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AI Architecture › L0 · Foundation
L0 · Foundation

L0 · Foundation

Cloud HPC, bare metal, and supercomputing — the offline multi-physics and batch-training substrate.

THE STACK · click to jumpL7Ecosystem & StrategyL6Experience & VisualizationL5Applications & CopilotsL4OrchestrationL3Twin Modeling & AIL2Data FabricL1Control PlaneL0Foundation▲tlmctl▼L0 · FOUNDATIONThe offline compute substrate — multi-physics & batch training.1Cloud HPCelastic burst2Bare-Metal ClusterGPU / CPU3Supercomputingmulti-physics runs4Batch Trainingmodel builds5Simulation FarmGrad-Shafranov · MHD6Object StorecheckpointsMACHINE TIETrains the models that ship UP to L3 — no real-time path to the machine.KRONOS FUSION ENERGYAI-NATIVE S.M.A.R.T. GENERATORFOUNDATIONSHEET 02REV. 2026-08L0 · AI-NATIVE STACK
L0 · Foundation — its place in the stack (left, click any layer) and its internal components (right). Telemetry rises; control descends.

What this layer does

Cloud HPC, bare metal, and supercomputing — the offline multi-physics and batch-training substrate. Every page in this section is part of the same control stack — click any card to go deeper, or return to the Master Blueprint to see how it connects.

Explore this layer

Activation and Material Damage ModelingAmbipolar Potential SolverBare-Metal Provisioning and DeterminismBatch Retraining PipelineCheckpointing and Fault ToleranceCloud HPC vs Bare-Metal ComputeCold, Warm, and Hot Storage TiersCompute Scheduling and OrchestrationContainerized Simulation EnvironmentsCoupled Multi-Physics Coupling MathematicsData Escalation TiersDeterministic Simulation SeedsDisruption Simulation for the BreederDistributed Training StrategyExascale ConsiderationsFinite-Element DiscretizationGNN Training on Sensor TopologyGPU Cluster ArchitectureGPU Clusters for Millisecond Twin SynchronizationGrad-Shafranov Equilibrium Solver FarmGyrokinetic Turbulence SimulationsHyperparameter Search FarmInterconnect Fabric and Collective CommunicationMHD Simulation WorkloadsMixed-Precision Numerical StrategyMonte Carlo Variance ReductionNegative-Triangularity Shape ScansNeutronics Monte Carlo for the BreederNeutronics Monte Carlo for the BurnerNuclear Cross-Section Data PipelinePINN Training WorkloadsPetabyte Pulse HistoriesReproducibility of ComputeStorage Array ArchitectureSupercomputing for Offline Multi-Physics Monte CarloSurrogate Model GenerationTandem-Mirror Equilibrium for the BurnerThe Compute SubstrateThe Edge-to-Cloud PipelineThe Hardware Abstraction LayerThe L0 Foundation LayerThe Latency GradientThe Mathematics of the Simulation WorkloadsThe Neutron Transport EquationThe Twin Refinement LoopTime-Integration SchemesTraining-Data Curation from Pulse HistoriesTransport Simulation WorkloadsTritium Breeding Ratio Parameter SweepsUncertainty QuantificationVerification and Validation of Simulation CodesWhy Offline Compute Is Separated from Real-Time
Content reviewed August 2026 · design-and-simulation stage