Demystifying AI Compute: A Visual Guide to Requirements and Costs

AI Computing Hardware Ecosystem

flowchart TD
    AI[AI System] --> Compute[Computing Units]
    AI --> Memory[Memory Systems]
    AI --> Infrastructure[Infrastructure]
    
    Compute --> GPU[GPUs]
    Compute --> TPU[TPUs]
    Compute --> ASIC[AI ASICs]
    Compute --> CPU[CPUs]
    
    Memory --> HBM[High Bandwidth Memory]
    Memory --> DRAM[System DRAM]
    Memory --> Storage[Fast Storage - SSDs]
    
    Infrastructure --> Power[Power Delivery]
    Infrastructure --> Cooling[Cooling Systems]
    Infrastructure --> Network[Network Fabric]
    
    class AI,Compute,Memory,Infrastructure,GPU,TPU,ASIC,CPU,HBM,DRAM,Storage,Power,Cooling,Network nodeStyle;
    
    classDef nodeStyle fill:#fff,stroke:#FF8000,stroke-width:2px,color:#333;
        

flowchart TD
    Training[Training Costs] --> ModelSize[Model Size]
    Training --> DataSize[Dataset Size]
    Training --> Iterations[Training Iterations]
    Training --> Hardware[Hardware Choice]
    
    ModelSize --> Parameters[Parameter Count]
    ModelSize --> Architecture[Model Architecture]
    
    DataSize --> Preprocessing[Preprocessing Costs]
    DataSize --> Storage[Storage Costs]
    DataSize --> Quality[Data Quality Requirements]
    
    Iterations --> Convergence[Convergence Rate]
    Iterations --> Experiments[Experimental Iterations]
    
    Hardware --> Cloud[Cloud vs On-Premise]
    Hardware --> Specs[Hardware Specifications]
    
    class Training,ModelSize,DataSize,Iterations,Hardware,Parameters,Architecture,Preprocessing,Storage,Quality,Convergence,Experiments,Cloud,Specs nodeStyle;
    classDef nodeStyle fill:#fff,stroke:#FF8000,stroke-width:2px,color:#333;
        

flowchart TD
    Inference[Inference Costs] --> ComputeModel[Compute Resource Model]
    Inference --> ProcessingMode[Processing Mode]
    Inference --> DeploymentLocation[Deployment Location]
    
    ComputeModel --> OnDemand[On-Demand/Pay-per-use]
    ComputeModel --> Reserved[Reserved Capacity]
    ComputeModel --> Spot[Spot/Preemptible]
    
    ProcessingMode --> RealTime[Real-time Inference]
    ProcessingMode --> Batch[Batch Processing]
    
    DeploymentLocation --> Cloud[Cloud-based]
    DeploymentLocation --> Edge[Edge Computing]
    DeploymentLocation --> Hybrid[Hybrid Approach]
    
    RealTime --> LowLatency[Low Latency Requirements]
    RealTime --> HighAvailability[High Availability Needs]
    
    Batch --> CostEfficiency[Cost Efficiency]
    Batch --> TimeTolerance[Time Tolerance]
    
    class Inference,ComputeModel,ProcessingMode,DeploymentLocation,OnDemand,Reserved,Spot,RealTime,Batch,Cloud,Edge,Hybrid,LowLatency,HighAvailability,CostEfficiency,TimeTolerance nodeStyle;
    classDef nodeStyle fill:#fff,stroke:#FF8000,stroke-width:2px,color:#333;
        

flowchart TD
    subgraph AWS["AWS Optimization"]
        A1[Use Spot Instances] --> A2[60-90% savings]
        A3[Reserved Instances] --> A4[40-60% savings]
        A5[Savings Plans] --> A6[Flexible commitment]
    end
    
    subgraph GCP["Google Cloud Optimization"]
        G1[Preemptible VMs] --> G2[70-80% savings]
        G3[Sustained Use Discounts] --> G4[Automatic discounts]
        G5[Custom Machine Types] --> G6[Right-size resources]
    end
    
    subgraph Azure["Azure Optimization"]
        M1[Azure Reserved VM] --> M2[Up to 72% savings]
        M3[Low Priority VMs] --> M4[60-80% savings]
        M5[Azure Hybrid Benefit] --> M6[License savings]
    end
    
    classDef awsStyle fill:#fff,stroke:#FF9900,stroke-width:2px,color:#333;
    classDef gcpStyle fill:#fff,stroke:#4285F4,stroke-width:2px,color:#333;
    classDef azureStyle fill:#fff,stroke:#0078D4,stroke-width:2px,color:#333;
    
    class AWS,A1,A2,A3,A4,A5,A6 awsStyle;
    class GCP,G1,G2,G3,G4,G5,G6 gcpStyle;
    class Azure,M1,M2,M3,M4,M5,M6 azureStyle;
        

flowchart TD
    Start[New AI Project] --> Prototype[Prototype Phase]
    Prototype --> ProtoCompute[Cloud Compute\nSmall-Scale Testing]
    
    Prototype --> Validation[Validation Phase]
    Validation --> ValidationComp[Hybrid Approach\nCloud for peaks\nBasic on-prem]
    
    Validation --> Production[Production Phase]
    Production --> EvaluateWorkload{Evaluate Workload\nCharacteristics}
    
    EvaluateWorkload -->|Consistent & Predictable| OnPrem[On-Premise\nPrimary Infrastructure]
    EvaluateWorkload -->|Variable & Scaling| Cloud[Cloud-First\nStrategy]
    EvaluateWorkload -->|Mixed Requirements| Hybrid[Hybrid Infrastructure]
    
    OnPrem --> OnPremStrategy[Cost Strategy:\n- Bulk hardware purchase\n- 3-5 year amortization\n- Cloud backup]
    
    Cloud --> CloudStrategy[Cost Strategy:\n- Reserved instances\n- Spot/Preemptible VMs\n- Auto-scaling policies]
    
    Hybrid --> HybridStrategy[Cost Strategy:\n- Baseline on-premise\n- Cloud bursting\n- Workload optimization]
    
    class Start,Prototype,ProtoCompute,Validation,ValidationComp,Production,EvaluateWorkload,OnPrem,Cloud,Hybrid,OnPremStrategy,CloudStrategy,HybridStrategy nodeStyle;
    classDef nodeStyle fill:#fff,stroke:#FF8000,stroke-width:2px,color:#333;
        

flowchart TD
    Start[Start Cost Estimation] --> ModelType{What type of\nAI model?}
    
    ModelType -->|NLP/Text| TextModel[Text-Based Model]
    ModelType -->|Vision| VisionModel[Vision-Based Model]
    ModelType -->|Multi-modal| MultiModal[Multi-modal Model]
    
    TextModel --> TextSize{Model Size?}
    VisionModel --> VisionSize{Model Size?}
    MultiModal --> MultiSize{Model Size?}
    
    TextSize -->|Small <1B params| TextSmall[Estimate:\n$2K-$10K Training\n$50-$200/mo Inference]
    TextSize -->|Medium 1B-10B| TextMed[Estimate:\n$10K-$100K Training\n$500-$5K/mo Inference]
    TextSize -->|Large >10B| TextLarge[Estimate:\n$100K-$1M+ Training\n$5K-$50K+/mo Inference]
    
    VisionSize -->|Small| VisionSmall[Estimate:\n$5K-$20K Training\n$100-$500/mo Inference]
    VisionSize -->|Medium| VisionMed[Estimate:\n$20K-$200K Training\n$1K-$10K/mo Inference]
    VisionSize -->|Large| VisionLarge[Estimate:\n$200K-$2M+ Training\n$10K-$100K+/mo Inference]
    
    MultiSize -->|Small| MultiSmall[Estimate:\n$10K-$50K Training\n$200-$1K/mo Inference]
    MultiSize -->|Medium| MultiMed[Estimate:\n$50K-$500K Training\n$2K-$20K/mo Inference]
    MultiSize -->|Large| MultiLarge[Estimate:\n$500K-$5M+ Training\n$20K-$200K+/mo Inference]
    
    TextSmall & TextMed & TextLarge & VisionSmall & VisionMed & VisionLarge & MultiSmall & MultiMed & MultiLarge --> Adjust[Adjust Based On]
    
    Adjust --> DataSize[Dataset Size]
    Adjust --> TrainingDuration[Training Duration]
    Adjust --> UserScale[User Scale]
    Adjust --> RegionCosts[Regional Cost Differences]
    Adjust --> Optimization[Optimization Techniques]
    
    class Start,ModelType,TextModel,VisionModel,MultiModal,TextSize,VisionSize,MultiSize,TextSmall,TextMed,TextLarge,VisionSmall,VisionMed,VisionLarge,MultiSmall,MultiMed,MultiLarge,Adjust,DataSize,TrainingDuration,UserScale,RegionCosts,Optimization nodeStyle;
    classDef nodeStyle fill:#fff,stroke:#FF8000,stroke-width:2px,color:#333;