedinfrastructure,it'svitaltokeepGPUsbusytomaintainROI.

Anotheraspectoftrainingistheneedforcollectivecommunicationsoperationssuchas

AllReduceandAllGatherthatareimplementedinsoftwarebutcloselyassociatedwiththe

hardwarenetwork.TheseoperationsinstructGPUstoactinconcertwithoneanotherto

assembleanddisassembleshardsofdatawithintrainingworkflows.Again,RDMA,alongwithsuperiorcongestioncontrolathighspeedswithlowlatency,isessentialtosupportclear

channelsbetweenGPUs.

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Intrainingnetworks,thebottleneckisthenetworkitself,afactorthathasledtonetworkingvendors'effortstostreamlineprocessing.Moreonthosesolutionsinthenextsection.

Inference.Incontrastwithtraining,inference,ortheapplicationofatrainedmodeltospecificdata,context,orqueries,alsorequireslowlatencyandhighthroughputbutindifferentareasofthenetwork.Inferencingbeginswithinputfromthefrontendofthenetwork,theenduser,andistaskedwithdeliveringoutputtothatuser.Trafficismoreorientednorth-south,andlowlatencyisapplicabletothecommunicationswiththeuser,notonlybetweenGPUs.

Whilemostinferencetakesplaceinhyperscalernetworkstoday,agrowingamountof

inferenceisalsofocusedontheedgeofthenetwork,wherequeriesfromloTdevices,

smartphones,laptops,cameras,andotherdevicesoriginate.Forthisreason,someinferenceisassociatedwithsmalllanguagemodels(SLMs)andreducedtokenizationtoaccommodatetheperformancelimitationsofthesedevices.

OneoftheproblemswithinferenceisthatitsbottleneckisoftenintheGPU,notthenetwork.Thisisinpartduetotheneedforkeyvaluecaching(KVcaching)ininference,inwhichthe

contextsurroundingaqueryisstoredsoasnottoberepeatedlygeneratedasqueriesevolve.Loadingmodelweights,orthoseelementsinthemodelthathelpdetermineinputandoutput,plusKVcachefromhigh-bandwidthmemory(HBM),intotheGPU'scomputeunits(tensor

cores)oneverytokengenerationsteptakesupmemorybandwidth,leavingtheGPUswaitingfordatatoprocess.

Theprofileofinferencecallsforchangesinnetworking.Atscale,streaming,loadbalancing,andstatefulroutingbecomekeytoperformance.KVcachecanbedisaggregatedandoffloaded

fromGPUHBMtoCPUmemory,dedicatedmemorynodes,orstorageconnectedviahigh-

speedfabric.Likewise,theinferenceworkload—consistingofprefill,orpromptprocessing,anddecode,oroutputgeneration—canbeseparatedontohardwareoptimizedforeachphase,withKVcachetransferredbetweenthemoverafastnetworkinterconnect.

TheAscendanceofEthernet

MostnetworkingforAlbetweenclustersandsitesisbasedonEthernet,thoughwith

proprietaryadjustmentstocontrolcongestionandimprovethroughput.Themajornetworkingvendors,includingAristaNetworks,Cisco,andHPE/Juniper;andhyperscalers,includingAWS,Google,Meta,andMicrosoft,havecreatedtheirowncongestioncontrolandloadbalancingalgorithmstofitRemoteDirectMemoryAccessoverConvergedEthernetv.2(RoCEv2),whichallowsGPUs,overEthernet,tocommunicatedirectlytooneanother'smemorywithoutthe

intermediaryofaCPUinhigh-performancecomputing(HPC)andAlnetworks.

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Toreview:RDMAwasoriginallydevelopedaspartofInfiniBandtoallowdatatransferfromthememoryofoneGPUtoanotherwithouthavingtoinvolvetheCPU.Ethernetsupporters

adaptedittorunoverIPandEthernetviaRoCE.Today,mostnetworkingvendorssupport

RoCEv2,whichaddsavarietyofenhancements,andtheUEChasaddedcongestioncontroltoavoidthetendencyinTCPnetworksforpacketcongestiontohappenwhenmanydatasourcessimultaneouslytransmittoasinglesource.SincemultipleGPUscommunicatetoserversin

parallel,ithasbeenvitaltosolvethispotentialsnarl.

Notably,NVIDIA'sInfiniBand,thatvendor'sownRDMA-compatibleHPCnetworkingprotocol,isnowcompetingforspacewithNVIDIA'sSpectrum-XEthernetplatform,whichhasbeenwidelycredited,alongwithNVIDIA'sNVLinktechnology,withhelpingNVIDIAachieveanastonishing263%year-over-yearincreaseinnetworkingrevenuesforitslatestreportedquarter.

Allofthisraisesthequestionofhowworkisprogressingbymajorvendorsandhyperscalers,includingNVIDIA,toestablishanEthernetspecificationthatincorporatesthebestofthe

industry'sproprietaryEthernetalgorithms.Asnoted,theUltraEthernetConsortium(UEC)publisheditsfirst1.0specificationinJune2025.Thespecoutlinesphysicalandlinklayerdirectives,aswellasanewtransportprotocolcalledUltraEthernetTransport(UET)for

intelligentflowcontrol,alongwithopenAPIsforobservabilityandautomation.

Atthesametime,individualvendorsaddspecificoptimizationstotheirplatforms.Forexample,CiscorecentlyrolledoutanewsetofAlnetworkinginnovationsthataddressthecriticalneedsofthenextphaseofAlbuildoutsandtheagenticera.CiscorecentlyintroducedNexusOneasafullyintegratedstackthatintegratessilicon,systems,optics,software,andasingleoperating

model—allwhiledeliveringasecure,consistentandunifiedoperationalexperience,on-premises,inthecloud,APl-drivenfirstandextendstocloud-nativeoperations.

Still,theUECacknowledgesthere'smuchmoreworktobedone.There'saneedforflexible

mechanismsthatensurethatcongestioncontrolisadaptabletovaryingkindsofAltraffic.Aswenotedabove,trainingandinferenceworkloadscallfordifferinglatency,traffic,andburstingpatterns.Andtherearefinepointsbasedonfront-andback-endtopologies,suchasthe

connectionofvirtualmachinesorKubernetesandtheplacementofstorage.

Uptonow,networkingvendorshavebakedtheirowncongestioncontrolalgorithmsinto

networkinterfacecard(NIC)siliconandswitchfirmware.OnevendormightimplementData

CenterQuantizedCongestionNotification(DCQCN)forRoCEv2traffic,whileNVIDIAusesits

owncongestioncontrolsinSpectrum-X,andhyperscalersdeploytheirownhardware

congestioncontrol.TheUEChasmadea2026priorityofProgrammableCongestion

Management(PCM),whichwillspecifyastandard-languagecongestioncontrolmechanismthatwillworkinanyNICsupportingPCM.

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OtherissuesbeingworkedonbytheUECincludethefollowing:

CongestionSignaling(CSIG).ThisreferstothecapabilityfortelemetryfromEthernettraffictoincludecongestioninformation-amajorbidtoimprovetheobservabilityofAlnetworking,atopicwe'llexamineinmoredepthshortly.

Smallmessageperformance.TheUECisworkingtoimprovehowpacketscommunicatetheirpurposeanddirectionwithinaworkflow.Ethernetwasn'tsetuptohandleAI'sshorter,burstiertrafficpatternsefficiently,makingitsheadersadragonlatency.Reducingtheheaderswill

streamlinetrafficflows.

In-NetworkCollectives(INC).ThisworkinvolvesoffloadingsomeoftheGPU-to-GPU

communicationinherentincollectivecommandssuchasAllReduceandAllGatherontothenetworkswitch.Thisreducesthetotaldatavolumetraversingthenetwork,reduceslatency,andfreesGPUandNICresources.

Storageintegration.TheUECalsoisworkingtoimprovetheconnectionbetweenUltra

EthernetandstorageinAlnetworks,atopicthatwasn'tfullyaddressedinthefirstspec.WithKVcachingandtheunificationofmemoryandstorageinAlnetworks,thisisavitalareato

address.

Security.WhileUEC1.0providessomesecuritythatwaslackinginRoCEv2,including

authenticationandencryption,thespecneedstobefleshedoutinmoredetailtoavoidanylatencythatencryptionadds.TheUECmustalsoaddressmultitenancyconcerns,anissuethatisn'tadequatelyaddressedintheinitialspec.

NosinglevendorcanclaimtohavefullyimplementedtheUEC1.0spec,sincethatremainsaneffortinflux.Despitetheunfinishedwork,however,UltraEthernetremainsontracktobealong-termsolutionforAlnetworkingacrossclustersandsites.TheUECmembershiprosterhasgrowntoincludeeverymajorplayerinnetworkingforAl,includingcomponentproviders.

FollowingarethelogosofUECSteeringMembers:

AMD

HPE

ARISTA

intel.

BROADCOM

XMeta

BullCISCO.

MicrosoftORACLE

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Source:UltraEthernetConsortium

ThelistofUECGeneralMembersincludesthefollowing.BothlistsoffercompellingevidenceofmajormarketplayerssupportingUltraEthernetasitevolves:

CAbabaCoud

cādence

雪冀

回MARVEL

NVIDIA

Ospirent

ARRCUS

CORNELIS

NETWORK5

JUnIPer

MIPS

PReferrks

synOPsyS

ostrope

DLiy

KEYSIGHTu

HBC

PURESTORAGE

VIAVI

WBnSocoe

世纪互联

hilByteoDonceVNET

GoogleCloudHUAWEI

Lawrence

National

Lenovo

Livermore

Laboratory

NO<IA⑨NTT

QualconM

ZTE中兴

Source:UltraEthernetConsortium

OtherEthernetEfforts

TheUECisn'ttheonlygroupfocusedonimprovingEthernetforAlnetworking.TheIEEE,whichgovernsEthernetprotocols,hasbeenworkingonIEEE802.3dj,aspecificationthatexpandsthecapabilityofEthernetlanes,orsignalingchannels,from100Gbit/sto200Gbit/s.Thespec,

whichisn'tfinalized,willallowforfasterdataratesatthesamenumberoflanes,reducing

powerconsumptionandthenumberoftransceiversneededtoreachthemaximumof1.6

Tbit/s,theemergingspecforhigh-speedEthernetnetworks.TheIEEEisreportedlyworkingtofinalize802.3djthisyear,thoughtherearemurmuringsofdelays.

OthereffortsareunderwaytohelpEthernetwithso-calledscale-upnetworkingwithintherack,anissuewe'llcoverinaseparateupcomingreport.WhilesomeUACmemberssayUltra

Ethernetcanbedeployedwithintherackaswellasbeyond,effortssuchasBroadcom'sopen-sourceScale-UpEthernet(SUE)andtheconsortium-drivenUltraAcceleratorLink(UALink)—thelattersupportedbyAMD,AsteraLabs,Cisco,Google,HPE,Intel,Meta,Microsoft,and

others—willchallengeUACaswellasNVIDIA'sNVLink.

TheOpenComputeProject’s(OCP)NetworkingProjectreleaseditsEthernetforScale-Up

Networking(ESUN)1.0specificationonMarch10,2026.BuiltontechnologyfromBroadcom,ESUNoffersatechniqueforlosslessEthernet,smallmessagingforIPheaders,andsupportformulti-hoptopologies.We'llcoverthisinmoredetailinPt.2ofourNetworkingforAlseries,whichwillbefocusedonscale-up.

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It’sunlikelythatanyoftheorganizationscitedabovewillestablishtheultimatespecsforAl-

enabledEthernet.Vendorsappeartobeopentoparticipatinginmorethanoneeffort,knowingthatthemarketwillfavorsolutionsthatencompassasmuchinnovationwithaslittlevendor

lock-inaspossible.Thisisimportant,sincemanynetworkingforAlvendorsclaimtheircustomersareseekingalternativestoNVIDIA'sdominance.

.

Enterpriseswillalsogravitatetosolutionsthatofferopen-standardmethodsandAPlsthatenablecustomization.Inthenextsection,we'llexaminethestateofleadingproductsandservicesandhowwelltheyare—oraren't—movingtowardthesegoals.

Group

Leadingmembers

Totalmembers

Statedpurpose

Status

UltraEthernet

Consortium

(UEC)

AMD,Arista,Broadcom,Bull,Cisco,HPE,Intel,Meta,

Microsoft,Oracle

>100

companies

"DeliveracompletearchitecturethatoptimizesEthernetforhigh

performanceAlandHPCnetworking.exceedingtheperformanceotoday'sspecializedtechnologies.UECspecificallyfocusesonfunctionality,performance,TCO,anddeveloperandend-userfriendliness,while

minimizingchangestoonlythoserequiredandmaintainingEthernetinteroperability."

UltraEthernet1.0specificationwas

deliveredJune2025

UECAlliance

OpenComputeProject,

StorageNetworkingIndustryAssociation(SNIA),

OpenFabricsAlliance(OFA),IEEE802.3

4groups

""ItistheintentionofUECtofullyengagewiththerelevantSDOs(StandardsDevelopmentOrganizations)tomakeUECtechnologyanofficialelementofthebroadlydeployedfamilyofEthernetbasedtechnologiesandproducts."

Recentevents:plugfestfor802.3djinDecember2025;multivendordemo

atOFFC2026;a

TechnologyExplorationForumissetforOctober2026

Ethernet

Alliance

Amphenol,Broadcom,Cisco,

ExFo,Formerica

OptoElectronics,HPE,

Huawei,Intel,Juniper,

Keysight,Marvell,MaxLinear,TEConnectivity,Teledyne

LeCroy.VIAVI

>50

"TheEthernetAllianceisaglobal,non-profit,industryconsortiumofmemberorganizationsthatarededicatedtothecontinuedsuccessandadvancementofEt