為智能賦能-人工智能和數(shù)據(jù)中心能耗分析

2024WhitePaper

PoweringIntelligence

AnalyzingArtificialIntelligenceandDataCenterEnergyConsumption

2|PoweringIntelligenceMay2024

EXECUTIVESUMMARY

KeyMessages

?IntheUnitedStates,poweringdatacenters,providingcleanenergyfor

manufacturing,supportingindustrialonshoring,andelectrifyingtransporta-tionaredrivingrenewedelectricloadgrowth.Clustersofnew,largepoint

loadsaretestingtheabilityofelectriccompaniestokeeppace.

?Datacentersareoneofthefastestgrowingindustriesworldwide.Between

2017and2021,electricityusedbyMeta,Amazon,Microsoft,andGoogle—themainprovidersofcommerciallyavailablecloudcomputinganddigitalservices—morethandoubled.

?Afundamentaluncertaintyinprojectingdatacenterloadgrowthcomesfromthebroademergenceofartificialintelligence(AI)technologiesinbusiness

anddailylife—punctuatedbytheexplosionintopublicconsciousnessofgen-erativeAImodels,suchasOpenAI’sChatGPT,releasedinNovember2022.

WhileAIapplicationsareestimatedtouseonly10%–20%ofdatacenterelectricitytoday,thatpercentageisgrowingrapidly.

?AImodelsaretypicallymuchmoreenergy-intensivethanthedataretrieval,streaming,andcommunicationsapplicationsthatdrovedatacentergrowthoverthepasttwodecades.At2.9watt-hoursperChatGPTrequest,AIque-

riesareestimatedtorequire10xtheelectricityoftraditionalGooglequeries,whichuseabout0.3watt-hourseach;andemerging,computation-intensivecapabilitiessuchasimage,audio,andvideogenerationhavenoprecedent.

?Toprovideanearlyassessmentofpotentialdatacenterloadgrowthatthenationallevel,EPRIhasdevelopedlow,moderate,high,andhighergrowth

scenariosfordatacenterloadsfrom2023to2030.Datacentersgrowto

consume4.6%to9.1%ofU.S.electricitygenerationannuallyby2030versusanestimated4%today.

?Whilethenational-levelgrowthestimatesaresignificant,itisevenmore

strikingtoconsiderthegeographicconcentrationoftheindustryandthe

localchallengesthisgrowthcancreate.Today,fifteenstatesaccountfor80%ofthenationaldatacenterload,withdatacentersestimatedtocompriseaquarterofVirginia’selectricloadin2023.Concentrationofdemandisalso

evidentglobally,withdatacentersprojectedtomakeupalmostone-thirdofIreland’stotalelectricitydemandby2026.

?WiththeshifttocloudcomputingandAI,newdatacentersaregrowingin

size.Itisnotunusualtoseenewcentersbeingbuiltwithcapacitiesfrom100to1000megawatts—roughlyequivalenttotheloadfrom80,000to800,000homes.Connectionleadtimesofonetotwoyears,demandsforhighly

reliablepower,andrequestsforpowerfromnew,non-emittinggenerationsourcescancreatelocalandregionalelectricsupplychallenges.

?EPRIhighlightsthreeessentialstrategiestosupportrapiddatacenterexpan-sion:

1.Datacenterefficiencyimprovementsandincreasedflexibility.

2.Closecoordinationbetweendatacenterdevelopersandelectriccom-

paniesregardingdatacenterpowerneeds,timing,andflexibility,aswellaselectricsuppliesanddeliveryconstraints.

3.Bettermodelingtoolstoplanthe5–10+yeargridinvestmentsneeded

toanticipateandaccommodatedatacentergrowthwithoutnegatively

impactingothercustomersandtoidentifystrategiesformaintaininggridreliabilitywiththeselarge,noveldemands.

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TABLEOFCONTENTS

EXECUTIVESUMMARY 2

KeyMessages 2

PotentialImpactsofArtificialIntelligenceonDataCenterLoadGrowth 4

EPRIU.S.DataCenterLoadProjections 4

DataCenterPowerDemandsAreConcentratedinaFewRegions 5

ARoadmaptoSupportRapidDataCenterExpansion 6

Introduction 7

ResearchQuestions 7

DataCentersintheUnitedStates 7

DataCenters’PrimaryElectricity-ConsumingHardwareandEquipment 9

AIandDataCenterPowerConsumptionInsights 10

ImmenseVolumesofDataareBeingProcessedDaily 10

HistoryofEnergyEfficiencyintheDataCenterIndustry 11

UnevenGeographicDistributionCreatesImbalanceinDataCenterLoad 12

AIImplicationsforPowerConsumption 14

ChatGPTandOtherLargeLanguageModels(LLMs) 15

ForecastingDataCenterLoadGrowthto2030 17

FourScenariosBasedonHistoricalData,ExpertInsights,andCurrentTrends 17

EnergyEfficiency,LoadManagementandCleanElectricitySupply 18

Energy-EfficientTrainingAlgorithms 18

Energy-EfficientHardware 19

Energy-EfficientCoolingTechnologies 19

ScalableCleanEnergyUse 20

MonitoringandAnalytics 20

ReducingDataCenters’EnvironmentalFootprint 21

ActionstoSupportRapidDataCenterExpansion 21

ImproveDataCenterOperationalEfficiencyandFlexibility 22

IncreaseCollaborationthroughaSharedEnergyEconomyModelforSustainableDataCenters 22

BetterAnticipateFuturePointLoadGrowththroughImprovedForecastingandModeling 23

AppendixA:State-SpecificScenarios 24

ProjectedDataCenterLoadScenariosforTop15States 24

RegionalDifferencesinDataCenterCapacitiesbyMetropolitanArea 27

ProjectionsofPotentialPowerConsumptionfor44States 28

AppendixB:InsightsIntotheEnergyUseofAIModels 29

References 31

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PotentialImpactsofArtificialIntelligenceonDataCenterLoadGrowth

Datacenteroperationisoneofthefastestgrowingindus-

triesworldwide.TheInternationalEnergyAgencyrecentlyprojectedthatglobaldatacenterelectricitydemand

willmorethandoubleby2026.IntheUnitedStates,thenationaloutlookcouldresembletheglobaloutlook,butishighlyuncertain.

Onekeyuncertaintythatcouldchangethetrajectoryof

datacenterloadgrowthistheuseofgenerativeAImodels.Bothpublicandcorporateimaginationsweretriggeredby

thereleaseofOpenAI’sChatGPTonNovember30,2022.Ev-idenceabouthowwidelythesetoolswillbeusedandhowmuchtheywillchangecomputationalneedsisjuststartingtoemerge.Theseearlyapplicationswereestimatedtore-

quireabouttentimestheelectricity—from0.3watt-hoursforatraditionalGooglesearchto2.9watt-hoursforaChat-GPTquery—torespondtouserqueries.Creationoforiginalmusic,photos,andvideosbaseduponuserpromptsand

otheremergingAIapplicationscouldrequiremuchmorepower.With5.3billionglobalinternetusers,widespreadadoptionofthesetoolscouldpotentiallyleadtoastep

changeinpowerrequirements.Ontheotherhand,his-

toryhasshownthatdemandforincreasedprocessinghaslargelybeenoffsetbydatacenterefficiencygains.

EPRIU.S.DataCenterLoadProjections

Drawingonpublicinformationaboutexistingdatacenters,publicestimatesofindustrygrowth,andrecentelectricity

demandforecastsbyindustryexperts,EPRIpreparedfour

scenariosofpotentialelectricityconsumptioninU.S.data

centersduringtheperiodfrom2023to2030(FigureES-1).Thebluelineinthefigure,runningfrom2000to2020,

traceshistoricaldatacenterelectricityconsumptionesti-

mates.From2000to2010,datacenterloadgrewascentersexpandedacrossthecountrytosupporttheemerging

internet.From2010to2017,despitecontinuedgrowthincomputingdemandsanddatastoragethisloadgrowthflat-tenedduetoefficiencygainsandthereplacementofsmall,relativelyinefficientcorporatedatacenterswithlarge,

cloudcomputingfacilities.Inrecentyears,loadgrowthhaslikelyaccelerated,drivenbyemergingAIapplicationsandCOVID-eraincreasesindemandforserviceslikestreamingandvideoconferencing.Thelightblueareahighlightsun-certaintyinarangeofdatacenterelectricityconsumptionestimatesfor2021to2023.Coloredbandsshowthefour

projections,whichcombineestimatesofincreaseddata

processingneedswithassumptionsaboutefficiencygains.Thewidthsofthesebandscarryforwardtheuncertaintyaboutthe2023startingloadlevel:

?Lowgrowth—3.7%annualloadgrowthbasedonaStatistaprojectionofdatacenterfinancialgrowthis-suedpriortothereleaseofChatGPT.

ANNUAL

%OF2030ELECTRICIT

Y

LowgrowthModerategro

4.6%50%

Highgrowt

h10%

.

6.8%

Max

Averagehist

oricaldata

Min

20002005201020152020

20252030

SCENARIO

GROWTHRA

TECONSUMPTI

ON

600

500

ElectricityConsumption(TWh/y)

wth5%

3.7%

400

Highergrow

9.1%

th15%

300

200

100

0

FigureES-1.ProjectionsofpotentialelectricityconsumptionbyU.S.datacenters:2023–2030.%of2030electricityconsumptionprojectionsassumethatallother(non-datacenter)loadincreasesat1%annually.

5|PoweringIntelligenceMay2024

?Moderategrowth—5%annualloadgrowthbasedonanexpertassessmentcommissionedbyEPRI.

?Highgrowth—10%annualloadgrowthconsistentwithbothaMcKinseyestimateandanotherexpertassess-

mentcommissionedbyEPRIinsummer2023.

?Highergrowth—15%annualgrowthbasedupona

commissionedexpertassessmentconsistentwithrapidexpansionofAIapplicationsandlimitedefficiency

gains.

Theestimatesofdatacenters’shareoftotalU.S.electricityconsumptionin2030—9.1%,6.8%,5.0%,and4.6%—as-

sumethatallotherloadsincreaseat1%peryear.Data

centersaccountedforabout4%ofthetotalloadin2023(averageestimate).

DataCenterPowerDemandsAreConcentratedinaFewRegions

Fifteenstatesaccountedforanestimated80%ofthe

nationaldatacenterloadin2023.Rankedfromhighesttolowest,theyareVirginia,Texas,California,Illinois,Oregon,Arizona,Iowa,Georgia,Washington,Pennsylvania,New

York,NewJersey,Nebraska,NorthDakota,andNevada.

Concentrationofdemandisalsoevidentglobally,withtheInternationalEnergyAgencyrecentlyprojectingthatdatacentersinIrelandcouldaccountforalmostone-thirdof

Ireland’stotalelectricitydemandby2026.

ThemapinFigureES-2showstheeffectin2030ofapplyingtheannualU.S.datacentergrowthrates(averagedacross

thefourscenarios)toprojectstate-levelloadsagainsta

backdropof1%annualgrowthinotherloads.Withevenlyspreadgrowth,thedatacentershareofloadinVirginiain-creasestoalmost50%inthehighergrowthscenarioandto36%whenaveragedacrossthefourscenarios.Thesharesinotherstatesvarywidelywithfiveotherstatesprojectedtoapproach20%ormoreofelectricitydemandunderthese

simplifiedassumptions.Inreality,loadgrowthisunlikely

tobespreadevenly.Datacentersfavorsiteswhereinter-

netconnectionsarestrong;whereelectricityprices,land

costs,anddisruptiveeventsarelow;whereskilledlaboris

available;nearpopulationcentersandusers;andwherethecenterscandevelopbackuppowertoensurepowersupply(usuallynaturalgasordieselgenerators).Theadditional

l

I

\

/

2030DataCenter%ofStateElectricityConsumption

0–5%5–10%10–15%15–20%20+%

FigureES-2.2030projecteddatacentershareofelectricityconsumption(assumesaverageofthefourgrowthscenariosandthatnon-datacenterloadsgrowat1%annually)[4,8,9]

6|PoweringIntelligenceMay2024

requirementofsomedevelopersfornew,cleanelectricitygenerationsourcesaddstothechallengeofdevelopinganddeliveringthisnewgeneration.

ARoadmaptoSupportRapidDataCenterExpansion

Themostseriouschallengestodatacenterexpansionare

localandregionalandresultfromthescaleofthecenters

themselvesandmismatchesininfrastructuretiming.A

typicalnewdatacenterof100to1000megawattsrepre-

sentsaloadequaltothatofanewneighborhoodof80,000to800,000averagehomes.Whileneighborhoodsrequire

manyyearstoplanandbuild,datacenterscanbedevel-

opedandconnectedtotheinternetinonetotwoyears.

Newtransmission,incontrast,takesfourormoreyearsto

plan,permit,andconstruct.Anddevelopingandconnectingnewgenerationcanalsotakeyears.

EPRIhighlightsthreeessentialstrategiestosupportrapiddatacenterexpansion.Thesestrategiesemphasizein-

creasedcollaborationbetweendatacenterdevelopersandelectriccompanies.

1.Improvedatacenteroperationalefficiencyandflex-

ibility.Althoughgainsindatacenteroperationalef-

ficiencyhaveplateauedinrecentyears,thereareclearopportunitiesforfurtherimprovement,includingmoreefficientIThardware;lowerelectricityuseforcooling,lighting,andsecurity;andmoreefficientAIdevelop-

mentanddeploymentstrategies.Effortstoincreasebothtemporalandspatial(i.e.,spreadingcompute

geographically)flexibilityarecriticaltohelpingaccom-modatethesenewloads.

2.Increasecollaborationbetweendatacenterdevel-

opersandelectriccompanies.Developingadeeper

understandingofdatacenterpowerneeds,timing,andpotentialflexibilities—whileassessinghowtheymatchavailableelectricsuppliesanddeliveryconstraints—cancreateworkablesolutionsforall.Enabledbytechnol-

ogyandsupportingpolicies,datacenterbackupgen-

erators,poweredbycleanfuels,couldsupportamorereliablegridwhilereducingthecostofdatacenterop-eration.Shiftingthedatacenter-gridrelationshipfromthecurrent“passiveload”modeltoacollaborative

“sharedenergyeconomy”—withgridresourcespower-ingdatacentersanddatacenterbackupresources

contributingtogridreliabilityandflexibility—couldnotonlyhelpelectriccompaniescontendwiththeexplo-

sivegrowthofAIbutalsocontributetoaffordabilityandreliabilityforallelectricityusers.

3.Improvepointloadforecastingtobetteranticipate

futurepointloadgrowthandmodelingoftransient

systembehaviortomaintainreliability.Forecastsneedtomakebetterprojectionsdescribingnewpointload

locations,magnitudes,andtimingalongsidebetter

techniquesformakingdecisions—tobuildornotbuildlonglead-timeinfrastructure—whilefacingtheeco-

nomic,regulatory,andpoliticaluncertaintyassociatedwithsitingtheselargepointloads.Also,real-timemod-elingofdatacenteroperationalcharacteristicsinan

increasinglyinverter-basedgridisneededtomaintainreliability.

7|PoweringIntelligenceMay2024

INTRODUCTION

ResearchQuestions

Asthenumberandsizeofdatacentersexpandtosupportcontinuedgrowthindataprocessing,internettraffic,andrapidexpansioninartificialintelligence(AI)applications,somecriticalquestionsemerge:

?Howrapidlycanweexpectdatacenterstoexpand,andhowdoestherapidgrowthinAIchangetheirpower

demands?

?Whatistheimpactofthesedevelopmentsonelectricloadandresourceadequacy?

?Whatimplicationsdothesetrendshaveforfutureelec-tricityinfrastructureplanning?

?Howcanthedatacenterandelectricutilityindustriesworktogethertosupportrapiddatacenterexpansion?

DataCentersintheUnitedStates

AsofMarch2024,therewereapproximately10,655datacentersglobally;halfofthem,5,381,wereintheUnitedStates.Justoverthreeyearsago,inJanuary2021,therewereapproximately8,000datacenters,withaboutone-thirdofthemintheUnitedStates[1].

Theconstructionofnewdatacentersisacceleratingata

rapidpace,largelydrivenbydemandforAI-poweredtaskssuchasspeechrecognition,tailoreddiagnostics,logistics,

internetofthings(IoT),andgenerativeAI.TheexpansionofinterestingenerativeAIisparticularlynotableduetothe

overnightpopularityofChatGPT,releasedonNovember30,2022,markingthepublic-facingstartofatechnologyrace.

Datacentersvarysignificantlyindesignandpurposeandaregenerallygroupedintotwocategories,smallorlarge

scale.Small-scaledatacenters,representingabout10%ofU.S.datacenterload[2],typicallycatertolocalizedopera-tionsandservicesmallbusinesses,governmentfacilities,

orspecificdepartmentalneedswithinlargercorporations.Theyincludeserverrooms/closetsembeddedinbuildingsand“edgedatacenters,”whicharestrategicallylocatedontheouteredgesofnetworkstobringcomputingcapabilitiesclosertouserswhoaregeographicallydistantfromlarge

clouddatacenters[3].Thoughtheelectricitydemandsofeachinstallationarerelativelymodest—500kilowatts(kW)to2megawatts(MW)—theyaccountforroughlyhalfofallservers[3].Marketresearchanalystshaveprojectedthe

globaledgedatacentermarkettogrowatacompoundan-nualgrowthrate(CAGR)of22.1%to2030[4],highlightingtherisingimportanceofsmall-scaleandedgedatacentersindigitalinfrastructure.

Large-scalecommercialdatacentersaredesignedtoserveextensiveoperationsandoftenservemultiplebusinesses

orevenentireindustries.Thesedatacentersseekproximitytocustomersandaskilledworkforceandcanbenefitfromlowerlandcosts,propertytaxes,laborrates,energyprices,andriskofsevereweatherorseismicactivity[5].Figures

1–3showmapsofvariouslarge-scalefacilitytypes,whichinclude:

?Enterprisedatacenters,whichareownedandoperatedbysinglecompaniesfortheirexclusivecomputingandnetworkinguse.Theseaccountforabout20–30%of

totalload[2,6].

?Co-locationcenters,whereseveralbusinessesmayrentspacetohousetheirserversandotherhardwarewithsharedenergyandcoolinginfrastructure.

?Hyperscaledatacenters,whicharecapableofrapidlyscalinguptheiroperationstomeetthevastcomputingneedsofcloudgiantslikeAmazonAWS,GoogleCloud,andMicrosoftAzure.Giventheirlargescaleandrecentemergence,theyareoftenattheforefrontofelectric-ityconsumptionandefficiencyinnovations.Hyperscaleandco-locationcenterstogetheraccountforthelion’sshareofU.S.datacenterload—about60%–70%[7].

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EnterpriseDataCenters

EnterpriseDataCenters(numberperstate)

0258

Figure1.U.S.enterprisedatacenterdistributionasof2022[4,8,9]

oCo-LocationDataCenters

Co-LocationDataCenters(numberperstate)

0163

Figure2.U.S.co-locationdatacenterdistributionasof2022[4,8,9]

9|PoweringIntelligenceMay2024

aHyperscaleDataCenters

HyperscaleDataCenters(numberperstate)

040

Figure3.U.S.hyperscaledatacenterdistributionasof2022[4,8,9]

DataCenters’PrimaryElectricity-

ConsumingHardwareandEquipment

Theelectricityneedsofdatacentersaredetermined

primarilybythethreeconstituenthardwarecategories.

Eachcategory’sproportionofenergyconsumptioncanvarydependingonthedatacenter’sage,configuration,type,

andfunction[10,11,12,13].Thethreemaincategoriesandtheirenergyconsumption[2,13,14]are:

?ITequipment,typicallycomposing40%–50%ofdata

centerenergyconsumption,encompassesthefollowingfoundationalhardwareunits:

-Servers,whicharetheworkhorses,responsiblefordataprocessingandcomputationaltasks

-Storagesystems,whichincludebothtraditional

harddiskdrives(HDDs)andthefaster,more

energy-efficientsolid-statedrives(SSDs),crucialfordataretention

-Networkinfrastructure,whichcomprisesswitches,routers,andothercomponents,ensuringseamlessdatatransferandconnectivity

?Coolingsystems,typicallycomposing30%–40%ofdatacenterenergyconsumption,arecriticaltomaintaining

anoptimaltemperaturewithindatacenterstopreventhardwaremalfunctionandensurelongevity.WhiledatacentershistoricallyusedtraditionalHVAC,advanced

coolingtechnologiesindatacentershavetransitionedtowardssystemsthatarespecializedfordatacenter

use.PleaserefertothesectionEnergyEfficiencyandLoadManagementbelowformoredetails.

?Auxiliarycomponents,typicallycomposing10%–30%ofdatacenterenergyconsumption,areusedforvarious

operationalneedsandincludeuninterruptiblepowersupplies,securitysystems,andlighting.

Assessingdatacenterenergyefficiencyiscrucialtogauginghoweffectivelytheyuseelectricity.Theseassessmentshelptoidentifytrends,driveimprovements,andsetbenchmarksforelectricityusage;andplayakeyroleinoperational

strategy[15,16].

10|PoweringIntelligenceMay2024

AIANDDATACENTERPOWER

CONSUMPTIONINSIGHTS

ImmenseVolumesofDataareBeingProcessedDaily

Datacenters’worldwideelectricityusein2022totaled300millionmegawatt-hours(MMWh),or1.2%ofallload,a45%increasefrom2015[17].IntheUnitedStatesin2023,datacentersaccountedforabout4%oftotalelectricitycon-

sumptionor150MMWh,equivalenttotheaverageannualconsumptionof14millionhouseholds[9,18].

Since2017,annualdatavolumeshavesoared,triplingtoaround4,750exabytes(anexabytebeingabillion

gigabytes)by2022,showcasingtheimmensevolumeof

informationbeingprocessedandtransmittedgloballyeveryday[19].In2022,thedailygenerationofdata—including

captured,copied,orconsumed—reachedapproximately

13exabytes,asurgepartlyattributabletotheburgeon-

ingimpactofAImodels[17].Concurrently,in2022,globaldatatransmissionnetworkenergyusewasreportedto

bearound260–360MMWh,roughlyequaltodatacenterpoweruse[17,20].Figure4illustratesthedramaticriseinglobalconsumerIPtraffic.

Datacentersarefacingasignificantchallengewithinternettrafficgrowingnearly12-foldinthepastdecade,atrend

paralleledbyincreasingAI-relatedworkloaddemands[19].ThehistoricalprecedentisshowcasedinFigure5,which

contraststheU.S.datastoragesupplyversusestimated

demandfrom2009to2020,underscoringagrowingdeficitandtheneedtoaddressthesetrends[22].

Despitetheimmensegrowthinnetworktrafficanddatageneration,loadgrowthhasbeenmuchslowerduetoef-ficiencygainsandconsolidation.

DatavolumeofglobalconsumerIPtrafficfrom2017to2022

Datavolumeinexabytespermonth

156201254319

396

400

300

200

100

0

122

201720182019202020212022

Figure4.TrendsinglobalconsumerIPtraffic,2017–2022[21]

45,000

StorageCapacityinExabytes

40,000

35,000

30,000

25,000

20,000

15,000

10,000

5,000

0

Demand

lSupply

200920102011201220132014201520162017201820192020

Figure5.U.S.datastoragesupplyvs.demand,2009–2020[22,23]

11|PoweringIntelligenceMay2024

HistoryofEnergyEfficiencyintheData

CenterIndustry

Overthelast25years,U.S.datacenterloadgrowth,asshowninFigure6,hasexperiencedthreephases:

1.Energyconsumptiongrewintheearly2000sdrivenbytherapidexpansionofinternetinfrastructureandthedot-comboom[24].

2.From2010–2020,electricityconsumptionstabilizedasdatacenterexpansionwasoffsetbyequallyrapidim-

provementsinenergyefficiencyachievedboththroughimprovementsatindividualfacilitiesandthroughthe

transitionfromsmalldatacenterstomoreefficientcloudfacilities[25,26].

3.Recentloadgrowthindatacentersisdrivenmainlyby

theexpandingdemandforcloudservices,bigdataana-lytics,andAItechnologies—whichrequiresignificant

computationalresources—andaslowingofefficiencygains[27].

Efficiencygainsinindividualdatacentershavebeenledbyadvancementsinserverefficiency,whichhavebeen

significant,leadingtoreducedpowerconsumptionperunit

ofcomputingpower[28].Powerandcoolingequipment,requiredtooperatetheITcomponents,hasalsoimproveditsefficiency.PowerUsageEffectiveness(PUE)andData

CenterInfrastructureEfficiency(DCIE),keyefficiency

metricsinthedatacenterindustry,aredefinedintheboxonthenex