2026AI基础设施真实成本研究报告：本地部署与云部署总拥有成本TCO分析

TheRealCostofAIInfrastructure

ATCOAnalysisof

On-PremisesvsCloud

May2026

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ExecutiveSummary

There’sbeenatippingpointoverthelastcoupleofyearsasAImovedfrom

experimentstopilotprojectstothepointwhereorganizationshavedecidedthat

thereareenoughbenefitsfromAItojustifyspendingbigmoneyonit.That’swhenAIbecomesinfrastructure.

Overthenextseveralyears,webelievethatAIwon’tbediscussedasaseparate“thing.”AIfeatureslikemachinelearning,deeplearning,andgenerativeAIwillbefoldedintoanyapplicationorworkflowwhereitmakessense(andplentywhereitprobablydoesn’tmakesenseaswell).

In2025alone,theglobalAIinfrastructuremarket(AIhardware,notfacilities)was

$318billionaccordingtoIDC,morethandoublethe$153billionspentin2024.Thisisexpectedtocontinueunabatedfortheforeseeablefuture.

Theproblemthatmanyorganizationsarewrestlingwithrightnowisnotthe“if”they’regoingtoneedanAIinfrastructure,butthe“what”and“where”questions.

The“what”questionishighlydependentonuniqueorganizationalfactorslikewhattheyseeasthelowhangingAIfruitfortheirbusinessandhowbigamovetheywanttomake.

Inthisreport,weareaddressingthe“where”questionintermsofon-premisesvs.publiccloudoptionsandtherespectivecosts.

Wecomparedtheestimatedcostofdeployingandoperatingaproduction-scaleAIinfrastructureon-premisesversusinthreemajorcloudplatforms:AmazonWeb

Services(AWS),GoogleCloudPlatform(GCP),andOracleCloudInfrastructure

(OCI).Thecomparisonisapples-to-apples,basedonpubliclyavailablepricingandclearlydefinedworkloadassumptions.Themodelassumessteady-state,24×7

operationofa248-GPUenterpriseAIcluster.

Thescenarioexaminesamature,diversifiedmanufacturingorganizationthathasalreadycompletedextensiveAIpilotprogramsacrossmultiplebusinessunits.

Thoseeffortshavedemonstratedmeasurableoperationalimprovementsinareas

suchasdefectreduction,scheduling,supplychainoptimization,andAI-augmentedHPCworkloads.Basedonvalidateddemand,theorganizationisevaluatinga

centralized,sharedAlinfrastructuretosupportongoingmodeltraining,testing,anddevelopment.

Cloudconfigurationsareprovisionedtodelivercomparableperformanceandcontinuousenterprise-scalecapacityundercommittedpricing.Allcostsarederivedfrompubliclyavailablepricesourcesatthetimeofanalysis.

1-Year,3-Year,5-YearCumulativeCosts,(USDmillions)

$180

$160

$140

$120

$100

$80

$60

$40

$20

$0

OCI(Oracle)AWS(Amazon)GCP(Google)OriginAl(Penguin)

■Year5

Undertheseconditions,thecostdifferenceisconsiderable.Overathree-year

period,thecloudoptionsareestimatedtocostanaverageofover3.5×morethantheon-premisesdeployment.Overfiveyears,thatdifferentialwidensfurther.

Thecostspreadisanillustrationofrentvsbuyeconomics.Simplyput,ifyouuse

somethingalotandit'sasignificantcost,inmostcasesyou'rebetteroffpurchasingitthanrentingit.

Ifyouchangetherequirementsandassumptions,thenumberswillchangeandsometimesthedecisionwillchangeaswell.Inthisreport,weclearlystatetherequirements/assumptions,configureon-premisesandCloudServiceProvider(CSP)environmentstobestsatisfytheconditions,thencostthemout.

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TableofContents

WhyThisReport,WhyNow?

5

BuildingtheModel

6

GlobalAssumptionsandModelingBoundaries

7

Continuous24×7steady-stateoperation

ClusterUtilization

HighPerformanceInfrastructureConfiguration

NoSpotorPreemptibleCapacity

Three-YearCommitmentPricingWhereAvailable

WhatThisModelDoesNotAttempttoCapture

AICrrseline

Ii

9

StoragePerformanceBaseline

NetworkingRequirements

PerformanceEquivalence

On-PremisesConfiguration:PenguinSolutionsOriginAI

12

OriginAICostSummary(PenguinSolutions)15

AmazonWebServices(AWS)ConfigurationSummary

18

AWSCostSummary19

GoogleCloudPlatform(GCP)ConfigurationSummary

20

GCPCostSummary21

OracleCloudInfrastructure(OCI)ConfigurationSummary

22

OCICostSummary24

WhattheNumbersShow

25

AppendixA:DetailedOn-PremisesConfiguration&Costs

27

AppendixB:DetailedAWSConfiguration&Costs

29

AppendixB:DetailedGCPConfigurations&Costs

30

AppendixB:DetailedOCIConfigurations&Costs

31

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WhyThisReport,WhyNow?

Everyonewhohasevencasuallykeptupwithbusinessortechnologyknowsthatwe’reinthemidstofahugemovetoaddAIfunctionalitytonearlyeveryaspectofbusiness.

Thekeyquestionthatmanyorganizationsarewrestlingwithnowishowtoaddthe

necessarytechnologytoenabletheirAIinitiatives.Dotheyaddnewservers,GPUs,andinfrastructuretotheirexistingdatacenter?Ordotheyrentthecapacityinthecloud?Andwhatcantheyexpecttopayineithercase?

I’vebeencuriousforawhileaboutwhatitreallycoststorunseriousIT(andnowAI)infrastructureon-premisesversusinthepubliccloud.

Cloudpricingispublic.AnyonecangointoAmazonWebServices(AWS)orGoogleCloudpricingtoolsandmodeldetailedconfigurations.Butit’smuchhardertoobtainafullybuilt-out,enterprise-sizedon-premiseswithrealpricingattached.Withoutthat,anapples-to-applescomparisonislargelyspeculation.

PenguinSolutionswasinterestedinthesamequestion.ThesurgeinAIinvestmenthas

createdawaveofinfrastructuredecisionpoints,andorganizationsareactivelydebatingwheretheirlong-termAIbackboneshouldreside.Penguinfundedthetimerequiredformetoconductadetailedcostanalysisofa248-GPUon-premisesclusterandcompareit

againstAWS,GoogleCloud,andOracleCloud.

Tobeclear,Penguinprovidedadetailedhardwareconfigurationandassociatedpricingfortheon-premisessystem.Ibuiltthecloudmodelsbyusingpubliclyavailablepricingtools.Idefinedtheworkloadassumptions,utilizationmodel,storageandnetworkrequirements,andranthecostcomparisons.Themethodologyandconclusionsinthisreportaremine.

PriorworkinHPCandinfrastructureeconomicshasgivenmeageneralsenseofhowthis

mightturnout.Theunderlyingeconomicsaren’tmysterious.Ifyou’revisitingacityfora

week,youbookahotel.Ifyou’restayingforayear,yourentanapartment.Ifyou’resettlinginforthelongterm,youbuyahouse.Thelongerandsteadierthedemand,thestrongerthe

economiccaseforownership.

Thatdoesn’tmeanhotelsarewrong.Weabsolutelyneedhotels.Wealsoneedapartmentsandhouses.Butonemodeldoesn’tfiteverysituation.ThesameistrueforAIinfrastructure.

Butanalogiesaren’tanalysis.Thepointofthisreportistomovebeyondintuitionandrunthenumbersunderclearlydefinedreal-worldconditions.

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BuildingtheModel

Weapproachedthiscomparisonthesamewaywewouldapproachanyinfrastructureevaluation:definetheworkloadfirst,thenbuildsystemstosupportit.

Themodelassumespersistentusageandhighutilizationplus24×7operationofa

centralized248-GPUAIclusterservingmultiplebusinessunitswithinamature,globalmanufacturingorganization.Utilizationismodeledatapproximately80%,reflectingaggregatedemandacrossarangeofongoingAIinitiatives.

CloudconfigurationswerebuilttomirrorthesameGPUcount,storagecapacity,andoverallperformanceenvelope.Nospotinstancesorpreemptiblecapacityareused.

IselectedAmazonAWS,GoogleCloudPlatform,andOracleCloudInfrastructurebecausetheyhadinstanceswithx86CPUscoupledwithNVIDIAB200GPUs.MicrosoftAzuredidnothavetheseinstancesavailableduringourresearchperiod.Wealsodidnotanalyzethe

smallerAIspecialistcloudprovidersduetotheirrelativelysmallsizeandnewnesstothemarket.

Wherepublishedthree-yearcommitmentpricingwasnotavailable(AWSB200andOCI),weappliedconservativediscountassumptionsbasedonhistoricalprecedentandtypical

enterprisenegotiationpatterns.Thoseassumptionsarecalledoutexplicitlyintherelevantsections.

Five-yeartotalsarecalculatedbyextendingmonthlypricinglinearly.Whyfiveyearsinsteadofthree?BecausemajorCSPshaveextendedtheirownserverdepreciationschedulesto

fiveyears,andthePenguinSolutionson-premisesconfigurationincludesfive-yearbreak/fixservicecoverage.

Noassumptionsaremadeaboutrenegotiation,futurepricereductions,orhardwarerefreshcycles.Vendorsnegotiatepricingallthetimebasedondealsize,timing,andinternal

revenuetargets.That’sreal,butit’soutsidethescopeofthismodel.

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GlobalAssumptionsandModelingBoundaries

Continuous24×7steady-stateoperation:

LargeglobalorganizationswithmultiplelinesofbusinesstendtohavemanyAIefforts

underwayatthesametime.Someareearlypilots,othersaretestingphases,andsomearefull-scaletrainingorretrainingworkloads.ThisclusterisdesignedprimarilyforAI

developmentandtraining.Itcouldhandleinferenceworkloadsaswell,butinferencedemandvarieswidelydependingonthemodelanddeploymentpattern,sowe’renotlookingatitspecificallyhere.

ClusterUtilizationAssumption:

Utilizationismodeledattheaggregateclusterlevel,notperindividualnode.Weareusing80%astheaverageclusterutilizationrate.Theideaistoreflecttotaldemandacross

multipleconcurrentinitiativesratherthanassumeeveryGPUisfullysaturatedatalltimes.IncentralizedHPCandenterpriseAIenvironments,sustainedutilizationinthe75–85%

rangeisn’tunusualwheninfrastructureissizedtothevalidateddemandinsteadofspeculativegrowth.Thismodelshowsoperatingreality.

HighPerformanceInfrastructureConfiguration:

AItrainingworkloadsarecomputationallyintensiveandrequiretightlyintegrated,high-performanceinfrastructure—essentiallysupercomputing-classsystems.Equivalentconfigurationsareavailablefrombothon-premisesvendorsandCSPs,andthecloudmodelswerebuiltaccordingly.

NoSpotorPreemptibleCapacity:

Theon-premisessystemisavailabletousers24×7,sotheCSPconfigurationsareheldtothesamestandard.Manyoftheworkloadsmodeledhererunfordaysorevenweeks.

Allowinginterruptionswouldrequirefrequentcheckpointing,whichaddsoverheadand

cost.Spotmarketpricingandavailabilityalsofluctuatesignificantly,makingthembettersuitedforshort-termorburstworkloadsratherthanproductionenvironmentswithongoinghighutilization.

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Three-YearCommitmentPricingWhereAvailable:

Committedpricingimprovescostpredictabilityandreducescloudexpense.Insomecases,high-demandGPUinstancesareonlyofferedunderreservedorcommittedagreements,sothisassumptionmimicshowlargeenterprisesactuallyprocurecloudinfrastructure.

WhatThisModelDoesNotAttempttoCapture

•Softwareorapplicationlicensingbeyondbaselinesystemmanagementtools.

•Managedservices.Thesearewidelyavailableandcustomizable,buttheyare

outsidethescopeofthisinfrastructurecostcomparison.Break/fixserviceis

includedfortheon-premisessystemsinceCSPinfrastructureinherentlyincludeshardwaresupport.

•Financialtreatment(CapExvsOpEx).Depreciationschedules,taxtreatment,andcapitalstructuringvarybyorganizationandaren’tpresentedindepthhere.

•Futurepricingchanges.Allpricingisfrompubliclyavailabledataasof1Q2026.

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AIInfrastructureConfigurationBaseline

Beforecomparingspecificvendorimplementations,it’simportanttodefinethecommonconfigurationbaselineusedacrossallscenarios.Thegoalisnottomodelahypotheticalhyperscaleenvironmentorasmallexperimentalcluster,butaserious,large-scale

enterprisedeploymentsizedtothevalidateddemand.

Thisconfigurationassumesalarge,matureorganizationconsolidatingAIdevelopmentandtrainingworkloadsintoacentralizedsharedresource.Itisintentionallylargeenoughto

exposemeaningfuleconomicdifferences,butnotsolargeastorepresenthyperscaler-scaleinfrastructure.

ClusterSizeandGPUGeneration

OnPremises&CSPConfigurationSummary

Specification

OriginAI(PenguinSolutions)

AWS

GoogleCloud

OCI

ComputePlatform

CustomGPUcluster

p6-b200.48xlarge

a4-highgpu-8g

BM.GPU.B200.8

#ComputeNodes

31

31

31

31

GPUs(Total/Node)

248/8

248/8

248/8

248/8

GPUType

NVIDIAB200

NVIDIAB200

NVIDIAB200

NVIDIAB200

CPUperNode

IntelXeon(128cores)

Intel(192vCPUs)

Intel(224vCPUs)

AMDEPYC(256cores)

MemoryperNode(GB)

2048

2048

3968

2048

LocalStorageperNode

30.72TBNVMe

30.72TBSSD

28TBSSD

54TBSSD

Interconnect

400Gb/sNDRInfiniBand

3200Gb/s(EFA,RDMA)

3600Gb/s(RDMAfabric)

3200Gb/s(RoCEv2RDMA)

SharedStorage

VASTData,2.5PBusable

FSxforLustre,2.5PBusable

ManagedLustre,2.5PBusable

OCILustre,2.5PBusable

Admin/MgmtNodes

8×dualIntel6767P

8×i4i.32xlarge

8×m3-megamem-128

8×VM.Standard.E5.Flex

DeploymentModel

On-premcluster

Persistent(24x7)

Persistent(24x7)

Persistent(24x7)

(*detailedconfigurationsincludedbelow)

Theconfigurationisbuiltaround248NVIDIAB200GPUs,deployedas31nodeswith8GPUspernode.

TheB200generationwasselectedbecauseitisawidelyavailable,production-provenGPUarchitectureofferedacrosstheselectedcloudprovidersandtheon-premisesvendor.

Atthetimeofmodeling,B300-classsystemswereemergingbutnotyetbroadlyavailable

acrossallprovidersincomparableconfigurations.UsingB200GPUsallowsforaconsistent,apples-to-applescomparison.

FutureGPUgenerationswillimproveperformanceperwattandperdollar.That

improvementbenefitsbothrentalandownershipmodels.Thefocushereisthecoststructureundersustainedutilization,notgenerationalbenchmarking.

The248-GPUscalewaschosentorepresentasubstantialbutrealisticenterprise

deployment.ItislargeenoughtosupportmultipleconcurrentAIinitiativesacrossbusiness

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unitsandtostresstheeconomicmodelmeaningfully,butitdoesnotapproachhyperscaledatacentermagnitude.

StoragePerformanceBaseline

AItrainingworkloadsrequirehigh-throughput,parallelstoragecapableofsustaininglargedataflowstoandfromGPUnodes.StorageneedstodeliverenoughdatatokeepGPUs

highlyutilizedandavoididletime.Thebaselineconfigurationassumesstorage

performanceconsistentwithapproximately250MB/sperTiBinthecloudenvironments,alignedwithenterprise-classmanagedparallelfilesystemtiers.

On-premisesstorageisconfiguredtodelivercomparableorhigheraggregateread/writethroughputusingascale-outarchitecture.Theintentisnottooptimizeforanysingle

vendor’speakperformance,buttoensurethatallconfigurationsmeetaconsistentperformanceenvelopeappropriateforlarge-scaleAItrainingworkloads.

Capacityandthroughputweresizedtoavoidbottlenecksineitherenvironment.Storagecapacitywassetat2.5petabytesofusablespace.

ThisbaselineintentionallyfocusesonAIdevelopmentandtrainingworkloads.Inference

environmentsvarysignificantlydependingonmodelsize,deploymentpattern,andlatencyrequirements,makingthemhighlyworkload-specificandlesssuitableforgeneralizedcostmodelingatthisscale.

NetworkingRequirements

Large-scaleAItrainingdependsonhigh-bandwidth,low-latencyinterconnectsbetweenGPUnodes.Thebaselineassumesahigh-performancefabricappropriatefordistributedtrainingworkloads.

On-premisesconfigurationsutilizemodernhigh-speedinterconnecttechnologiescommoninHPC-classdeployments.Cloudconfigurationswereselectedtoprovidecomparable

intra-clusternetworkingcapabilitieswithinasingleregion.

Allscenariosassumethatthefullclusteroperateswithinasingleregionordatacenterenvironmenttoavoidcross-regionlatencypenaltiesandtomaintaintrainingefficiency.

Thisconfigurationisoptimizedfordistributed,multi-nodetrainingworkloadsratherthan

single-nodefine-tuningtasks.Whilesmallerfine-tuningjobscanbeaccommodated,the

clusterissizedandinterconnectedtosupportlarge-scalesynchronizedtrainingrunsacrossmanyGPUs.

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PerformanceEquivalence

Allvendorconfigurationsareconstructedtomeetthesamegeneralperformanceandcapacitytargets.Thegoalisnottoclaimarchitecturalsuperiorityforanydeploymentmodel,buttodefineaconsistentbaselinesothatcostcomparisonsreflecteconomicstructureratherthanconfigurationdifferences.

Thisbaselineisarealistic,enterprise-scaleAIinfrastructureforamaturecompanywhereAImodelswillneedtobetestedandtrainedatscale.

Configuringequallycapablesystemsacrosson-premisesandcloudenvironmentsisnottrivial.Theterminology,specifications,andpackagingdiffersignificantlybetweenphysicalinfrastructureandcloud-definedinstances.EvenamongCSPs,thereislimitedcommongroundinhowinstancesandperformancecharacteristicsaredescribed.

Forthatreason,thefollowingsectionsexplicitlyoutlinehoweachconfigurationwasconstructed.Whiletheimplementationsdifferindetail,eachisdesignedtomeet

comparableperformanceexpectations.

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On-PremisesConfiguration:PenguinSolutionsOriginAIInfrastructure

Theon-premconfigurationinthisanalysisisbasedonadetailedsystemspecificationandpricingprovidedbyPenguinSolutions.Thesystemisacentralized248-GPUAIcluster

intendedtosupportsustainedtrainingworkloadsacrossmultiplebusinessunits.Thespecificationsbelowsummarizethecompute,storage,networking,andsupportinginfrastructurecomponentsincludedinthequotedconfiguration.

ComputeInfrastructure(Summarized)

Component

Specification

GPUNode

Configuration

31nodes;248GPUstotal(8×NVIDIAB200(192GB)pernode);DualIntelXeonCPUs;2TBRAMpernode;30.73TBNVMepernode;NVIDIA

400Gb/sInfiniBand

Theclusterconsistsof31GPUnodesconfiguredfordistributedmulti-nodetraining

workloads.EachnodecombineseightB200GPUswithbalancedCPU,memory,andhigh-speedinterconnectresources.

DetailedsystemspecificationsareprovidedinAppendixA.

StorageConfiguration(Summarized)

StorageConfiguration

Component

Specification

Architecture

VASTDatascale-outstorage

Configuration

64VASTCboxes;10VASTDboxes

UsableCapacity

~2.5PB

AggregateThroughput384GB/swrite;600GB/sreadSoftwareSubscription$354,000peryear

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Storagecapacityandthroughputweresizedtomeetthecloudstorageperformancetieratapproximately250MB/sperTiB.Theconfigurationsupportssustainedtrainingworkloadsandlargedatasetiterationcycles.

DetailedstoragespecificationsareprovidedinAppendixA.

NetworkingFabric(Summarized)

Component

Specification

Back-EndNetwork

NDRInfiniBand(400Gb/spernode);non-blocking,fullyredundant

Front-EndNetwork

400GbEthernet;non-blockingGPU/storageconnectivity;redundantarchitecture

Out-of-BandNetwork

DedicatedGigabitEthernetmanagementnetwork

ThearchitectureseparatesGPUinterconnecttraffic,storageandin-bandcommunications,andmanagementfunctions.TheGPUfabricoperatesinafullyredundant,non-blocking

topologyoptimizedfordistributedtraining.

InfiniBandisusedby55%ofthesystemsonthe

Top500

listoflargestsupercomputersand68%ofthetop100supercomputers.

DetailednetworktopologyandswitchspecificationsareprovidedinAppendixA.

Integration,Support,andClusterManagement

Theon-premisespurchasepriceincludesallrackhardware,cabling,andrelated

infrastructurerequiredtodeliveracompleteandfullyfunctioningsystem.Thequotedconfigurationisanintegrated,deployableclusterratherthanapartialbillofmaterials.

Integrationanddeliveryservicesareincludedaspartofthesystemprice.Thiscovers

systemassembly,configuration,validation,anddeploymentwithinthetargetdatacenterenvironment.

Five-yearExtendedPlatformSupportisincludedandprovides:

•24×7technicalsupportavailability

•Severity1:24×7response,one-hourtarget

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•Severity2:9×5response,four-hourtarget

•Severity3:9×5response,eight-hourtarget

Thissupportcoveragespansthefullfive-yearmodelinghorizonusedinthisanalysis.The

configurationalsoincludesPenguin’sClusterWareAIsoftwareplatform,whichprovides

centralizedclustermonitoring,management,andoperationaltoolingacrosscompute,

storage,andnetworkingcomponents.ClusterWareAIsoftwareisbilledat$128,000peryear(invoicedmonthly)andisincludedinthetotalcostofownershipcalculationsacrossthe

five-yearperiod.

Allintegration,support,andmanagementsoftwarecostsareincludedinthetotalcostofownershipmodelandarenottreatedasoptionaladd-ons.Detailedsupportterms,servicescope,andsoftwareinclusionsareprovidedinAppendixA.

EnergyandFacilitiesAssumptions

Penguinprovidedmeasuredpowerdatafortheclusterundermaximumsystemload:538kWofITload.Forthecontinuoususagemodel,weappliedour80%averageutilizationassumptiontoconvertpeaktestpowerintoanestimatedaverageoperatingload.

AnnualEnergyCalculation(BaseCase)

•PeaktestedITload:538kW

•ModeledaverageITload(80%):538×0.80=430kW

•MarginalPUE(air-cooled):1.30

•Incrementalfacilityload:430×1.30=559kW

•Annualhours:8,760

•Annualenergy:559×8,760=4,896,840kWh

•Electricityrate:$0.12/kWh(UScommercialrateaverage,Ohio)

•Estimatedadditionalannualelectricitycost:$587,621

ThisapproachtreatstheclusterasanincrementalloadwithinanexistingdatacenterandappliesamarginalPUEtocapturecoolingandpoweroverheadattributabletotheaddedITload.

Onthefacilitiesside,thismodelassumesthatthereissufficientelectricalserviceanddatacenterfloorspacetoaccommodatetheAIinfrastructurecluster.Thisisn’ttrueinall

situations,ofcourse.Buttherearesomeroutestopursuebeforelookingforaco-location

May2026TheRealCostofAIInfrastructure15of31

deal.Largedatacentersnearlyalwayshave‘ghost’systemsthatarepoweredon,takeupfloorspace,andyethavefewornousers.Adatacenterassessmentuncoversthese

systemssotheycanbedecommissioned,andtheirpower/footprintdevotedtonewerandmoreefficientsystems.

Ifthisdoesn’tfreeupenoughresourcesforthenewcluster,thenco-locationisthebestoption,andthereisadizzyingarrayofco-locationvendorsandplansavailabletoday.

PersonnelAssumptions

Operatingacentralized248-GPUAIclusterrequiresdedicatedinfrastructureoversight.Themodelassumesthreeincrementalfull-timeequivalents(FTEs)associatedwithongoing

clusteroperations.

Thisincludes:

•Oneseniorcluster/AIarchitectatanannualfullyloadedcostof$260,000

•Twoclustersupportengineersat$125,000eachperyear

TheserolescoverAIinfrastructurearchitecture,systemmanagement,administration,andusersupport.Theadditionalstaffwillhelpsupportacentralizedsharedenvironment

servingmultiplebusinessunitsandsupportingconcurrenttrainingworkloadsacrosstheorganization.

Totalincrementalpersonnelcostis$510,000peryear,includedinthetotalcostofownershipcalculationsandextendedacrossthefive-yearmodelinghorizon.

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On-PremisesCostSummary:PenguinSolutionsOriginAIInfrastructure

CapitalInvestmentComponent

Cost

GPUNodeInfrastructure(31nodes,248GPUs)

$18,833,366

AdministrativeNodes(8nodes)

$336,940

StorageHardware(VASTConfiguration)

$2,138,000

TotalCapitalInvestment

$21,308,306

Thecapitalinvestmentincludesallnetworkinginfrastructure,racks,cabling,integrationservices,andfive-yearextendedplatformsupport.DetailedspecificationsareprovidedinAppendixA.

AnnualOperatingCostsComponent

AnnualCost

Energy(Incremental)

$587,621

VASTSoftwareSubscription

$354,000

ClusterWareAI

$128,000

Personnel(3FTE)

$510,000

TotalAnnualOperatingCost$1,579,621

TotalCostofOwnership(OpEx+CapEx)3-YearTCO$26,047,168

5-YearTCO$29,206,410

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AnnualCostswithDepreciationandCostShare:

AnnualCosts,3-YearStraightLineDepreciation

AnnualCosts,5-YearStraightLineDepreciation

Cost

%Total

Cost

%Total

Purchase($21,308,306)

$7,102,769

82%

$4,261,661

73%

VastStorageSWcost

$354,000

4%

$354,000

6%

ClusterWareAISoftware

$128,000

1%

$128,000

2%

Power&Coolingcosts

$587,621

7%

$587,621

10%

AdditionalPersonnelcosts

$510,000

6%

$510,000

9%

$8,682,389.47

$5,841,282.00

It’sinterestingtonotethathardwaredepreciationisbyfarthelargestportionofannual

costsbothonathree-andfive-yeardepreciationbasis.We’veheardalotinthepressaboutelectricitydemandradicallyincreasingwiththeadventof