摩根士丹利 -半导体：中国AI加速器-谁有望胜出 China's AI Accelerators – Who's Poised to Win

MorganstanleyRESEARCH

April26,202609:50PMGMT

GreaterChinaSemiconductors|AsiaPacific

China'sAIAccelerators–Who's

PoisedtoWin?

WeinitiatecoverageonCambriconandIluvataratOverweight,andMetaXatEqual-weight.

Bullvs.bearcasesforChinaAIaccelerators:WhowillcaptureChina’sinference-drivensubstitutioncycle?Ina

recentreport,

wearguedthatChinaisnarrowingtheUSleadinAIcomputenotsimplyatthechiplevelbutalsothroughsystem-level

innovation,supply-chainlocalization,andincreasinglyattractiveinference

economics.WecontinuetobelievethispathwillliftChina’sdomesticAIacceleratorself-sufficiencyrateto86%by2030,reshapingtheglobalcompetitivelandscapeforAIsemiconductorsoverthenextdecade.

Overthepastmonth,ourchannelcheckshaveturnedincrementallymore

constructiveonChina’sAIacceleratorindustry.1)AtourChinaSummit,majorAILLMdeveloperssuchasMiniMaxandZhipusignaledtheirwillingnesstoadopt

domesticAIchipsaslongastokeneconomicsarecompetitive.2)OurrecentfieldtripindicatedthattheNvidiaGPUsupplyinChinatightenedafterthe

SMCI-related

disruption,

redirectingincrementaldemandtowarddomesticalternatives.3)StrongspotdemandforNvidiaRTX5090inChinasuggestsAIinferencedemandremainsrobust.4)RisingtokenpricesandGPUrentalpricesalsopointtoastill-tight

computemarket.Themainnegativedatapoint:pricecompetitionappearstobe

arrivingearlierthanwehadexpected,assomevendorshavestartedcuttingpricestogainshare.5)WAICwillbeheldinShanghaiinJuly2026,whereweexpecttoseenext-generationChineseAIacceleratorproducts,especiallyfromIluvatar.

Stockcalls:WeinitiatecoverageonCambriconandIluvataratOverweight,andMetaXatEqual-weight.WebelieveallthreearewellpositionedtobenefitfromChina’sacceleratingAIchiplocalizationtrend,thougheachoffersadifferentiatedinvestmentcase.

•Cambricon(Overweight;PTRmb1,588):WeviewCambriconastheleadingdomesticAIinferencechipplay,supportedbystrongCSPcustomer

anchoring,provenhardware-softwareco-optimization,andsolidpositioninginlarge-scalecloudinferencedeployments.

•Iluvatar(Overweight;PTHK$600):WeviewIluvatarfavorablyforitsdiversifiedsupplychainstrategy,strongersupplyvisibility,andgrowingexposuretocloudcustomers.

•MetaX(Equal-weight;PTRmb758):WeseeMetaXasadifferentiated

domesticGPGPUvendor,withrelativelystrongCUDA-likesoftware

compatibility,andamorescalablenear-termmanufacturingpath.However,itsvaluationislessattractivethanpeers.

Keyriskstoourviewinclude:1)slowerAIdemandgrowth,2)earlier-than-expectedpricingpressure,3)policy/exportheadwinds.

FouNDAtioN

MoRGANSTANLEyTAiwANLimiTEd+CharlieChan

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EquityAnalyst

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AsiaPacific

IndustryViewAttracti∨e

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companiescoveredinMorganStanleyResearch.Asaresult,investorsshouldbeawarethatthefirmmayhaveaconflictofinterestthatcouldaffecttheobjectivityofMorganStanley

Research.InvestorsshouldconsiderMorganStanley

Researchasonlyasinglefactorinmakingtheirinvestmentdecision.

Foranalystcertificationandotherimportantdisclosures,refertotheDisclosureSection,locatedattheendofthisreport.

+=Analystsemployedbynon-U.S.affiliatesarenotregisteredwithFINRA,maynotbeassociatedpersonsofthememberandmaynotbesubjecttoFINRArestrictionson

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ExecutiveSummary–Don'tUnderestimateChina'sAl

ComputeEcosystem

BoomingChinaAIGPUmarket

China’sAIGPUmarketisenteringamorecommerciallygroundedphase,withthedebateshiftingfromwhetherdomesticchipscanparticipatetowhichvendorswillwinmeaningfulshareasinferencedemandscales.Inourview,twostructuralforcesshapethemarket:(1)arapidriseinAIinferencedemand,drivenbycommercializationacrossconsumerand

enterpriseapplications,and(2)persistentexportcontrols,makinglocalizationalong-

durationfeatureofChina’sAIcomputemarketratherthanatemporarypolicyresponse.Together,theseforcesexpandtheaddressablemarketfordomesticAIacceleratorsand

improvetheprobabilityofsustainedsubstitution.Thisalignswithourframeworkthat

China'sAIchipTAMcouldreachUS$67bnby2030,withdomesticself-sufficiencyrisingto86%.

OurcorethesisremainsthatChina’slocalizationstrategyisgainingtraction:scaling

domesticchips,foundries,packaging,andequipmentcapabilitiestopartiallyoffset

process-nodedisadvantages.Inthebullcase,domesticAIsemisbroadenfrominference

intoselectedtrainingworkloads,softwareecosystemsimprovefasterthanexpected,andsomevendorsachieveoverseasadoptionorindirectexportopportunities.Inthebearcase,productdifferentiationfades,pricingpressureintensifiesearlierthanexpected,andthe

sectormovestowardcommoditizationandconsolidation.

Ehibi2ChiAllⅠd

Exhibit1:ChinaAlacceleratorsⅠmarketcaptrend

50

-

Jul-25Aug-25Sep-25Oct-25Nov-25Dec-25Jan-26Feb-26Mar-26

CambriconHygonMetaXMooreThreadsBirenIluvatarCoreX

300

250

200

150

100

(US$bn)

Source:FactSet丿MorganStanleyResearch

xt:naacceeratorsrevenuetren

Source:Companydata丿MorganStanleyResearchestimates

MoRGANSTANLEyREsEARcH3

Exhibit3:

"10Dragons"ofChineseAIGPGPUvendors.WefocusonCambricon,MetaX,Iluvatarcompanyresearchinthisreport

Source:Companydata,MorganStanleyResearch

China'sAIcomputeindustrycanbecompetitiveglobally,givenstrongsystemdesignandinfrastructure

Morebroadly,webelieveChina’sAIGPUraceisnolongerjustachip-specificationcontest.WhiledomesticsiliconstilltrailstheUSbyroughlytwogenerationsatthechiplevel,theeffectivegapisnarrowingthroughmulti-diedesign,advancedpackaging,rack-scalesystemarchitecture,opticalnetworking,andsoftware-hardwareco-optimization.Thisiswhywethinksystem-levelcompetitivenessmattersmorethanever.Inamarketincreasingly

dominatedbyinferenceandutilization,thevendorthatdeliversthebestreal-worldtokeneconomicsatacceptablesoftwaremigrationcostislikelytowincustomerbudgets,evenwithoutleading-edgeprocesstechnology.

Fromaninvestmentperspective,thisleadstoasimpleconclusion:thesectorshouldnotbevaluedasamonolithicpolicytheme.Instead,investorsneedtodistinguishbetweenvendorswitharealisticpathtoshipmentscale,ecosystemcredibility,andpricing

discipline,andthosethatmaystruggletoconverttechnicalpotentialintodurable

revenuesandmargins.Wethereforeevaluatethegroupthroughatwo-dimensional

frameworkofeconomics×execution,combiningTCO,tokencost,TPS,andperformance-per-dollarwithqualitativefactorssuchasfoundryaccess,softwareecosystemmaturity,CSPrelationships,androadmapcredibility.Inourview,thatframeworkremainsthemosteffectivewaytoseparatelikelywinnersfromthoseatriskofbeingmarginalizedastheindustryconsolidates.

Whathasbecomeclearerthroughourrecentchannelchecksisthateconomics,not

ideology,isdrivingadoption.AtourChinaSummit,majorLLMdevelopersindicatedtheyarewillingtodeploylocalGPUsaslongastokencostiscompetitive.Thisalignswithour

4

corefindingthatdomesticacceleratorsalreadyoffermateriallylowerTCOthanNvidia

productsavailableinChinaandthatleadingChinesechipscanachievebroadcost-per-

tokenparityininferenceworkloads.Inotherwords,purchasingdecisionsareincreasinglymadeondeployableeconomicsratherthanabsolutepeaksiliconperformance.This

mattersbecauseChina’sAIdemandisbecomingmoreinference-heavy,morerecurring,andmoreutilization-driven,whichstructurallyfavorssolutionsoptimizedaroundcost

efficiency,softwareadaptation,andavailabilityratherthanheadlinebenchmarkleadership.

Exhibit4:RelativestrengthsofUSandChinaAIindustries

Chippackaging

Memory:HBM,LPDDR5

Waferfront-end

10

8

6

4

2

0

Policysupport

Powersupply

AIdatacenterspaceServersystem

Softwareoptimization

(LLM)

Opticalnetworking

ChinaUS

Source:MorganStanleyResearch

Exhibit5:DomesticchipshavelowerTCOandcomparablepertokencost(AILLMinference)vs.NVIDIA'sprocessorsforChina

-ChinaAIchipTCOcouldbe30-60%lessthanNvidia'sAIprocessors.

-PertokencostoftopChineseAIacceleratorscanmatchwithorsurpassNvidia'sprocessorsforChina.

H200A100H20910B910C950PRMLU370MLU580MLU590MLU690C500C600S5000BI-V100BI-V200PPUP800

NVIDIAHuaweiCambriconMetaXIlluvatarT-HeadKunlun

TCOof10MWcapacity(US$mn)Pertokencost(US$cent)(Right-axis)(Lowerisbetter)

0.0050

0.0040

0.0030

0.0020

0.0010

0.0000

90.00

80.00

70.00

60.00

50.00

40.00

30.00

20.00

10.00

-

0.0060

Source:Companydata,MorganStanleyResearchestimates

Who'spoisedtowininChinaAIGPUs?

China’sAIacceleratorecosystemspansmerchantchipvendors,sovereign-backedplayers,andcaptivechipdesignhouseslinkedtomajorcloudserviceproviders(CSPs).Weassessthisecosystem'scompetitorsinaglobalGPU/ASICcontext,comparerelativepositioningacrossperformance,cost,andexecution,andapplyaconsistentvaluationframeworktoidentifystockswiththemostattractiverisk/reward.OurfieldworkwithCSPssuggeststhatwhileper-tokencostisthesinglemostimportantKPI,softwareoptimizationand

MorganstanleyRESEARCHFouNdAtioN

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strategiccustomerpartnershipsmatterevenmorethanwehadassumed.

Basedonrecentshipmenttrends,customerallocation,marketshareevolution,andthe

earlier-than-expectedonsetofpriceerosion,webelievethenextphasewillbedefinedlessbytheoreticalpeakperformanceandmorebycommercialexecution,softwarereadiness,andcustomercapture.

Fromacompetitiveperspective,webelievethemarketshouldbesegmentedbycustomertype.Fortop-tierCSPsandleadingLLMdevelopers,theprimarydecisionmetricis

increasinglyper-tokencost,butthatKPIaloneisnotsufficient.Inpractice,software

maturity,frameworkcompatibility,cluster-leveloptimization,andthedepthofstrategic

partnershipsplayadecisiveroleinorderallocation.ForsovereignAI,telecom,SOE,and

government-linkeddemand,supplysecurity,domesticcontrollability,andpolicyalignmentcarrygreaterweight.Thiscreatesroomfordifferentwinnersacrossend-markets.Inour

view,vendorswithstrongCSPco-developmentrelationshipsandcrediblesoftwarestacksarebetterpositionedtowinhigh-volumecloudinferencedeployments,whilevendorswithstrongerdomesticsupplychainvisibilityorgovernmentrelationshipsmaybebetter

positionedinsovereignandpublicsectorprojects.

Exhibit6:OrderplacementandpotentialordersfordomesticAIacceleratorsdevelopers,accordingtoourindustrychecks

Source:MorganStanleyResearch

Withinthisframework,weseemeaningfuldifferentiationamongCambricon,MetaX,

Iluvatar.CambriconstandsoutontheASIC/DSApath,whereitsinferenceperformance,customeranchoring,andhardware-softwareco-optimizationsupportstrongdeploymenteconomics,particularlyinlarge-scalecloudusecases.Iluvatarisdifferentiatedbyits

diversifiedfoundrystrategy,bettersupplyvisibility,andapragmaticpathtocustomermigrationthroughsoftwarecompatibility.MetaXisoneofthemorecredibledomesticGPGPUplayers,inourview,duetorelativelystrongerCUDA-likesoftwarecompatibilityandamanufacturingpaththatmayprovemorescalablenearterm.

Inshort,Cambriconlooksstrongestoncurrentcloudinferencetraction(secondonlyto

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6

HuaweiAscend),Iluvataronsupply-chainresiliencepluscommercialoptionality,and

MetaXonscalableGPGPUpositioning.

Near-termmarkettrackerforChinaAIGPUdemand

Atthesametime,near-termindustryconditionshaveturnedmorefavorablefordomesticvendors.OurrecentfieldtripsuggestsNvidiaGPUavailabilityinChinahastightened,

creatingmoreroomforlocalsubstitution.Strongspot-marketdemandforNvidia5090

cards,alongsidehighertokenpricesandGPUrentalprices,alsopointstoresilient

downstreaminferencedemand.Thesedatapointsreinforceourviewthatthedemand

environmentremainsrobust,especiallyforcustomersthatneedimmediatedeploymentratherthanwaitingforsupplynormalization.Thecaveat:competitionisintensifyingfasterthanexpected.Wearealreadyseeingpricecutsinpartsofthemarket,implyingthesectormaymoveintoamarket-sharephaseearlierthanwehadassumed.Asaresult,weexpectexecutionqualitytomattermorethanever–particularlyinsoftwareoptimization,

customersupport,andstrategicaccountpenetration.

TheWorldArtificialIntelligenceConference(WAIC)willbeheldinShanghaiinJuly2026,whereweexpecttoseenext-generationChineseAIacceleratorproducts,especiallyfromIluvatar.

Exhibit7:Nvidia's5090pricekeepsrisinginChina

NVIDIAgaminggraphiccardpriceinChina

35,000

30,000

25,000

20,000

15,000

10,000

4090DistributorpriceonTaoBao(Rmb)5090DDistributorpriceonTaoBao(Rmb)

4090Tagprice(Rmb)

5090/5090DTagprice(Rmb)

Source:Taobao,MorganStanleyResearch

Exhibit9:SurgeinByteDance(VolcanoEngine/Doubao)tokensindicateshighAIdemand

Monthlytokensprocessed

2,400

2,100

1,800

(trillion)

1,500

1,200

900

600

300

-

Apr-24Jun-24Aug-24Oct-24Dec-24Feb-25Apr-25Jun-25Aug-25Oct-25Dec-25

Bytedance(VolcanoEngine/Doubao)

Z.ai

(GLM)China(NationalDataAdministration)

Source:Companydata,MorganStanleyResearch.ByteDancenumbersrepresentmonthlyrun-ratebasedondailynumbers.

Exhibit8:AveragetokenpriceforChina'smainstreamAILLMs

Input(Rmb/mntoken)Output(Rmb/mntoken)

20.0

18.0

16.0

14.0

12.0

10.0

8.0

6.0

4.0

2.0

0.0

18.118.1

12.7

13.4

12.2

3.3

4.13.9

2.8

2.2

1Q252Q253Q254Q251Q26

Source:Companydata,MorganStanleyResearch

Exhibit10:ChinaCSP'scapexwillbeakeydemanddriverforChinaAIGPU

Source:Companydata,MorganStanleyResearch(E)estimates

MoRGANSTANLEyREsEARcH7

ldentifyingWinnersWithOurFramework:Cambriconand

lluvatarAreOurPreferredPlays

Buildingonourperformanceandcostanalysis,weapplyastructuredframeworktoassessdomesticAIchipvendors’relativepositioning,focusingonquantitativeeconomicsand

qualitativeexecution.

Ourframework:economics×execution

Weevaluatevendorsacrosstwokeydimensions:

•Inferenceeconomics(quantitative)–includingTCO,costpertoken,TPSperformance,andperformanceperwatt/dollar

•Executioncapability(qualitative)–includingaccesstoleading-nodecapacity,softwareecosystemmaturity,depthofCSPrelationships,andproductroadmapsoundness

Inourview,sustainedleadershiprequiresstrengthinboth.Vendorsthatexcelinonlyone

–e.g.,stronginsiliconbutwithaweakecosystem–areunlikelytoachievedurableshare.

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Exhibit11:ComparisonamongCambricon,MetaXandIluvatar

8

Ticker688256-SH688802-SH9903-HK

Product

MLU220/270/370/580/590/690

(AITraining+inference)

CSeries(AItraining+inference)

NSeries(AIInference)

GSeries(Graphicrendering)

Tiangai100/150/200/300(AItraining+inference)

Zhikai100(AIinference)

GPGPU/ASICASICGPGPUGPGPU

Chipsuppliers

7nm/N+212nm/N+17nm

Processnodeforlatestproducts

majorCSPs

Securedordersfrom✔X✔

Sovereignfundasmajorshareholder

X✔X

Pertokencost

performance

2025Revenue(Rmbmn)

CNY6,497

CNY1,644

CNY1,034

Profitablity✔XX

Source:Companydata,MorganStanleyResearch

Cambricon:Leadingininferenceperformanceandcustomeranchoring

Withinthisframework,weseeCambriconasoneofthestrongestpositionedplayersontheASIC(DSA)pathway.

Quantitatively,Cambricon’slatestgeneration(e.g.,MLU590)deliverscompetitive

inferenceperformance,withourTPSanalysissuggestingmeaningfuloutperformancevs.NVIDIAH20undercertainDeepSeekR1scenarios.Combinedwithcompetitivepricing,thissupportsstrongcost-per-tokeneconomics,whichweviewasthekeyCSPdecisionmetric.

Qualitatively,Cambriconbenefitsfromdeepcustomerintegration.Basedonourindustrychecks,itsmulti-yearcollaborationwithByteDanceenabledcontinuoushardware–

softwareco-optimizationandreal-worlddeploymentvalidation,providinganadvantageinapplication-leveltuningandcommercializationreadiness.

Takentogether,weviewCambriconasanear-termleaderininference-drivendeployments,particularlywhereefficiencyandcustomer-specificoptimizationarecritical.

Iluvatar:Leveragingsupplychainresiliencewithstrongorder

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visibility

WebelieveIluvatariswellpositionedtobenefitfromacceleratingdomesticAIchip

MoRGANSTANLEyREsEARcH9

substitutioninChina,supportedbysupplychainresilience,softwarecompatibility,and

improvingcommercialtraction.

Basedonourindustrychecks,leadingChineseCSPshaveplacedsizablepre-ordersfor

Iluvatar'sTianGai-150AIchips,withshipmentsexpectedtocommencein2H26.

Importantly,Iluvatar’sdiversifiedfoundrystrategy–includingexport-compliant

productionatTSMC–offersgreatercapacityvisibilityvs.peersrelyingsolelyondomesticfabsornon-compliantmanufactureoverseas,reducingsupplydisruptionrisk.

Onsoftware,Iluvatar’sGPGPUarchitectureoffershighCUDAcompatibility,lowering

migrationfriction.ThecompanyhashelpedclientstomigrateLLMstacksfromNVIDIAplatformstoTianGai-150.Inourview,thispositionsIluvatarfavorablyasenterprisesseekpragmaticNVIDIAalternatives.

MetaX:Positioningforscalabilitythroughsoftwareandsupply

WithintheGPGPUpathway,weviewMetaXasacredibledomesticparticipant,supportedbyitsfocusonimprovingCUDAecosystemcompatibility.WhileCUDAremainsNVIDIA’skeymoat–givendeepintegrationacrosscompilers,libraries(e.g.,cuDNN,NCCL),andalargedeveloperbase–italsocreatesstructuralswitchingcoststhatarenoteasily

replicated.

Againstthisbackdrop,MetaX’sstrategyofbuildingaCUDA-likesoftwarestackand

compatibilitylayerprovidesareasonableadoptionpathwayfordomesticcustomers.

Basedonourindustrychecks,thecompanyhasmadesteadyprogressincompiler

adaptation,frameworkcompatibility(e.g.,PyTorch),andruntimeoptimization,althoughoverallecosystemmaturityandstabilitystilllaggloballeaders.

Inaddition,MetaXadoptsapragmaticmanufacturingstrategy,leveragingrelatively

maturenodes(e.g.,N+1/12nm)tosupportyieldstabilityandsupplyavailability.Whilethismaylimitpeakperformancevs.leading-edgeproducts,itoffersamorebalancedtrade-offamongperformance,cost,andmanufacturability.Overall,weviewMetaXasacompanywithimprovingexecutionandscalabilitypotential,thoughfurthervalidationinlarge-scalecommercialdeploymentsremainsakeyfactortomonitor.

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PerformanceandCost:WhichDomesticAlChipsStandOut?

10

Inferenceeconomicsmoreimportantthanfoundationmodel

trainingforChinaAIGPUmarket

Inaprior

ChinaAIInsightsnote,

weconductedacomprehensivecomparisonofdomesticAIacceleratorsacrosskeyperformanceandeconomicmetrics,includingtotalcostof

ownership(TCO),totalprocessingperformance(TPP),tokenoutputpersecond(TPS)underDeepSeekR1inference,andperformanceperwatt.

1.TPS-therevenue

WebelieveTPS(tokenspersecond)isanotherrelevantmetricforChina’s

inferencedrivenmarket.UnlikepeakFLOPS,TPScapturesendtoendsystem

performance,reflectinghardwarecapability(computethroughput,memoryand

interconnectbandwidth)andsoftwareefficiencyunderrealworldworkloads.Using

DeepSeekR1asourbenchmarkandcalibratingassumptionsagainstNVIDIA’sdisclosedH200result(5,899TPSinFeb2025),wefindthatleadingdomesticaccelerators–suchasHuawei’sAscend950PR/DTandCambricon’sMLU690–canoutperformNVIDIAH20by50-150%inourscenarios.Thisreflectsimprovementsincomputecapabilityand

systemleveloptimizationandcomputetonetworkbalance.

Domesticvendorshavemademeaningfulprogressthroughmemory,interconnect,andsystemarchitectureimprovements,enablingcompetitiveinferenceperformancedespiteprocessnodedisadvantages.Thisreinforcesourviewthatperformanceleadershipisincreasinglyworkloaddependent,withdomesticchipsalreadycompetitiveininferencescenarios,evenasNVIDIAmaintainsanedgeatthetechnologyfrontier.

Exhibit12:TPS(tokenspersecond)analysisforChinaAIaccelerators

AIinferenceTPSperAIacceleratorwithDeepSeekR1(Tokens/s)

2,063

538

247

MLU690BW1000C500C600S4000S5000

HygonMetaXMooreThread

956942

170

MLU370MLU580MLU590Cambricon

7,000

6,000

5,000

4,000

3,000

2,000

1,000

-

P800PPUR300AP800I20L600

T-HeadKunlunEnflame

1,179

932

226

1,521

932

625

327

BI-V100BI-V200Illuvatar

1,536

910CHuawei

1,365

A10