AI Training Systems

Training System Architecture

A modern AI training system comprises GPU compute nodes (typically 8 GPUs per server), high-bandwidth interconnects (high-bandwidth GPU interconnect within node, InfiniBand between nodes), a parallel file system for dataset loading and checkpoint storage, and orchestration software (Kubernetes, Slurm, or proprietary schedulers) that manages job scheduling, fault tolerance, and multi-tenant isolation.

Training systems are designed for sustained maximum utilisation — a training run lasting weeks must maintain 85%+ GPU utilisation to be economically viable. Any bottleneck (network, storage, power, cooling) that drops utilisation below 70% can increase training costs by 40% or more.

Frontier vs. Enterprise Training

Frontier model training (GPT-4, Gemini, Llama) requires 10,000–100,000+ GPUs running for months, with total compute costs exceeding USD 100 million per training run. These systems use full-fat-tree InfiniBand topologies, dedicated power infrastructure (50–100+ MW), and custom orchestration.

Enterprise training and fine-tuning requires 8–128 GPUs for hours to days, with compute costs of USD 1,000–50,000 per run. These workloads are ideal for multi-tenant GPU cloud platforms where costs are shared across organisations. Constellation's infrastructure serves both tiers — dedicated clusters for frontier-scale clients and GPUaaS for enterprise fine-tuning.

Fault Tolerance & Checkpointing

Large-scale training systems must handle GPU failures gracefully. With 10,000+ GPUs, individual GPU failures are statistically guaranteed during a multi-week training run. Training systems implement automatic checkpointing (saving model state every 30–120 minutes), failure detection, and automatic recovery — ensuring a single GPU failure doesn't lose more than 1–2 hours of training progress.

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