人工智能如何重新构建 未来的数据中心 HOW AI IS RE-ARCHITECTING THE DATA CENTER OF THE FUTURE

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RE-ARCHITECTING(→)THEDATA

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PRAvEENMIDHA“WHITEPApERŊ

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HOWAIISRE-ARCHITECTING(→)

THEDATACENTEROFTHEFUTURE

ExecutiveSummary

Artificialintelligence(AI)hasproventobeaparadigmshiftforworkloadsinthemoderndatacenter.Ithasreshapedindustryperspectivesoneverythingfromdataprocessingtopowerconsumption.ThiswhitepaperexaminesAI’stransformative

impact,includingtherisingtemperatureofdatautilization,thegrowingenergydemandsofacceleratedcomputing,andthe

limitationsofcurrentstoragearchitectures.Weexplorehownewandinnovativetechnologiesarebridgingthesegapstomeettheneedsoftomorrow'sAI-drivenworkloads.

ThreeKeyTrendsintheDataCenter

1.AI:AMissionCriticalEnterpriseWorkload

AIhaslongexistedinthetechecosysteminvariousforms,butwhenagamechangingAIchatcametomarketinNovember2022,itsparkedarevolutionincomputecenteredaroundLargeLanguageModels(LLMs)andotherGenerativeAI(GenAI).Thisnewworkloadprovidesexcitingnewcapabilitiesthatinturnprovidedasurgeofinterestandinvestment.

AIITspendingisprojectedtogrowatacompoundannualgrowthrate(CAGR)of31.92%,from$315.9billionin2024to$1.262trillionby2029.Bythen,AIspendingwillconstitute16.4%oftotalITexpenditures.1

CAGR22%

CAGR39%

ServiceprovidersandenterprisesareheavilyinvestinginrelativelynascentanddistinctAIinfrastructure.Accordingtoarecentreport,datedAugust4th2025,the2025GlobalCloudCapExisnowtrackingto$445Band56%Y/Ygrowth,withupsideto

+30%growthin2026.2

WeareexitingtheearlystagesofwhatsomehavecalledtheAIgoldrush,anditisalreadyclearthatAI’srisesignifiesa

paradigmshiftintheentiredatacenterarchitecture,withimplicationsforstorage,compute,andnetworking.Attheendof2024,SandiskintroducedanewAIDataCyclestorageframeworktohelpcustomersunlockAI’spotential.

Learnmoreabouttheframework

2.RisingGlobalDataTemperatures

DatafuelsAI.Atitscore,AIisfundamentallyaboutdatautilizationanddatageneration.LLMsthriveonever-expanding

datasetstoenhanceaccuracy,versatility,andbiasmitigation.Intheprocess,AIsystemscreatenewdata,makingexistingrepositoriesmorevaluableformodelcontextandlearning.

ThecurrenttrendforpublisheddatapointsforLLMsshowsthattrainingdatasetsizesaregrowingdramatically.AImodels

operateinacontinuousloopofdataconsumptionandgenerationandthevastmajorityofthisdataisunstructured.However,thisinsatiabledemandfordatacreateschallengesforstoragearchitects.

HerearesomekeyinsightsfromIDC’s2025GlobalDatasphere3:

•Datagenerationgrowth:24%CAGRfrom2023to2028

•Enterprisedataoutpacingconsumerdata:30%CAGR

•Unstructureddatadominance:92.9%ofalldatain2023

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LLMshavebecomemultimodal,processingdiversedatatypessuchastext,video,andimages.Theevolutionofdata-richgenerativeAIchatmodelsimpactsthecapacityrequirementsofunderlyingstorage,andthespeed,latency,retrievability,andscalability.

TooptimizetrainingtimeandGPUusage,storagearchitecturesmustquicklydeliverrelevantdatasetstoGPUs.IDCcategorizesdataintothreelatencytypes4:

•UltraReal-Time(<40mstransferlatency)

•Real-Time(40ms–200mslatency)

•Nominal-Time(>200mslatency)

AsdatalatencytrendstowardsReal-TimeandUltraReal-TimeinGPUcentricworkloadsthereisagrowingneedforfaster

storageandretrievalindatacenters.Serviceprovidersarerespondingtothiswithinnovationslikeamajorcloudprovider’sS3optimization(Nov2023),offeringupsignificantlybetterperformanceforAIandMachineLearning(AI/ML)trainingwithsingle-digitmillisecondlatency.5

3.Power:TheNewMetricforDataCenterCapacity

Datacentercapacityhastraditionallybeenmeasuredbyfloorspacesize.However,AI-drivenworkloadsareinherentlymorepowerintensivethanmainstreamworkloads.WhileGPUsaremorepowerefficientthanCPUsforAImodeltraining,the

amountofGPUsrequiredtotrainthelatestmodelsismeasuredinthetenstohundredsofthousandsofunitsoperatingconstantlyatfullcapacityformonths.Likewise,AIinferencingworkloadsleveragelargeclustersofGPUstogenerate

responses,andconsumemorepowerthantraditionalanalyticsordatabaseworkloads.

AIdatacenterpowerconsumptionisgrowing:

•AIdatacenterenergyconsumptionisprojectedtogrowata19.5%%CAGR,between2023and2028.6

•TheshareofU.S.powerdemandconsumedbydatacentersisexpectedtoincreasefrom4.4%todaytobetween6.7and12%by20287

Powerhasbecomeacriticalmetricfordatacenters,incurringthelargestoutgoingexpenseofdatacenteroperationand

accountingfor60%oftotalspendingforserviceproviders.6Risingelectricitypricesandgrowingdemandunderscorethe

importanceofenergy-efficientarchitectures,andacomplexinterplayoffactorswillmakesupplymoreunpredictableand

costly,suchasenvironmentalregulation,geopoliticalfactorsandextremeweatherevents.Powerhasemergedasthenewlyimportantunitofmeasureasitbetterrepresentsdatacentercapacitythanfloorspace.

TheEconomicsof“StateoftheArt”StorageHDDsDominateToday,butChangeIsComing

In2024,TapeandSSDsrepresentedacombined20%whileHDDsrepresentedtheremaining80%ofthehyperscalestoragemediashareofinstalledbaseofcapacity.8

EBInstalledBaseShare6

Layeredonthisstoragemedia,CloudServiceProviders(CSPs)offeraccesstoallthreestorageprotocols:Block,File&Object.

WhileBlockandCloudFilestoragehavesmallerfootprints,Objectstorageisthelargestandfastest-growingsegmentindatastorage.Blockstorageworkloadsareperformance-demanding,latency-sensitive,andhaverandomaccesspatterns(IOPS).

CloudFilestorageisdesignedforsharedfileaccess,withbothBlockandFileusingamixofSSDsandHDDs.

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Objectstorageemphasizesscaleandeconomics,withcommonusecaseslikebackup&restoreandarchiving.Asa

hypotheticalexample,amajorcloudproviderboasts350trillionobjects,amountingtoexabytes(possiblyzettabytes)ofdata.Objectstorageworkloadsaremostlysequential(bandwidth),favoringHDDs.

Givenitsfocusonscaleandcost-efficiency,it'snosurprisethatmostclouddatahastraditionallybeenstoredonHDDs.

TheChangingWorkloadMix

AIisdrivingtheadoptionofdatalakesasanemergingusecase,besidesthetraditionalbackup&restoreandarchiveforobjectstorage.Datalakesarecentralizedrepositoriesthatstoreandprocesslargeamountsofunstructureddata,andtheysupport

bothtrainingandinferencetasks.Unliketraditionalworkloads,datalakesrequirehighperformanceandlowlatency.HDDsarestrugglingtomeetthesedemands,pavingthewayforSSDsthatcanchecktheboxesforbothperformanceandcapacityinperformance-criticalscenarios.

ServiceprovidersarerespondingtothisgrowingAIworkloadbyintroducinganewstorageclassforobjectstoragethatis

optimizedtodeliverasignificantperformanceupliftneededbytheseworkloads.Thisrequiresasolutionthatcancheckboththeboxesofhigherperformanceandlargercapacity.

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Asthefollowinggraphshows,HDDperformancehasstagnatedovertheyears,whileperformanceperterabytehassteadilydecreasedascapacityhasincreased.

HDDThroughputandCapacityGrowth

CSPshavetypicallyofferedseveralstorageclasses,whichcanbebroadlydividedinto3buckets:Standard,InfrequentAccessandArchival.ThesehavetraditionallybeenservedfromHDD,andwithsomearchitecturalchangesHDDscouldperformas

neededwithover-provisioningforcapacityorperformance.WiththerecentdemandsofAI,CSPshavestartedtooffera

performance-optimizedtierthatisdifficulttoachievesimplybyaddingmorecapacity-thisiswherehighcapacitySSDsarefitforpurpose.

TotalCostofOwnership(TCO)IsKey

Historically,storagebuildoutshavebeendrivenbydeviceacquisitioncosts,giventheavailabilityofabundantspace,low-

costpower,andreasonableperformancemetrics.Onaverage,theprice-per-gigabyte(ASP$/GB)forflashstoragehasbeenapproximatelysixtimeshigherthanHDDs,makingHDDsthecleareconomicchoice.

However,thisequationisshifting.Risingelectricitycosts,increasingpowerdemandsfromGPUs,andtheneedforfasterdatalakesarereshapingthestoragelandscapeforAIworkloads.Asaresult,TotalCostofOwnership(TCO)isbecomingamore

relevantmetricthanASP($/GB)alone.

TCOincludesboth:

•Acquisitioncosts:Servers,storage,networking,andsoftware.

•Operationalcosts:Infrastructure(floorspace,power—bothidleandactive),labor,support(hardwarereplacements,dataprotection,reliability).

BeyondTCO,anothercriticalshiftismovingfromrawstoragetoeffectivestorage,whichprovidesamoreaccuratemeasureofstorageefficiency.Effectivestorageconsidersfactorssuchas:

•Capacityutilization

•Dutycycle(activevs.idle)

•Replicationfactor

•Performancemultiplier

•Datareductionratios

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Asthedemandforbothstoragedensityandperformancedensitygrows,evaluatingtheavailableHDDandSSDcapacitypointsbecomesessential.

ProjectedSSDandHDDCapacityGrowth9

LetsassumetwovariationsofSSD/HDDASP($/GB)withmultiplesof6xand5x.

UsingtheseassumptionsandwhatwehavelearnedaboutthenewdeploymentssupportingAIworkloadsinthedatacenter,webuiltaTotalCostofowernship(TCO)modelforahypotheticalgreenfielddatacenterwith1EB(1000PB)ofstorage.Themediaoptionsareall-HDDwiththehighestavailable30TBHDDvsall-SSDwithvariouscapacitypointsfrom30Tto240T.

SSD/HDDTCORatio10

ItisclearfromthechartthatasSSDcapacitiesgoup,theirTCOsstarttolookmorecompetitivevsHDD.Inthesecondchart,wekepttheASPmultipleat6xbutdoubledthecostofpower.Athigherpowercost,theall-SSDTCOstartstolookmore

attractiveatlowercapacitypoints.

NOTE:ForSAS/SATA,theconversionfromHDDtoSSDwasrealizedat3xmultiple

PerformanceConsiderations

ThelatestSSDcapacitypointsleverageQuad-LevelCell(QLC)NANDtechnology,whichincreasesbitdensityby33%

comparedtoTriple-LevelCell(TLC).WhileQLChaslowerrandomread/writeperformance,lowersequentialwritespeeds,

andlowerendurancethanTLC—alongwithslightlyhigherwritepowerconsumption—itsincreasinglayercountandcompactpackagingcontinuetodrivecapacityadvancements.

Moreover,QLCSSDsarewell-suitedforread-heavy,large-blocksequentialworkloads,aligningwithobjectstorageusecases.DespiteQLC’strade-offs,SSDssignificantlyoutperformHDDs,offering:

•MassiveIOPSadvantagesinrandomworkloads.

•2–3xhighersequentialthroughput(bandwidth)comparedtoHDDs.11

•SuperiorscalabilitythroughtheNVMe™interface,unlikeHDDs,whichremainconstrainedbylegacySAS/SATAinterfaces.

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ClosingThoughtsontheGrowingRoleofAIinDataCenters

AIisrapidlyemergingasastrategicworkloadinthedatacenter.Theracetodevelopnext-generationLLMsiswellunderway,withthesemodelsexpectedtobemorecompute-intensiveonaverage.AstheytrainonlargerdatasetswithfasterGPUs,thedemandsonstoragearchitectureareevolving.Futurestoragesolutionsmustnotonlyscaleincapacityandspeedbutalso

optimizepowermanagementmoreintelligentlythanlegacyalternatives.

IDC'sviewisthatGenAIwillhaveagreaterpositiveimpactonSSDandmemoryspendingthanHDDs.12

AtSandisk,we’reexcitedaboutthefutureofAI-drivenstorageinnovation.Ournext-generationSSDsarebeingdesignedwith

highercapacities,continuedperformancescalingthroughPCIe™Gentransitions,andconfigurablepowerprofilesthatalignwiththeperformanceandcoolingrequirementsofmoderndatacenters.

AsSSDcapacitiesgrow,newtechnicalchallengesandopportunitiesarise—suchasblastradiusmanagement,dataretentionvs.power-performancetradeoffs,indirectionunit(IU)size,anddataplacementstrategies.AtSandisk,werecognizethese

challengesandareactivelyinnovatingtoaddressthem,ensuringthatourstoragesolutionsmeettheevolvingneedsofAI-powereddatacenters.

1.IDC,WorldwideArtificialIntelligenceITSpendingForecast2024–2028,#US52635424,October2024.

2.MorganStanleyResearch,August42025,CloudCapex.

3.IDC,WorldwideGlobalDataSphereStructuredandUnstructuredDataForecast,2024–2028#US52554824September2024.

4.IDC,StreamingandReal-TimeDatainIDC’sGlobalDataSphere,#US53122225,2025.