摩根士丹利：全球科技行业研究：存储领域-如何布局新的 AI 瓶颈

MorganstanleyRESEARCHGlobalIdea

January15,202610:30PMGMT

GlobalTechnology

Memory–HowtoPlaytheNew

AIBottleneck

Memorysitsinacapacity-constrainedcyclewithunusuallylongordervisibilitydrivenbyAIinference.For2026,theriskis

executionandtransition,notdemand.Asteeperpricingclimb

andfavourableconditionslikelypersistthrough2027.Multipleshaveexpanded,butwethinkstockcallscanstillworkwithmuchhigherearningsupsidefromhere.

Inferencebecomesamemorychallenge,notjustcompute.Memoryaccess

increasinglydeterminestheperformanceoflongertext,image/videoandAgenticAIworkflows,withfarmorerobustmemoryrequirementsthanpriorAImodelsto

supportcontext,autonomyandcontinuouslearning.ThesesystemsrequiresuperiorserverDRAMandenterpriseNANDtofunctioneffectively.

Memorycycle–asteeperpricingclimb.Memorypricingpowerisshiftingat

lightingspeed.WeexpectasteeperupcyclewithrapidgainsinDRAM,HBM,NAND,andlegacymemory.Innovationandarchitecturalredesigncontinuetoimprove

memoryefficiency,enablingAIsystemsdeliverbetterlatencyandcostprofiles,

materiallyenhancinguserexperience.ThislowerstheeconomicbarriertoadoptionandunlocksasignificantlylargerAITAM,evenasaggregatememorydemand

continuestoscalewithbroaderdeployment.Ouranalysissuggeststhattext-onlyAIinferencealonecouldaccountfor35%of2026globalmemorysupplyforDRAM

and92%forNAND.

What'schanged?Thekeydebateisnowshiftingtowhethersupplycancatchupandwherethetruechokepointsits,andthereforewhethertightnessandpricingpowerpersistornormalize.Near-termpriceexpectationsmatterless,inourview,butourchannelchecksindicatepotentialupsidetoalreadyaggressive70%+QoQhikesforbothDRAMandNAND.Inventorylevelcontinuestofallacrossthesupplychain.

Capexaccelerationisinevitable,focusedonDRAM,andwedoexpectmore

meaningfulgreenfieldexpansionsfrom2027.Thesupply-demandgapforlegacymemoryiswideningfurtherforDDR4/3,NORandSLC/MLCNANDandweraisePriceTargetsacrosstheboard.

Bottlenecksarethewinners–buymemoryandsemicap,especiallyEUV.We

preferhigherpricingpowerinDRAM(Samsung,SKhynix,MU),legacymemory

(Winbond),HDD(WDC),andcapexbenefitsviaSPE(ASML),packaging(DISCO)vs.downstreamhardwareandconsumerfacingmarginpressure.Negativefactorsto

watchincludetheimpactofdemanddestructionforsystem/hardwaredevicevendorsandchallengingYoYgrowthcomparisonsfrom2H26formemory.

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Technology-EuropeanSemiconductors

Europe

IndustryViewIn-Line

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HowtoPlaytheAIBottleneck?

Historicmemoryshortage–aprecursortoahistoricsemiconductorproduction

footprint.WithDRAMpricesnowsurpassingmetalsasabenchmarkofscarcity,the

memorysectorentersaperiodofextendedcapacityconstraint.Memoryalongwith

advancedlogicfoundrymanufacturersmustfindwaystosecureandmanagesupplychainsforrapidlygrowingAIinfrastructureconsumptionandensureexpansioninwafer

manufacturing.Thebottlenecksinthesemiconductorindustrybecomethewinnersin

stocksperformanceandtheknock-oneffectonadjacentpartsofsemiconductorstendstobeunderestimated–thekeybottlenecktoAIhasshiftedtocommodityDRAMandNANDfromCoWoSandHBM.Nextcouldbesemiconductorequipment–inparticularEUV

(

ASMLHoldingNV:StrongerSet-Upfor2027;RaisePTto€1,400)

.Henceourglobaltop

10pickstoplaythememorybottleneck:

•DRAM–Samsung,MUandalsolikeSKhynix

•LegacyMemory–Winbond

•Storage–WDC

•Advancedpackaging–DISCO

•Semicap–AMAT,ASMI

•EUV–ASML

Thenatureofthecorrelation.Thesemicap(semiconductorcapitalequipment)aswellasthelogicfoundrycyclesarecloselyandintrinsicallycorrelatedtotheDRAMcycle,with

thememorymarketactingasaprimarydriverofthebroadersemiconductorindustry's

cyclicalnature.Intheshortrun,thereisnosignofanimminentpeakinmemorypricing

andprofitability–addtherecentTSMC

strongcapexguide

withleadingedgefoundry

tightnessandcapexfortheindustryissettoacceleratetowardsalltimehighsby2027-28.Semicapcompaniesgenerallyexperiencetheupturn(anddownturn)inprofitabilityonetotwoquartersaheadofmemorymakers,asequipmentordersareplacedwellinadvanceofproduction.However,stocksinflectaroundthesametimeasillustratedinExhibit1.The

shareperformancelagissignificantandamaterialcatch-upmoreprobable.

Exhibit1:ASMLvs.DRAMYoYperformance–significantlaggard

ASMLDRAM

250%

200%

150%

100%

50%

0%

-50%

-100%

Source:FactSet,MorganStanleyResearch.

Note:ThreeDRAMcompanies=SamsungElectronics,MicronandSKHynix

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Exhibit2:ASMLorderswellbelowpeaktoday

ASMLQuarterlyNetBookings(EURmillions)

10,000

MemoryLogic

9,000

8,000

7,000

6,000

5,000

4,000

3,000

2,000

1,000

1Q19

2Q19

3Q19

4Q19

1Q20

2Q20

3Q20

4Q20

1Q21

2Q21

3Q21

4Q21

1Q22

2Q22

3Q22

4Q22

1Q23

2Q23

3Q23

4Q23

1Q24

2Q24

3Q24

4Q24

1Q25

2Q25

3Q25

0

Source:CompanyData,MorganStanleyResearch

Exhibit3:Semicapexposurelandscape

Source:AppliedMaterials,MorganStanleyResearch

EUVLessonsfromHistory–TimetoPlayChess,NotCheckers

ASML–PartyLikeIt's2010.Theperiodbetween2010-12wasextremelycapacity-constrainedinEUVlithography,andsawextremelylimitedvolumesofproductionfromASMLasthesoleviableEUVtoolsupplier.AsEUVtoolswerenotyet

production-worthy,theyrequiredco-development,risk-sharingandguaranteed

demandtojustifycontinuedinvestment.Inotherwords,accesstoEUVtoolswasdeterminedlessbypriceandmorebyrisktoleranceandstrategiccommitment.Weseeasimilarpotentialset-upwithEUVcapacityincreasinglyconstrainedinto2027-28e.What'sdifferentthistimeisbothSKhynixandMicronusing6-layersofEUVforfuture1c/gamaDRAMandlotsofcashonthebalancesheetcompetingfor

toolsvs.Samsungandadvancedlogicfoundry.

SamsunghoardingEUVtools.SamsungElectronicspositioneditselfasaleadEUVpartner,byplacingearlytoolreservations,engaginginadeepengineering

collaborationandexpressedwillingnesstoabsorbpotentialyieldrisks.SamsungElectronicsendedupabsorbingadisproportionateshareofearlyEUVtool

availability,effectively'crowdingout'otherlogicplayerslikeTSMCandIntel.Asaresult,TSMCandIntelwereforcedtorelylongeronadvancedDUVmulti-

patterningtools.Thisepisodehasprecededandcatalyzedashiftfrom

transactionalequipmentpurchasingtostrategicco-ownershipofcriticalsupply-chainassets.

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Exhibit4:

SouthKoreaSemiconductorEquipmentImports

2002

2002

2003

2004

2005

2005

2006

2007

2008

2008

2009

2010

2011

2011

2012

2013

2014

2014

2015

2016

2017

2017

2018

2019

2020

2020

2021

2022

2023

2023

2024

2025

Equipments(Import)——3mmaYoY(RHS)

3,500

3,000

2,500

2,000

1,500

1,000

500

0

(%)

600

500

400

300

200

100

0

-100

-200

US$(mn)

Source:KoreaCustoms,MorganStanleyResearch

Strategicassets.In2012,ASMLannouncedacustomerco-investmentprogram

sizedat€1.38bninminoritystakepurchasesandlong-termR&Dfunding,withIntel(15%),Samsung(3%)andTSMC(5%)participatingandASMLremaining

independentwithnocontrolrightsheldbyanycustomers.Althoughthe

announcementofthispartnershipgeneratedamutedmarketreaction,thisepisodehighlightedastructuralfeatureofthesemiconductorsupplychain–controloverbottleneckcapitalequipmentcantemporarilyreshapecompetitivedynamics,evenwithoutformalexclusivity.Today,weseesimilardiscussionsaroundAdvanced

PackagingCapacity,HBMsupplychainsandFoundryco-investmentmodels.

Memoryplayers'currentengagementwithASML'sEUV(High-NAEUVtool)intheformofacquisitionandstrategicpartnership:ASMLhasbeenbuildinga

substantialresearchandsupportcampusinHwasung,SouthKorea,designedtostrengthencollaborationwithbothSamsungandSKhynix.

•SamsungisplanningtopurchasemultipleASMLHigh-NAEUVtoolswithplanstoinstallthemfor2nmfoundryproduction(notmemory).This

representsaleaddeploymentamongmajorplayersratherthanapassiveposition.

•In2025,hynixinstalledanASMLTwinscanNXE:5200BHigh-NAEUV

systematitsM16fabinIncheon,SouthKorea.ThismarksthefirstHigh-NAEUVtooldeployedformemoryproductionoutsideR&Dusage.AsthefirsttointegrateaHigh-NAEUVsysteminamassproductioncontext,SKhynixisimplicitlypartneringwithASMLonbothdeploymentanddevelopmentofnewlithographycapabilities.

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Exhibit5:ASMLoutperformed

theMSCI2010-2012

Sharepriceperformancerelativeto01/01/2010

120%

ASMLMSCI

100%

80%

60%

40%

20%

0%

-20%

-40%

Source:FactSet,MorganStanleyResearch

Exhibit6:ASMLNTMPE2010-12

ASMLHoldingNV-PE-NTM

18

16

14

12

10

8

6

Source:FactSet,MorganStanleyResearch

Memory–whythesuddenbottleneck?

MemorybecomesasignificantbottleneckforAIdevelopment.Key-Value(KV)Cacheisemergingastheprimarymemoryscalingconstraintintransformerinference.Ascontextlengthsandconcurrencyrise,KVCachememorygrowslinearly,saturatinghigh-bandwidthmemorywellbeforecomputelimitsarereached.Thismakesinferenceincreasingly

memory-boundandunderpinstheindustry'spushtowardarchitecturalandsoftware-levelmemoryefficiency.AIinferenceisfundamentallydifferentfromLLMtrainingandis

becomingincreasinglydifficulttoscale.Recentbreakthroughsandemergingusagepatternsmakeinferencemorememory-intensive,oftenrequiringadditionalhigh-

bandwidthmemoryratherthanless.CurrenttrendsthatareincreasingthememoryrequirementsofinferenceandamplifyingtheKVCacheprobleminclude:

•Context-MemoryinAgenticAIisdeeplyintertwinedwithcontext–without

context,eventhemostsophisticatedconversationbecomesmeaningless

(tendencytohallucinate).Contextconstantlyretrievespriorsharedhistorytomakesenseofthepresent.ItincreasescomputeandsignificantlymorememorydemandastheamountofinformationtheLLMmodelcanlookatrises

meaningfullywhengeneratingananswerthatisofquality.

•Reasoninggeneratesalongsequenceofthinkingbeforethefinalanswer,similartohowpeoplesolveaproblemstep-by-step.Thissignificantlyincreaseslatencyandthelongsequenceofthoughttokensstrainsthememory.

•Multimodal(images,audio,videogeneration)arelargerdatatypesthatconsumefarmorethantextgeneration.

•MixtureofExperts(MoE)expandsmemoryusagewiththeuseofmultiple

experts(forexample,ChinaDeepSeekv3has256MoE)invokedselectivelyratherthanasingledensefeed-forwardblock,whichallowsmodelsizetogrow

significantlyforhigherquality(relativetoamodestincreaseintrainingcost)andhelptraining.

MorganStanleyResearch7

Exhibit7:AIInferencePrefillvs.Decodingstages

Source:MorganStanleyResearch

TheAIhardwareracehaspivotedtowardslessglamorousmemoryfromprevious

computehorsepower.ComputedeterminesAIbutmemorynowdetermineshowfarandhowfastitcanscale.AsAIsystemsmovefurtherawayfromtrainingtowards

inference,thephysicsofperformancechangeswithAIinferenceworkloads,especially

AgenticAI,whichisincreasinglymorememory-boundthancompute-bound.MemorylivesatdifferentlayersinanAIAgentsystem(short-termworkingmemory,storedgeneral

facts,long-termmemoryspan,pre-trainedexternalknowledgebase,tooloutputs,userhistory).Asmodelsgrowinsizeandcontextwindowsexpand,thechallengeislessabouthowfastchipscancomputebuthowquicklytheycanfeeddatatothoseprocessors.

AstepchangeinAIisunderwayin2026...TheAIindustryisshiftingfromgenerativeAItoAgenticAI,with2025beingtheyearwhenwemasteredreasoningandintroduced

AgenticAI,to2026,whichwillbeaboutmovingAIfromexperimentationtocore

infrastructureandenterpriseagentadoption.Theseagentsarenowmorereliable,havestrongermemory,havefewerhallucinationsandcontinuouslearninghasbegun.Weareintheprocessoffusingfrontiermodelswithcustomizedopen-sourcesystemsrunningonenterpriseservers.Thenextendmarketwillbemuchbigger–thatisPhysical,whenwemoveintelligencefromthecloudintoindustrialAIandhumanoids.

…withadramaticimpactformemory.AgenticAIdrivesmassivedemandforDRAMandNANDbyrequiringsignificantlyhighermemorycapacityandperformancetosupportitscorefunctionsofcontext,autonomy,planningandcontinuouslearning.Weareshiftingfromreactivesingle-taskmodelstoproactive,autonomous,andcontinuouslylearning

systemsthatrequiresignificantandreliablememoryresourcestofunctioneffectively.

ThisshifthasledDRAMdemandprioritizingtheproductionofhigh-end,AI-specificmemory,drivingupoverallDRAMdemandsurgeandprices.

8

Exhibit8:AgenticAI–Memorytiersillustration

Source:TowardsDataScience,MorganStanleyResearch

Whatisthelong-termbullcaseformemory?

We'restillatthebeginning,nottheend.Despiterecentprogress,ChatGPTisonlythreeyearsold,operational1GWdatacentersdon’tyetexist,andexpertssee“nowallsinsight”forpre-training.AImodelscontinuetoexhibitsubstantialheadroomforimprovement

throughbothincreasedcomputeandefficiencygains,asdemonstratedbyrecent

breakthroughssuchasDeepSeekV4.Beyondtraditionalscaling,weareuncoveringnewoptimizationleversacrosstrainingandinference,includingdynamicreasoningdepth(howmuchamodel'thinks'beforeresponding).Atthesametime,thegrowingadoptionof

visionandmultimodalAImodelsisstructurallyincreasingmemoryrequirements,astheseworkloadsprocesshigh-dimensionalinputsandmaintainlargerintermediate

representationinfastmemory.Wehavefoundnewscalingrelationshipsinotherpartsofmodeltrainingandinference,includinghowmuchtothinkbeforeansweringaquestion.

Exhibit9:NumberofAImodelsreleased

Multimodal

Video

ImageGeneration

Vision

AllLarge-ScaleAIsystemsLanguage

180

160

140

120

100

80

60

40

20

0

2022

2023

2024

2025

201920202021

Source:OurWorldinData,MorganStanleyResearch

TheAIbuildoutkeepshittingnewinfrastructurelimits.Formemory,itisfacingthe

largestglobalscalingofanytechwaveinhistory.AIagentsarequicklybecomingacritical

MorganStanleyResearch9

bottleneckandthenextwaveofprogresswithAIagentswillnotcomefrombetter

reasoningbutratherwillcomefrombettercontexthandling.AnAIassistantthat

rememberseverythingismoreusefulthanabiggermodelthatrememberslittle.Thismeansmorememorylayerstounlockfarmorevaluethanreasoningimprovements.

Memoryandcontextmanagementareincreasinglythebottleneck.Theagent’ssourcecodeisjusttheorchestrationlayerwhereastheheavyliftinghappensinhowmemorygetsingested,organized,andretrieved.Memorysystemsarequicklybecomingthehidden

complexitybehindagents.Ifcodeusedtobethebottleneck,memorymightbethenew

one.LLMsrememberjustenoughcontexttosustainaconversation,butnotalifetimeofthem.Formanyusers,that’sfinebutforanyonewhoimaginesAIasapermanentcognitivepartneroragent,it’sastructuralfrustration.

•Storagecost:Keepingbillionsofusers’fullconversationhistories,indexedandinstantlyretrievable,wouldexplodestorageneedsandretrievallatency.

•Processingcost:Evenifthedataisstored,everyquerywouldneedmorecomputetosearch,rankandcontextualize.Thattranslatesdirectlyintohigherper-promptcosts.

•Hardwarecost:AtypicalAIserveruses8Xmorememorythantraditionalservers,andwitheachgeneration,thatnumberclimbsevenhigher.

Innovationandhigherefficiency.DeepSeekrecentresearchpaper

'ConditionalMemory

viaScalableLookup:ANewAxisofSparcityforLLMs'

demonstratesapathwaytoscalingmodelcapacitybeyondHBMlimitsbydecouplingreasoningfromknowledgestorage.Inthisarchitecture,latency-criticalreasoningremainsresidentinHBM,whilelarge,less

frequentlyaccessedknowledgememoryisoff-loadedtoCXL-attachedDDR5.Thiseffectivelyintroducesmemoryasanewaxisofsparsity,enablingmeaningfulmodelscalingevenasHBMcapacityremainsconstrained.

MoreNAND–NVIDIAInferenceContextMemoryStoragePlatform.AtCES,Nvidia

showcaseditsinferencecontextmemorystorageplatformwhichservesasaKVCacheforinferencing.ThisplatformispoweredbytheNVIDIABlueField-4DPU(DataProcessing

Unit)andinsertedanothertierofeSSDstorage,whichmanagestheoff-loadingand

sharingofKVCachedata.Inatypicalconfiguration,thisinfrastructureallowsforan

additional16TBofhigh-speedSSDstorage(presumablyNVMe)tobedirectlyassociatedwitheachRubinGPU,functioningasanextensionofthesystem'smemoryhierarchytohandleextremelylargecontextlengths.WithDPUmovingfromBlueField-3to4,DRAMcontentalsoupgradedfrom32GBDDR5to128GBLPDDR5X.

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Exhibit10:NVIDIAInferenceContextMemoryStoragePlatform

Source:NVIDIACES2026KeynoteContextStorage

SizingtheInferenceTAM

Itisalmostimpossibletocalculatetheexactdemandonmemorygiventhechanging

dynamicsonusergrowth,applications,andtechnologyinnovation.Whatinvestorsare

primarilytryingtounderstandishowmuchincrementalTAMcanbeaddedasKVCacheexpands.Currently,mostofthehotKVCacheisstoredinHBM/DRAM,whichmeansnotonlythatitisexpensivebutalsothememorydurationwillbeshort.AllowingKVCachetobeoffloadedtoeSSDmakeslongercontextandlongermemorydurationpossible,whichcaninturnsignificantlyenhancecurrentapplicationsandcreateabetterinferencesetupforAgenticAIdevelopmentandpenetration.

In

Exhibit11

,wecalculatedthetieredmemoryusageofaChatGPTlikemodelwithkeyassumptionsbelow(fullassumptionsin

Appendix:MemoryUsageTierBreakdown

Assumptions

):

•Weassumed800millionweeklyactiveusers(PeakQPS300,000req/s)

•Inputtokens/request:2,000tokens

•WeassumedliveKVis50%/50%splitonHBMandDRAMandreusedKVCacheis40%onDRAMvs.60%onNAND.(KVCacheinFP16precision)

•Assumedtextonlyapplications;image/videodemandisnotconsideredinthismodel

•Westicktotheindustrycurrentcommonpracticefortherestofourassumptionstotheextentpossible

Inconclusion,fora200,000GPUclusterthatrunssuchamodel,theHBMusageis

around200PB,DRAM4EB,NAND42EBonwarmdata/KVCacheoffloadanddatalake

demandisaround260EB.Ifweassumegloballytherearethreesuchmodels,thetotalAIinferencedemandwillaccountfor17%,35%and92%of2026globalmemorysupplyrespectivelyforHBM,DRAMandNAND.

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Exhibit11:TieredMemory/StorageUsageBreakdown

TierComponentTotal(TiB)Total(PiB)Notes

HBM

LiveKV(hot)

330

0

50%ofliveKV

DRAM

LiveKV(warm)

330

0

50%ofliveKV

RackSSD

LiveKV(offload)

-

-

DRAM

KVreusecache(24h,50%)

4,169,941

4,072

Time-integratedretainedKV40%inDRAM,60%inRackSSD

RackSSD

KVreusecache(24h,50%)

6,254,911

6,108

Time-integratedretainedKV40%inDRAM,60%inRackSSD

HBM

GPUoverheads

18,750

18

(workspace+runtime+sidemodels)

HBM

Weightsonactiveexperts(top-2/128)

186,731

182

Setinputifknown

DRAM

Hostbuffers/state

0

0

per-seqhoststate

RackSSD

LocalRackSSDcaches/logs

36,379,788

35,527

200TB/node(decimal)

HBMTotal

226,291

226

DRAMTotal

4,585,261

4,585

RackSSDTotal

46,877,348

46,877

DatalakeRAG+logs+caches(central)

293,556,000

293,556

Source:MorganStanleyResearchestimates

Note:detailedassumptionsinAppendix

ExtendingcontextlengthandincreasingtheKVCachewillhavethemostobvious

incrementalupliftonDRAMandRackSSD(assumingEffectivelivewindowcappedthe

usageofHBMandDatalakeismorerelevanttomodelsize,RAGandotherfactors).By

increasingtheinputtokensfrom2000tokens/requestto5,000tokens/requestholdingotherassumptionsunchanged,theincrementalKVCachewillincreasetheDRAMdemandbyaround2EBandRackSSDby3EBpermodel.

Exhibit12:Sensitivitytestoninputtoken/requestpermodel

Inputtokens/requestHBMTOTAL(PB)DRAMTOTAL(PB)RackSSDTOTAL(EB)DatalakeTOTAL(EB)

2,000

226

4,585

47

294

5,000

226

6,648

50

294

10,000

226

10,087

55

294

20,000

226

16,964

65

294

50,000

226

37,597

96

294

100,000

226

71,983

148

294

200,000

226

140,757

251

294

Source:MorganStanleyResearchestimates

Exhibit13:Inference-stagestoragebymemorytier

Storagetier

Whatliveshereinpractice

HBM(GPUmemory)

•Modelweights(activeshards)

•HotKVcache(recenttokensonly,typicallylast256–1,024tokens)

•Temporaryactivations/workspaces(attentionscratch,GEMMbuffers,logits,NCCLbuffers)

•Runtimemetadata(KVblocktables,schedulers,CUDAgraphs)

•Occasionallysmallsidemodels(safetyfilters,routers)

HostDRAM(CPUmemory)

•WarmKVcache(paged/offloaded)

•EvictedKVblocksfromHBM(majorityofKVatscale)

•Request/sessionstate(tokenqueues,schedulers,batchingmetadata)

•Prompttext+tokenizedinputs

•OptionalCPU-residentweights(rareforhigh-throughputchat)

Local/in-rackNAND(NVMe/SSD)

•ColdKVspill(emergencyorbatch/offlineinferenceonly)

•KVcheckpointsforlongjobs

•Localcaches(promptcache,embeddingcache)

•Node-levellogsandshort-termtelemetrybuffers

Datalake(sharedstorage)

•Modelstorage(checkpoints,shardedweights,multiplevariants)

•KVspillforoffline/recovery(notlatency-sensitive)

•RAGcorporaandindices

•Logs,telemetry,traces

•Long-livedcaches(promptreuse,embeddings,evaluationartifacts)

Source:MorganStanleyResearch

What'schanged?

Pricehikeseverywhere.Givenrecentbuyer-sellernegotiations,weseeDRAMpricing

momentumtoremainexceptionallystronginto1Q26,withupsiderisktoitspriorforecast.GlobalDRAMmarkethasenteredaphaseofdramaticpriceinflation,drivenprimarilybythethreemajorsuppliersreallocatingcapacitytowardhigh-marginserverDRAMandHBM

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tomeetrobustAIinferenceandinfrastructuredemandfrommajorCSPs.Thisshiftshas

createdasevere'capacitycrowding-outeffect'forPC,mobile,andconsumerDRAM,

resultinginafirmlyentrenchedhigh-price,low-volumeseller'smarket.WithUSCSPswillingtoabsorbsubstantialpriceincreasesandactivelysigninglong-termcontractstosecuresupply,