2026碳感知生成式AI研究报告-面向可持续大模型推理的一体化优化与调度

Carbon-awaregenerativeAI

IntegratedoptimizationandschedulingforsustainableLLMinferencing

AngshumanMukherjee,EnterpriseArchitect,CTOTeam,GlobalCGO

1|◎2026NTTDATA,Inc.

Caron-awaregenerativeAI

Contents

03Executivesummary

04Astract

05Introduction:Thepost-trainingenergycrisis

07Thescalingwall:Whytransformersstrugglewithsustainaility

09Modeloptimization:Shrinkingthecomputationalfootprint

12Datacenterefficiency:PUEandWUEforAIworkloads

15TheCPAS-Gframework:Caron-awarescheduling

18CPAS-Gsimulationmethodologies:VidurandVessim

19Caron-intensityarchetypesandschedulingstrategies

21Adaptivemodelselectionandsystemarchitecture

24Ethicalandregulatorycontext

25AstrategicroadmapforuildingsustainaleAI

27Conclusion

28Listofareviations

29References

2|©2026NTTDATA,Inc.

3|◎2026NTTDATA,Inc.

Carbon-awaregenerativeAI

1.Executivesummary

AsgenerativeAI(GenAI)transitionsfromexperimentalresearchintoenterprise-

wideproduction,itsenvironmental

footprinthasbecomeaninescapable

operationalreality.Thehighenergy

demandoflargelanguagemodels(LLMs),particularlyduringtheinferencing

phase,posesadirectthreattocorporatesustainabilitycommitmentsandglobalclimatetargets.

Thispaperpresentsacomprehensive,multilayered

technicalstrategytodecarbonizegenerativeAIdeployments

withoutcompromisingservicequality.Bycombiningdata

centergreeningmethods(PUEandWUEoptimization)

architecturalinnovations(state-spacemodels),advancedmodelcompression(quantizationandpruning),andthenovelCarbon-awareandPrecedence-awareScheduling

forGenerativeAI(CPAS-G)framework,organizationscanachievea30%to47%reductionintotalcarbonemissions.

1.1.Keycontributions

Thekeycontributionsexpectedtobemadebythisresearchareasfollows:

·CPAS-Gframework:Aconstraint-awareschedulingsystemthatrespectsdirectedacyclicgraphs

(DAG)-structuredtaskdependencieswhilemaximizinguseoflow-carbonenergywindows

·Architecturaltransitionframework:Amethodologyforhybridizingtransformerandstate-spacemodel

(SSM)architecturestoachievelinearcomplexityonlong-contextqueries

·Modelcompressionpipeline:Amultistagequantizationandpruningworkflowdelivering50%to70%energyreductionwithminimalaccuracydegradation

·PUEandWUEoptimization:Recommendationofmultipleoptimizationtechniquesforliquidcoolingandair-

conditioningofdatacenterswithatargettominimizePUE&WUE

Keyfinding:A30%to47%reductionin

totalLLMinferencingcarbonemissionsisachievablethroughathree-layerstrategycombiningarchitectureselection,modelcompressionandgrid-awarescheduling,withoutdegradingservicequality.”

·High-fidelitysimulationvalidation:ResultsvalidatedusingtheVidur-Vessimco-simulationstackonAlibabaClusterproductiontraceswithreal-worldcarbon

intensitydata

·Regulatoryalignment:ExplicitmappingtoEUAIActArticles53and55andCSRDreportingrequirements

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2.Abstract

Thisresearchinvestigatesthesynergisticimpactofdatacentercooling,modelarchitecture,optimizationandgrid-awareschedulingonthesustainabilityofgenerativeAI.TheCPAS-GframeworkorchestratescomplexAIworkflowsacross

time-varyinggridcarbonintensities,usingaconstraint-basedschedulingapproachthatrespectsreal-worlddependencystructures.

Recommendedvalidationthroughhigh-fidelityco-simulationusingVidur(LLMinferencesimulator)andVessim

(powergridemulator)demonstratesthatamultilayeredoptimizationapproacheffectivelymitigatesbothoperationalandembodiedemissions,providingascalableblueprintforsustainableAIinfrastructure.

Thetablebelowliststhekeytopicsreferredtointhispaper,andtheirrelateddomains.

Keyword

Domain

carbon-awarecomputing

Systemsdesign

greenAI

Sustainability

largelanguagemodels(LLMs)

AI/MLarchitecture

state-spacemodels(SSMs)

AI/MLarchitecture

quantization/pruning

Modeloptimization

sustainableinferencing

Cloudinfrastructure

directedacyclicgraphs(DAG)schedulingWorkfloworchestration

Caron-awaregenerativeAI

3.Introduction:

Thepost-trainingenergycrisis

3.1.Theinferencingdominanceshift

ThenarrativeofAI’senergycrisishashistoricallycenteredontrainingandthemassive,one-timeexpenditureofcomputetouildamodelfromscratch.However,asAImodels

entercontinuousproductionoperation,thisnarrativehas

fundamentallyshifted.Inferencing—continuouslyserving

modeloutputstoend-users—nowrepresentsthedominantportionofanAIsystem’slifetimecaronfootprintatscale.

WhileasingleLLMqueryconsumesonly0.3to1.0Watt-

hoursofelectricity,gloaldeploymentaggregatesthisintoacolossal,sustainedgriddemand.Millionsofdailyqueriesdriveround-the-clockenergyconsumptionfromdatacenterclustersthatfrequentlyrelyoncaron-intensivegridpower.Currentperformance-firstschedulingparadigmsprioritizelatencyaoveallelse,systematicallyignoringthereal-time“cleanliness”oftheenergysourcepoweringeachinference.

Figure1showshowenergyconsumptionininferencing

dominatesadeployedGenAIworkload.

Industrydatapoint:AsingleChatGPT-

levelqueryconsumesapproximately

10timesmoreelectricitythanastandardwesearch.1At10millionqueriesperday,thisamountstoroughly10MWhofdailyconsumptionfrominferencingalone.”

Trainingenergyandinferencingenergyoverathree-yeardeploymentlifecycle

InferencingenergycompoundstodominatetotalconsumptionfromYear2onward

450

400

Energyconsumption(MWh)

350

300

250

200

150

100

50

0

Year1,Q1Year1,Q2Year1,Q3Year1,Q4Year2,Q1Year2,Q2Year2,Q3Year2,Q4Year3,Q1Year3,Q2Year3,Q3Year3,Q4

Trainingenergy(MWh)Inferencingenergy(MWh)

Figure1:Trainingenergyandinferencingenergyoverathree-yeardeploymentcycle.

InferencingenergycompoundstodominatetotalconsumptionfromYear2onward.

1GoldmanSachsResearch,“AIispoisedtodrive160%increaseindatacenterpowerdemand,”GSGloalInvestmentResearch,2024.

5|©2026NTTDATA,Inc.

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3.2.ThetwodimensionsofAIcarbonemissions

AcompletesustainabilityaccountingforAIsystemsmustconsidertwodistinctemissioncategories:operationalandembedded.Table1presentsthedefinitionsandprimaryleversofthesecategories.

Table1:EmissioncategoriesforAIsystems

Emissiontype

Definition

Primarylevers

Operational

COzeqfromelectricityconsumed

duringtrainingandinference

Carbon-awarescheduling,architectural

renewableprocurement

efficiency,

Embedded

CO₂eqfrommanufacturing,Hardwareutilizationmaximization,

shippinganddisposingofhardwareextendedassetlifetimes,(GPUs,servers)circulareconomyprinciples

Accordingtoa2022studybyLannelongueetal.,theembodiedcarbonofasingleNVIDIAA100GPUrepresentsapproximately150kgCO₂eqto200kgCO₂eq.Fora512-GPUcluster,embodiedemissionsalonereach

77tCOzeqto02tCOzeq,equivalenttotheannualoperationalcarbonofrunningthesameclusterattypicalutilizationforseveralmonths.2

3.3.Scopeandorganization

Thisdocumentpresentsaholistictechnicalblueprintaddressingbothemissiondimensions:

·Section2diagnosesthearchitecturalrootcauseoftransformerinefficiency

·Section3detailsmodelcompressiontechniques,includingdata-backedcomparisons

·Section4includesastudyofdatacentercoolingtechniquesusingPUEandWUEindicators

·Section5specifiestheCPAS-Gschedulingframework,conceptuallyandarchitecturally

·Sections6to8coversimulationmethodology,regionalgridarchetypesandadaptivemodelselection

·Sections9to10addresstheregulatorycontextandphasedimplementationroadmap

2P.Lannelongue,J.Grealey,andM.Inouye,"Greenalgorithms:Quantifyingthecarbonfootprintofcomputation,"AdvancedScience,vol.8,no.12,2021.

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4.Thescalingwall:

Whytransformersstrugglewithsustainaility

4.1.Theself-attentionottleneck

ThelandmarksuccessofgenerativeAIislargelyattriutaletotransformerarchitecture;specifically,itsself-attentionmechanismthatallowseverytokeninasequencetoattendtoeveryothertoken.Thisgloalreceptivefieldispowerful,

utitisuiltonafundamentalcomputationalottleneck:memoryandcomputerequirementsgrowquadraticallywithsequencelength.

Inpracticalterms,asthelengthofaninputpromptdoules,thememoryrequiredyatransformerroughlyquadruples.Forasequenceof32,768tokens,commoninenterprisedocument-analysistasks,thisgeneratesanattentionmatrixwithoveroneillionentries.Thisiswhatwetermthe“scalingwall”:apointeyondwhichtransformerinferenceecomes

economicallyandenergeticallyprohiitive.

Conceptualinsight:Transformerself-attentionislikea

meetingwhereeveryonemustdirectlycomparenoteswitheveryoneelsesimultaneously.Doulingtheattendeesdoes

notdouletheconversations—itroughlyquadruplesthem.

Thisisthecoreofthescalingprolem.”

4.2.Practicalconsequencesforenterpriseworkloads

Forlong-sequencetasks—includinglegaldocumentanalysis,codeasecomprehension,genomicsequenceprocessingandfinancialreportgeneration—thetransformer’squadraticgrowthcreatesprohiitivecosts.

Tale2:Memoryfootprintcomparison:Transformer(quadraticscaling)andSSM(linearscaling)3

Sequencelength(token)

Transformermemory(GB)

SSMmemory(GB)

Memoryreduction

4,096

0.13

0.08

38%

16,384

2.01

0.31

85%

32,768

8.05

0.62

92%

131,072

128.8

2.49

98%

3Source:A.GuandT.Dao,“Mama:Linear-timesequencemodelingwithselectivestatespaces,”arXiv:2312.00752,2023.

Caron-awaregenerativeAI

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Figure2comparesthememoryfootprintofscalingtransformerandSSMworkloads.

TransformerandSSMscalingehavior:Acomparisonofmemoryandcomputescalingysequencelength

Memoryfootprint

1000

GPUmemoryrequired(GB,logscale)

100

10

1

0,1

0,01

0,001

1K4K8K16K32K64K128K

Sequencelength(tokens)

TransformerO(n)SSM/MambaO(n)

GPUmemoryrequired(GB,logscale)

100000

10000

1000

100

10

1

1K4K8K16K32K64K128K

Sequencelength(tokens)

TransformerO(n)SSM/MambaO(n)

Figure2:TransformerandSSMscalingehavioryGPUmemoryfootprint(left)andrelativecompute(FLOPs)(right).

TheareaetweenthelinesoneachgraphrepresentsachievaleenergysavingsthroughSSMrouting.

4.3.State-spacemodels:Thelinearalternative

State-spacemodels(SSMs),andinparticulartheMamaarchitecture,4resolvethequadraticottleneckymaintainingacompressed,fixed-sizeinternalstatethatisupdatedsequentiallyastokensareprocessed.Unliketransformers,

thememoryandcomputerequirementsofanSSMgrowlinearlywithsequencelength—doulingthesequencelengthdoules(notquadruples)theresourcerequirement.

ThekeycapailityofMama’s“selective”SSMdesignisthatthestateupdaterulesarethemselvesinput-dependent:themodeldynamicallychooseswhichinformationtoretaininitscompressedstateandwhattodiscard,enalingittomodelcomplex,content-awaresequencesefficiently.ThisselectivityiswhatallowsSSMstoecompetitivewithtransformersonqualitymetricswhilemaintaininglinearscaling.

BenchmarkresultsfromtheoriginalMamapapershowthatat1.4Bparameters,Mamamatchesorexceedsthequalityofatransformerofthesamesizeonstandardlanguagemodelingtasks,whileachieving5-timeshigherthroughputat

sequencelength16K.

4.4.HyridARCHITECTURE:AdaptiveSSM-transformerrouting

ApureSSMreplacementoftransformersisnotalwaysoptimal,astransformersretainadvantagesontasksrequiringprecisemulti-hopretrievalacrossshortcontexts.TheCPAS-Gframeworkthereforeemploysahyrid,query-adaptivearchitecture:

•Short-contextqueries(under8,192tokens)areroutedtotransformer-asedmodels,formaximumaccuracy.

•Long-contextqueries(8,192tokensormore)areroutedtotheSSM(Mama)pathway,reducingcomputey60%to80%.

•Mixed-modalityqueriesareprocessedthroughaconfiguralepipelinecominingtransformerprefillwithSSMdecode.Theroutingdecisionismadeytheintelligentgateway(descriedinSection8),incorporatingnotonlysequencelengthutalsoreal-timecaronintensitysignalstoensurethearchitecturallycheapestvialemodelispreferredduringhigh-carongridperiods.

4A.GuandT.Dao,“Mama:Linear-timesequencemodelingwithselectivestatespaces,”arXiv:2312.00752,2023.

Caron-awaregenerativeAI

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5.Modeloptimization:

Shrinkingthecomputationalfootprint

Evenafterarchitecturalimprovements,themodelitself

carriesalargecomputationalweight,whichisdetermined

yitsparametercount.Amultistagecompressionpipeline

isessentialtoreducetheper-inferenceenergydrawnacross

allquerylengths.Thetechniquesoutlinedelow

arecominedtoachievemaximumimpactwithminimalqualitydegradation.

5.1.Pruningandsparsearchitectures

5.1.1Unstructured(magnitude-ased)pruning

Unstructuredpruningidentifiesindividualweightsinthe

neuralnetworkwhosevaluesaresosmallthatremoving

themhasnegligileimpactonthemodel’soutputs.

Conceptually,itisanalogoustofindingtheleast-informativepixelsinanimageanddiscardingthem.Atsparsitylevelsof50%to70%,hardware-acceleratedsparse-matrixoperationscandelivera20%to40%computereduction.

5.1.2Structured(head/layer)pruning

Structuredpruninggoesfurther:insteadofremoving

individualweights,entireattentionheadsortransformerlayersareremovedasedontheircontriutiontothe

model’soverallaccuracy.Thisapproachismoreaggressive

utyieldshardware-nativespeedupswithoutrequiring

sparse-computehardware,achievinga15%to30%computereductionwithcarefulretraining.

5.2.Quantization:Reducingnumericalprecision

Quantizationconvertshigh-precisionnumerical

representationsofmodelweights(typically32-itor

16-itfloating-point)intolower-precisionintegerformats(8-itor4-it).Thisdirectlyreducesoththememory

andwidthdemandsandcomputerequirements—the

twoprimarydeterminantsofGPUpowerdraw.Amodel

quantizedto4-itintegersoccupiesroughlyone-eighthoftheGPUmemoryofits32-itcounterpart.

5.2.1Activation-awareweightquantization(AWQ)Standardquantizationdegradesqualityat4-itecauseittreatsallmodelweightsequally.AWQovercomesthisy

identifyingasmallsuset(roughly1%)ofsalientweightsthatstronglyinfluenceactivationoutputsandprotectingthemfromaggressivequantization.Theremaining99%ofweightsarequantizedaggressively.5

5.2.2GeneralizedPost-TrainingQuantization

GPTQ(GeneralizedPost-TrainingQuantization)isamodel

optimizationtechniqueusedtoreducethememory

footprintandinferencecostofGPTlargelanguagemodels(LLMs)yconvertingmodelweightsfromhigh-precision

formatssuchasFP16orFP32intolower-itrepresentations,typically8-it,withoutrequiringmodelretraining.GPTQ

worksyperforminglayer-wisequantizationandusing

second-ordererrorapproximationtechniquestoidentify

andcompensatefortheimpactofquantizationonmodel

accuracy.Duringtheprocess,weightsarequantizedin

onelockatatimewhileminimizingthereconstruction

errorintroducedylowerprecision,allowingthemodeltoretainmostofitsoriginalperformance.GPTQisparticularlyusefulfordeployinglargemodelsonresource-constrainedenvironmentssuchasedgedevices,laptops,single-GPU

systemsandcost-optimizedcloudinferenceplatforms.6

AWQachieveslessthan2%accuracydegradationonMassiveMultitask

LanguageUnderstanding(MMLU)

enchmarksatINT4precision,comparedto8%to15%degradationwithnaive

round-to-nearestquantization.

ThismakesINT4AWQvialeforproductiondeployments.7”

5J.Linetal.,“AWQ:Activation-awareweightquantizationforLLMcompressionandacceleration,”arXiv:2306.00978,2023.

6F.Frantaretal.,“GPTQ:Accuratepost-trainingquantizationforgenerativepre-trainedtransformers,”arXiv:2210.17323,2022.

7J.Linetal.,“AWQ:Activation-awareweightquantizationforLLMcompressionandacceleration,”arXiv:2306.00978,2023.

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5.3.Knowledgedistillation

Knowledgedistillationtrainsacompact“student”modeltoreplicatetheehaviorofalarger“teacher”model.Rather

thanlearningfromrawtraininglaels,thestudentlearnsfromtheteacher’sproailitydistriutionsoveroutputs,whichcarryricherinformationaouttheprolemstructure.Theresultingstudentcane4to10timessmallerthantheteacher,achievinga60%to80%energyreductionatthecostofmoderate(5%to15%)accuracydegradationon

complextasks.

5.4.Compressionpipelinecomparison

Figure3,Figure4andFigure5comparevariouscompressionpipelinetechniquesandtheirimpactonenergyreduction,accuracytrade-offsandspeedingupinferencingruns.8

Impactofcompressiontechniquesonenergyreduction

Energyreduction(%)

72

70

60

40

30

22

Unstructuredpruning

Structuredpruning

AWQINT4quantizaton

GPTQINT4quantization

Knowledgedistillation

Prune+AWQ

Figure3:Impactofcompressiontechniquesonenergyreduction.

Impactofcompressiontechniquesonaccuracytrade-offs

Accuracydegradation(%)

10

1

4

1

1

4

Unstructuredpruning

Structuredpruning

AWQINT4quantization

GPTQINT4quantization

Knowledgedistillation

Prune+AWQ

Figure4:Impactofcompressiontechniquesonaccuracydegradation.

8Sources:AWQ[Linetal.,2023];GPTQ[Frantaretal.,2022];LLaMA-2evaluations[Touvronetal.,2023].

Carbon-awaregenerativeAI

Impactofcompressiontechniquesoninferencespeedup

Inferencespeedup(x)

Unstructured

pruning

Structured

pruning

3

AWQINT4

quantization

GPTQINT4

quantization

Knowledge

distillation

Prune+

AWQ

Figure5:Impactofcompressiontechniquesoninferencespeedup.

Table3comparesthesecompressiontechniques,intermsofenergyreduction,accuracy,inferencespeedupandimplementationcost.

Table3:Impactofoptimizationtechniquesoninferenceenergyefficiencyandaccuracy⁹

Technique

Energyreduction

Accuracyimpact

Inferencespeedup

Implementationcost

Unstructuredpruning

20%to40%

<2%

1.3×to1.8×

Medium

Structuredpruning

15%to

30%

2%to5%

1.2×to1.5×

Mediumtohigh

AWQquantization(4-bit)

50%to

70%

<2%

2.0×to3.5×

Medium

GPTQquantization(8-bit)

30%to

50%

<1%

1.5×to2.0×

Low

Knowledgedistillation

60%to

80%

5%to15%

4.0×to10×

Veryhigh

Combined(Prune+AWQ)

65%to

80%

2%to5%

3.5×to5.0×

High

⁹Sources:AWQ[Linetal.,2023],GPTQ[Frantaretal,2022],Distillation[Hintonetal,2015].

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6.Datacenterefficiency:

PUEandWUEforAIworkloads

Model-leveloptimizationsreducethecomputational

footprintofeachinference,buttheyaddressonlyone

componentofadatacenter'stotalenvironmentalimpact.

ThephysicalinfrastructurethathousesGPUclusters

introducesitsowninefficiencies:powerconversionlosses,coolingoverheadandwaterconsumption.ForAIworkloadsspecifically,theseoverheadsaredisproportionatelylargerelativetotraditionalenterpriseIT.Twostandardized

metrics—powerusageeffectiveness(PUE)andwater

usageeffectiveness(WUE)—quantifytheseinfrastructureinefficienciesandprovideactionabletargetsforreduction.

6.1.Powerusageeffectiveness(PUE):

DefinitionandAI-specificchallenges

PUEisdefinedastheratiooftotalfacilitypowerdrawntothepowerconsumedsolelybyITequipment:

PUE=Totalfacilitypower(W)/ITequipmentpower(W)APUEof1.0isthetheoreticalideal,whereeverywattdrawnfromthegridpowerscomputedirectly.APUEof2.0meansthatforeverywattconsumedbyservers,anadditionalwattisconsumedbycooling,lighting,powerdistributionand

otherfacilityoverhead.Industryaveragesin2024were

approximately1.46forlegacyenterprisedatacenters,

whilehyperscalecloudoperators(Google,Microsoft,Meta)reportedfacilityaveragesof1.10to1.15.10

Purpose-builtAIGPUclusters,however,presentadistinctivelydifficultPUEchallengeforthree

structuralreasons:

1.Extremerackdensity

NVIDIAH100SXM5GPUsdrawupto700Weach.

An8-GPUserverthereforeconsumes5.6kWfromtheGPUsubsystemalone,beforeaccountingforCPUs,

memory,networkingandstorage.ModernAIracks

routinelyexceed40to80kWperrack,comparedto5to15kWforastandardenterpriseserverrack.Thisdensitymakesaircoolingphysicallyinsufficientanddrives

disproportionatecoolingoverhead.

2.Sustained24x7utilization

Traditionalenterpriseworkloadsexhibitburstyutilization

profiles,allowingcoolingsystemstooperateatpart-loadefficiencyforsignificantperiods.ContinuousLLMinferencingmaintainssustainedhighGPUutilization,eliminatingpart-loadcoolingbenefitsandkeeping

computerroomair-conditioning(CRAC)andcomputerroomairhandler(CRAH)unitsoperatingatmaximumenergydrawnaroundtheclock.

3.Non-uniformheatdistribution

GPUclustersproduceintenselocalizedhotspotsthatdisruptcomputationalfluiddynamicswithintraditionalraised-floordatacenters,reducingaircoolingefficiencyandincreasingtheprobabilityofthermalthrottling

eventsthatthemselvesdegradeinferencethroughput.

10UptimeInstituteGlobalDataCenterSurveyResutls2024.

Caron-awaregenerativeAI

Tale4:PUEenchmarksyfacilitytype11

Facilitytype

TypicalPUE

Primaryoverheaddriver

Notes

Legacyenterprisedatacenter

1.5to2.0

Aircooling,UPSlosses

Industryaverage~1.46

(UptimeInstitute,2023)

Hyperscalecloud(AmazonWeServices[AWS]/GCP/Azure)

1.10to1.20

Optimizedcooling,customhardware

Googlereported1.10gloallyin2023

Colocationwithaircooling

1.3to1.6

CRACunitinefficiencyathighdensity

TypicalGPU

workloadhosting

Purpose-uiltAIdatacenter(liquidcooling)

1.03to1.15

Minimalcoolingoverheadviadirectliquidcooling(DLC)/immersion

Best-in-classtargetfornewAIuilds

6.2.Waterusageeffectiveness(WUE):

ThehiddencostofAIcooling

WUEmeasuresthewaterconsumedyadatacentertocoolitsITequipmentperunitofITenergydelivered:

WUE=Annualsitewaterusage(liters)/ITenergy(kWh)

WUEismeasuredinlitersperkilowatt-hour(L/kWh).Theindustryaverageisapproximately1.8L/kWh,

utAI-intensivefacilitiesusingevaporativecoolingtowers

routinelyreach3L/kWhto5L/kWhduringpeaksummer

operation.A512-GPUH100clusterconsuming3.5MW

continuouslycouldthereforeevaporate55,000litersto

90,000litersofwaterperday—equivalenttothedailywaterconsumptionof200to350households.

Waterconsumptionhasemergedasacriticalsustainailitydimensioneyondcaron.Inregionsalreadyunderwaterstress—includingtheUSSouthwest,SouthernEuropeandlargepartsofIndia—sitingamajorAIdatacenterinvolvesdirectcompetitionwithagriculturalandmunicipalwater

users.RegulatorypressureonWUEisincreasing:several

USstatesandtheEU’sEuropeanGreenDealhaveegun

mandatingwaterconsumptiondisclosuresforlargedigitalinfrastructureoperators.WUEisthereforenotonlyan

operationalcostmetricutalsoamaterialsustainailityliaility.

6.3.StrategiestoreducePUEforAIworkloads

Thestrategieselowaresetoutinorderoftheir

implementationcomplexityandimpactforAI-specificGPUdeployments.Themostimpactfulinterventionsrequire

capitalinvestmentand,ideally,informedsitingdecisionsmadeeforeadatacenterisuilt.

11UptimeInstituteGloalDataCenterSurvey2023;GoogleEnvironmentalReport2023.

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6.3.1Directliquidcooling(DLC)andimmersioncooling

Directliquidcoolingringscoolant(waterordielectricfluid)directlytothechippackageviacoldplatesmountedon

CPUsandGPUs,removingheatatthesourceratherthanconditioningamienta