GPU Cluster Architecture
The GPU partitions where Kronos trains PINNs, GNNs, surrogates, and anomaly ensembles for both the breeder and the burner.
GPUs as the training engine of L0
The GPU partitions of L0 exist to train the models that later run in the real-time twin. This is dense, memory-bandwidth-bound work: physics-informed neural networks solving Grad-Shafranov and MHD residuals, graph neural networks over the diagnostic sensor topology, surrogate emulators of expensive solvers, and anomaly-detection ensembles for sub-threshold quench precursors.
Node and memory design
Nodes are built around high-bandwidth GPU memory and fast intra-node GPU-to-GPU links, because PINN training is dominated by repeated evaluation of differential-operator residuals over large collocation sets. The bottleneck is rarely raw arithmetic; it is feeding tensors and gradients through memory. Kronos sizes memory per GPU to hold the largest single-machine twin module without sharding.
Multi-GPU and multi-node scaling
Larger models shard across GPUs with tensor and data parallelism; large sweeps run one model per GPU in parallel. The interconnect fabric carries gradient all-reduce traffic for distributed training. For the breeder disruption-precursor ensembles, many small models train concurrently, so throughput matters more than single-model scale.
- High-bandwidth GPU memory sized to full twin modules
- Fast intra-node GPU links for tensor-parallel PINNs
- RDMA interconnect for gradient all-reduce across nodes
- Local NVMe scratch for training-shard streaming
The clusters serve both machines from one pool. A breeder equilibrium PINN and a burner ambipolar-potential surrogate can train side by side; only their data and residual definitions differ. Machine specialization lives in the loss functions and datasets, not the silicon.
Trained artifacts do not stay on the GPUs. They are versioned, validated, then compiled and quantized for the real-time twin and, where applicable, down to the L1 edge. The GPU clusters are the forge; the fast layers are where the finished tools are used.