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Chapter 9: Technical Specifications

9.1 System Requirements​

9.1.1 Node Requirements​

9.1.2 Hardware Specifications​

Node TypeMinimum SpecsRecommended SpecsOptimal Use Case
AI Training8x A100, 512GB RAM16x A100, 1TB RAMLarge model training
Inference4x T4, 128GB RAM8x A10, 256GB RAMModel serving
General Compute32 cores, 128GB RAM64 cores, 256GB RAMData processing
Storage2TB NVMe, 10Gbps10TB NVMe, 100GbpsData storage

9.2 Network Requirements​

9.2.1 Network Architecture​

9.2.2 Network Specifications​

ComponentMinimumRecommendedMission Critical
Bandwidth1 Gbps10 Gbps100 Gbps
Latency50ms10ms5ms
Reliability99.9%99.99%99.999%
Packet Loss0.1%0.01%0.001%

9.3 Performance Metrics​

9.3.1 Performance Categories​

9.3.2 Performance Targets​

MetricTargetMeasurementSLA
Training Speed90% GPU utilReal-time99.9%
Inference Latency100msPer-request99.99%
Storage IOPS100KContinuous99.9%
Network Latency10msEnd-to-end99.99%

9.4 Security Standards​

9.4.1 Security Architecture​

9.4.2 Security Requirements​

ComponentStandardImplementationVerification
EncryptionAES-256-GCMHardware TEERegular audit
NetworkWireGuardMesh VPNContinuous test
AccessZero TrustMFAReal-time check
MonitoringSIEMLog analysis24/7 SOC

9.5 Scalability Specifications​

9.5.1 Scaling Architecture​

9.5.2 Scaling Limits​

DimensionMinimumMaximumGrowth Rate
Nodes/Cluster31000100/month
GPUs/Node116As needed
Storage/Node1TB100TB10TB/month
Network/Node1Gbps100Gbps10Gbps/quarter

9.6 Compatibility Standards​

9.6.1 Software Compatibility​

9.6.2 Compatibility Matrix​

ComponentSupported VersionsIntegrationNotes
OSUbuntu 20.04+, RHEL 8+NativeFull support
ContainersDocker, containerdNativeOCI compliant
ML FrameworksPyTorch, TensorFlowOptimizedGPU enabled
Dev ToolsVSCode, JupyterLabIntegratedFull features

9.7 Resource Management​

9.7.1 Resource Architecture​

9.7.2 Resource Limits​

ResourcePer UserPer NodePer Cluster
GPUs16321000
vCPUs6412810000
Memory512GB1TB100TB
Storage10TB100TB10PB

These technical specifications provide a comprehensive framework for building, operating, and scaling the Swarm platform. They ensure consistent performance, security, and reliability across all deployments while maintaining flexibility for future growth.