麦肯锡 -托管数据中心：人工智能背后的基础设施竞赛 Colocation data centers The infrastructure race behind AI

June2026

McKinsey

GlobalInstitute

Investmentcase

Colocationdatacenters:

Theinfrastructure

racebehindAI

DatacentercapitalexpendituresaresurgingasAlworkloadsgrow,yet

competitivenessincreasinglydependsonsecuringadvancedaccelerators,reliableelectricity,andfastprojectdelivery.

byMariaGoodpaster,PiotrPikul,AnnaKortis,andOlivierBus

ThankstotheboominAl,datacentershavemovedfromthebackgroundofthedigitaleconomytocenterstage.Theyarethephysicalbackbonebehindcloudcomputing,enterprisesoftware,andgenerativeAlandsupportdailydigitalactivitybybusinessesandconsumers.Undercurrentadoptionscenarios,globaldatacenterdemandcouldalmosttriplebetween2025and2030,

fromabout82gigawattstoabout220gigawatts.

TheinvestmentcaseisoneoftenusedintheresearchfortheMcKinseyGlobalInstitute's

report,Catalyzingcompetitiveness:Wherebusinessesinvestandwhy.Thereportexamineshowvariationsinthebasiceconomicsofcomparableprojectsinfluenceinvestmentdecisionsindifferentregionsgloballyandtheimpactthosedecisionscanhaveonthefutureof

competitivenessandgrowthacrosstheworld.

AIdatacentersareexperiencingarapidincreaseininvestment

Notalldatacentersservethesamepurposes.Traditionalclouddatacenterssupportabroad

mixofworkloadssuchasstorage,webservices,andenterpriseapplications.Aldatacenters

arebuiltformorespecializedworkloads,housinglargeclustersofadvancedchipstotrainandrunAlmodels.ThisdistinctionmattersbecauseAlisnowaprimarydriverofdatacentergrowth.From2025to2030,non-Aldemandcouldincreaseby1.7timesfromabout38gigawatts

to64gigawatts.MeanwhileAl-relateddemandisexpectedtogrow3.5timesfromabout

44gigawattsto155gigawatts,accountingforapproximately70percentoftotaldatacenterdemand.?ThisanalysisfocusesonAldatacenters.

Aldatacentersrequiretwomaintypesofworkloads,trainingandinference.Training

involvesbuildingmodelsbyprocessingvastdatasetsoverconcentratedperiodsofveryhigh

consumptionofcomputingpower.Inferenceiswhathappensafterdeployment,whenamodel

isusedtoanswerprompts,runsearches,andgeneratecontentinrealtime.Manyofthelargestandmostpower-intensivefacilitiestodayarelargelydevotedtotraining,butinferencemay

growfasterasAladoptionbroadens.3ThisanalysisassumesthatAldatacenterscanservebothworkloadsandusesablendedaverageofthetwo.GlobalinvestmentindatacentersexcludingIThardwaremayexceed$1.7trillioncumulativelythrough2030.4

Thenextwaveofdatacenterdevelopmentwillikelycombineverylargetrainingcampuses,retrofittedorexpandedcloudfacilities,andabroadernetworkofinferencecapacitylocatedclosertousers.Facilitiesonceoperatedattensofmegawattsbutarenowbeingplannedforhundredsofmegawattsorevengigawatts,especiallyintheUnitedStatesandChina.

TheUnitedStatescurrentlyhasthelargestshareofdatacenterinvestment,followedbyChina

TheUnitedStatesaccountsforasignificantshareofdatacenterconstruction(table).NorthernVirginiaiscurrentlyhometooneofthelargestdatacenterclustersglobally,butgridconstraintsandlongertimetopowerarecontributingtonewdevelopmentsinothertier-onemarketsand

newlocations.5Domesticcloudprovidersandexpandingstate-backedinfrastructuresupportslarge-scaledevelopmentsinChina.⁶China'slargestdatacenterhubsremainconcentratedin

easternandcoastalregionswheredemandishighsuchasShanghaiandthePearlRiverDelta,aswellasinBeijinginthenorth.NewcapacityisincreasinglybeingpushedintowesternregionswithabundantenergysuchastheYangtzeRiverBasinandtheChengdu-Chongqingeconomiczone,underthe“EasternData,WesternComputing”strategy?

Colocationdatacenters:TheinfrastructureracebehindAI2

Colocationdatacenters:TheinfrastructureracebehindAI3

Elsewhere,datacenterexpansionissmaller.TheUnitedKingdomhasthemostdatacenters

inEurope,andconstructionispickingupinFranceandtheNordiccountries,whichhavemoreabundantenergy.TheNordicsinparticularbenefitfromlower-cost,low-carbonpower,acoolerclimaterequiringlessenergyforcooling,androomtoscale.SingaporeisSoutheastAsia's

establishedregionalhubwithalongstandingreputationforservingdemandfrommultinationalfirms,eventhoughlandandpowerconstraintshavepushedincrementalgrowthintonearby

Johor,Malaysia.8

Table

Installedhyperscalerdatacenterandcolocationcapacityin2025bycountriesanalyzed

Installedcapacity

Regionexaminedin

Country

(gigawatts,2025)

thisanalysis

UnitedStates

36.0

NorthVirginia

China

20

BeijingandShanghai

UnitedKingdom

2.2

London

Sweden

0.8

NorthernSweden

Singapore

1.4

Singapore

Global83.0

Regionaldifferencesinelectricitypricesandcoolingneedsexplainthedifferenceinlevelizedcosts

Thisanalysismodelsa100-megawatt,liquid-cooledAldatacenter,representingatypical

phaseinalargercampusbuild-out.Itassumesacolocationmodel,socostsincludeonlythephysicalinfrastructureneededtobuildandoperatethefacilityandexcludeIThardwaresuchasgraphicsprocessingunitsandservers,whichuserswhorentthespaceprovide.Thisallowsalike-for-likecomparisonofdatacenterinfrastructureeconomicsacrosscountries(see

sidebar"Methodology").

Chinaisthebasecaseinthisanalysisandreflectsthecostinlargedemand-centermarketswheredemandishigh,suchasShanghai.WecompareittosimilardatacenterinvestmentsinNorthernVirginia,Singapore,NorthernSweden,andLondon.

Colocationdatacenters:TheinfrastructureracebehindAl

Sidebar

Methodology

Thisinvestmentcasecomparesthe

costsofanAldatacenterprojectacrossvariousgeographiestounderstand

whatmakessomeregionsmorecost-

competitivethanothers.Ourlevelized

costmethodologyconvertsaproject'sfulllifecycleeconomicsintoasingleunitcost.Itcanbeinterpretedastheunit

pricethatwouldmakeaproject'snet

presentvalueequaltozerooveritsentirelifecycle,whichistheminimumprice

thatmakesthatprojectviableandisin

linewiththemacroeconomicconceptof

longrunmarginalcosts.Itisalsoinline

withthemacroeconomicconceptoflong-termmarginalcosts.Thelevelizedcost

conceptiscommonlyappliedintheenergysector,whereitisknownasthelevelized

costofenergy.Wedonotconsidertaxes,subsidies,andexternalities,whichvaryandarehardtopindown.

ThecalculationsareinformedbyMcKinsey'sworkinthedatacenter

industry,whichprovidesourunderstandingofthecapitalexpenditure,labor,energy,

materials,andotherinputs,aswellastime,typicallyneededtobuildandoperatea

datacenter.Wepricetheinputsatthe

typicalcostsinageography,drawingon

proprietarydatabasesmaintainedbyMGI'sEconomicsResearchteam,andatthe

typicalweightedaveragecostsofcapital,drawingonMcKinsey'sValueIntelligenceplatform,acurateddatabaseofthe

financialsofcompaniesglobally.

Benchmark:China'seasternregion

Thelevelizedcostofdatacenterenergy,measuredindollarspermegawatt-hour(MWh)offacilityenergy,capturesthelifetimecostofbuildingandoperatingphysicaldatacenterinfrastructure.

Inthefivecountriesintheanalysis,levelizedcostrangesfromroughly$200perMWhinplacesinChinawheredemandishightocloseto$380permegawatthourinLondon(Exhibit1).⁹In

China,costsbreakdownasfollows:55percentofcostsrelatetoutilities,justunder30percenttopowerandcoolingequipment,andtheremaindertoconstruction.

Colocationdatacenters:TheinfrastructureracebehindAI

Exhibit1

UtilitypricesandcapexarethebiggestcostsofbuildingandoperatinganAlcolocationdatacenter.

LevelizedcostofAlcolocationdatacenterenergy,$/MWhoffacilityenergy,beforetaxesanddirectsubsidies

■Utilities■Powerandcoolingequipment■Construction

China

109

53

36198

Sweden

54

101

68223

US

92

96

54242

Singapore

126

11276

315

UK2210659378

Note:Levelizedcostsshownarepretax.

IReflectsa100-megawattAldatacenterfacility,excludingIThardware.Datacenteconstructioncostrends"Turner&Townsend,2025;Globalconstruction

marketinteligence:?Turner&Townsend,2025;AsiaPacificdatacentreconstructioncostguide”Oushman&Wakefeld,2026.A7-percentpremiumwasaplied

totheTurner&Townsenddatacentercostindextoconvertair-cooledclouddatacentercoststoliquid-cooledAldatacentercosts.

Source:McKinseyGlobalInstituteanalysis

McKinsey&Company

Electricityisthemainreasonforthecostgap.Electricitypricesare1.9timeshigherinLondonthaninChina'smajordemandcentersinBeijingandShanghai,explainingnearly60percentofthedifference.Acrosscountries,utilitiesaccountforalmost30percentofthevariationinlevelizedcost,makingitthesinglemostimportantcostdriver(Exhibit2).

Powerandcoolingequipmentaccountsforcloseto30percentofthevariation.HotterclimateslikeSingaporerequiremoreintensivecooling.Bycontrast,marketslikenorthernSweden

andtheUnitedKingdomcanhavehigherequipmentcostsbecauseofstricterredundancyorperformancerequirements.Chinahaslowerpowerandcoolingequipmentcosts,reflectingitslargemanufacturingscale.

Exhibit2

EnergycostsaccountformuchofthevariationinthecostofanAlcolocationdatacenteracrosscountries.

LevelizedcostofAlcolocationdatacenterenergy,$/MWhoffacilityenergy,beforetaxesanddirectsubsidies

ConstructionPowerandcoolingequipmentUtilities

Basecase:China

36

53

109

198

Driversofcost

differences,basetocomparisoncountry

China

●CapitalexpendituresConstruction

Powerandcoolingequip

ment

98

1

+3

6

+54

198

21

+

+49

198

+49

+73

198

7

+2

+59

●Utilities

Power-57

Water

+2

-19

|+2

+16

|+1

+101

+3

●Performancedrivers

Timetomarket

Capacityutilization-10

Interactionvariable-6

+5

-10

-5

+5

-10

-14

-10

-6

+5

Countrytotal

Sweden

223

1.1×

UnitedStates

242

1.2×

Singapore

315

1.6×

UK

1.

378

9×

100-megawatttier3-equivalentAldatacenter.

Source:McKinseyGloballnsituteanalysis

McKinsey&Company

OuranalysissuggestsAldatacentersarefoundintwocategoriesofmarkets,supply-

advantagedexpansionmarketsanddemand-ledhubswithdeepdigitalecosystems.They

arehighlypower-intensiveassets,solower-costelectricitycancreateamajoradvantagefor

supply-orientedmarketslikeSweden.Buttheyarealsohighlynetworkedassets,requiringfiberconnectivityandproximitytomajorusers,whichexplainswhymoreexpensivemarketsliketheUnitedKingdomstillattractinvestment.

Moreover,thehighest-performingleading-edgechipsaresubjecttoexportcontrolsrelating

toChina.Iflesshighlyperformingchipsareused,asisgenerallythecaseinChina,theenergy

consumptionforeachunitofcomputeincreases,flippingtheeconomics(seesidebar"Whychipsfliptheeconomics”).

Colocationdatacenters:TheinfrastructureracebehindAI

6

Colocationdatacenters:TheinfrastructureracebehindAI

Sidebar

Whychipscanfliptheeconomics

OurlevelizedcostanalysisisolatesthecostofbuildingandoperatingAldata

centerinfrastructure,whichisthetypicalbusinessmodelforcolocationdatacenterplayers.However,theeconomicsforAl

labsdependowninfrastructureand

computinghardware,whichmeansthatdifferencesinchiporgraphicsprocessingunitavailabilitycanmateriallychangethecalculusacrosscountries.

TheUnitedStateshasstrongpositioninginadvancedAlcomputeduetoacombinationoffactorssuchasthedominanceof

cutting-edgecompaniesinAlacceleratordesign,closeintegrationwithleading

semiconductormanufacturersinTaiwanandSouthKorea,andamaturesoftwareecosystemthatenablestheirchipstobe

deployedatscale.AccesstothemostadvancedAlchipsinmainlandChinaislimitedbyUSexportcontrols.

Theseconstraintshavecontributed

toincreasedrelianceondomestic

alternativesandonbuildingamoreself-

reliantsemiconductorecosystem.Theseapproachescanstillsupportrapidbuild-outsbutmayinvolvetradeoffsinperformance,powerefficiency,andutilization,especiallyforfrontierAlworkloads.

Chinesesystemshavelowercompute

efficiencycomparedtothelatestplatformsfromleadingUScompanies.'Thecomputeefficiencyoftheirchips,measuredin

PFLOPSpermegawatt,istentimesormorethatofChina'smostsophisticatedchip.Asaresult,alargernumberof

Chinesechipsareneededtodeliverthesamelevelofcomputingpower.The

requirementofalargernumberofgraphicprocessingunitsmayhaveanimpact

oncost-effectivenessdependingonconfigurationandusecase.

Thishighlightsanimportantdynamic:DatacenterinfrastructureeconomicsandGPUeconomicsarecloselylinkedoutsideChinawhereAllabssourcethesamechips-buttheyalsoaffectthecaseinChina.Ifthe

availablechipsinChinaarelessefficient,morehardwareisneededtodeliverthe

sameoutput,raisinglevelizedcost.WhileChinamayhaveadvantagesindata

centerinfrastructure,theseadvantages

candiminishandevendisappearafter

differencesinchipsaretakenintoaccount.

Exhibit3

China'scurrentleading-edgechiplagsotherleading-edgechipsincomputingefficiencyandcost.

Costefficiencyvscomputingefficiencyofleading-edgeGPUs¹

chip

LeadingUSchip1

2,0003,000

25

LeadingChinese

15

1,000

Leading

USchip2

4,0005,000

Cost

efficiency,

$million/MW

Computeefficiency,petaflops/MW²

Graphicsprocessingunits.

2Petaflops(PFLOPS)areusedtomeasurecutting-edgecomputingpower.OnePFLOPequalsonequadilionfloating-pointoperationspersecond.Source:McKinseyGlobalInstituteanalysis

McKinsey&Company

1Thiscomparisonismeasuredinpetaflops,ameasureofsupercomputerspeedthatrepresentstheabilitytoperformonequadrillion(10^15)floating-pointcalculationspersecond.

Geographymattersmoreonthecostside,andbusinessmodelmattersmoreontherevenueside

Datacenterrevenuesarelessdependentongeographythancosts.Colocationpricingdoesvaryacrossmarkets,butrentalratesvarylessthanlevelizedcostsandoverlapsubstantiallyacrossmajordatacenterhubs.Pricingdependsnotjustonlocationbutalsooncustomertype,contractstructure,andlocalsupply-demandbalance.

Becausethisanalysisfocusesoncolocationdatacenters,therelevantrevenuebenchmarkiscolocationpricingindollarsperkilowattpermonth.Here,thebiggestdifferencesareofteninfluencedbybusinessmodelratherthanbygeography.ColocationdatacentersareownedbyoperatorslikeEquinix,QTS,DigitalRealty,DATA4,andChinDatathatservebothlarge

hyperscalertenantsandsmallerenterprisecustomers.

Retailcolocation,whichservessmallerormorefragmentedcustomers,typicallyhashigher

pricingbecausethoseprovidersoffergreaterflexibility,redundancy,andmanagedservices.

Atthesametime,large-scaledeployments,whetherthroughhyperscalertenantsorthroughwholesalecolocationagreementsforlargeenterprisesarelowerriskandlowerpricedbecausecapacityisoftenpreleasedunderlarge,long-termcommitmentsfromoneorafewcustomers.

Thisdifferencehasanimpactonreturns.Retailcolocationtypicallyearnsabout$200to

$380perkilowattpermonthandcangenerateequityinternalratesofreturnofroughly20to25percent.Wholesaleleasesearnless,atabout$150to$200perkilowattpermonthwithinternalratesofreturnofabout13to18percent.

Thissuggeststhatlowerlevelizedcostdoesnotautomaticallytranslateintohigherreturns.Businessmodelsandmarketsaturationdeterminewhethercostadvantagestranslateintohigherreturns.

Marketsattractinginvestmenttodayareonesthatcombineconnectivity,demand,andspeedofexecution

Beyondcostadvantages,Aldatacenterinvestmentflowstomarketsthatofferconnectivity,speedofexecution,and,increasingly,strategicalignmentwithnationalAlpriorities.

ConnectivityisimportantforAldatacenters,especiallythoseservinginferenceworkloads,needtositwithindensefibernetworksandclosetomajorusers.Thisexplainswhyestablishedhubs

suchasNorthernVirginia,London,andSingaporecontinuetoattractinvestmentevenwhentheyarenotthecheapestplacestobuildorsourceelectricity.

Investmenttendstofavormarketswithdemonstrateddemand.Developersaremorewillingtobuildatscalewhentheyhaveaccesstoabaseofhyperscaler,enterprise,orpublic-sectorcustomersandwhenfutureutilizationissupportedbypreleasingorexpansionbyexistingtenants.Inthesecases,

Colocationdatacenters:TheinfrastructureracebehindAl

8

Speedofexecutionmattersasprojectsbecomelargerandmorecomplex.10Laborshortages,

equipmentleadtimes,permittingdelays,andslowgridconnectionscanpreventotherwise

attractivemarketsfromaddingnewcapacityfastenough.Leadtimestoobtainkeycomponentssuchasgenerators,chillers,transformers,andswitchgearhavemorethandoubledsince2019,insomecasesreachingmorethanthreeyears.Gridconnectionisanotherbottleneck,withaveragewaittimesnowexceedingfouryears;insomemarkets,connectiontopowercantakeaslongasadecade."Suchconstraintsarealreadypushingconstructioncostshigherandextendingbuildtimes,limitinghowquicklynewcapacitycanbedeliveredeveninotherwiseadvantagedmarkets.

Governmentprioritiesareshapinginvestmentflowsmoredirectlythanbefore.InEuropeandelsewhere,so-calledsovereignAl,orplanstodevelopandhostAlinfrastructuredomesticallyratherthanrelyingonforeignproviders,isgainingtraction.Forexample,SoftBank'srecent

pledgetoinvestupto€75billioninafive-gigawattAlinfrastructurenetworkinFrance

underscorestheroleofthecountry'spoweravailability,governmentbacking,customeraccess,andsovereignAlprioritiesinattractingAldatacenterinvestment.12

Takentogether,thesefactorshelpexplainwhyinvestmentdoesnotalwaysflowonlytothe

cheapestenergymarketsonlyandhighlighttheimportanceofcreatingamarketwhere

datacenterprojectsareeasiertodeliverandscale.Asmuchas50percentofglobaldatacentercapacityduetocomeonlinein2026couldbedelayedbypermitting,gridconnectiondelays,publicopposition,andrisingpowerdemand.3

Reducingthetimeittakestoconnectadatacentertopowermayincludeaskingdatacenter

operatorstoleveragelong-termpowercontractsorcomeupwithself-generationarrangements,assomehyperscalersarealreadydoing.Suchstepscanoffsettheimpactofdatacenterson

localelectricitycosts,whichhavealreadystartedtoriseinNorthernVirginiaandotherparts

oftheUnitedStates.⁴Asprojectsscaleup,solutionsmayinvolvegoingbeyondpermittingto

createshovel-readysitesfasterandsupportstandardizeddesignsthatcangainapprovalsmoreefficiently.Improvedschedulesareoftenmorevaluablethanimprovedcostsandremainatop

priorityfordatacenterdevelopers.

Aclearstrategicapproach,especiallyinmorefragmentedmarketslikeEurope,mayhelp

policymakers.Withoutstrategicalignment,lower-costenergylocationssuchasberiaandthe

Nordiccountriesmayremainunderutilizedwhilehigh-demandhubssuchasParis,Frankfurt,

andLondonareconstrained.Thekeyisaligningdatacenterambitionswithadvantagesinpower,networks,demand,andexecution.

MariaGoodpasterisaMcKinseypartnerbasedintheDallasoffice,PiotrPikulisapartnerbasedintheTorontooffice,AnnaKortisisaMcKinseypartnerbasedintheViennaoffice,andOlivierBusisaconsultantbasedintheAmsterdamoffice.

TheauthorsoftheMcKinseyGloballnstitutereport,Catalyzingcompetitiveness:Whereinvestmenthappensandwhy,wouldalsoliketoKonstantinWirthandShuChernLimforcontributingtothisresearch.

Colocationdatacenters:TheinfrastructureracebehindAI

9

Endnotes

"ThenextbigshiftsinAlworkloadsand

hyperscalerstrategies,"McKinsey,December17,2025.

2"Scalingbigger,faster,cheaperdatacenterswithsmarterdesigns,"McKinsey,August1,2025.

3"ThenextbigshiftsinAlworkloadsand

hyperscalerstrategies,"McKinsey,December17,2025.By2030,demandforAltraining

datacentersisexpectedtorisefrom31GWto62GW