Architecture
AI cloud architecture is the blueprint for delivering GPU compute, storage, and orchestration as a unified cloud platform — engineered for the unique demands of AI training, inference, and fine-tuning workloads.
An AI cloud platform comprises five architectural layers: the physical facility (power, cooling, security), the compute layer (GPU servers and accelerators), the storage layer (high-performance parallel file systems), the network fabric (InfiniBand or 400G Ethernet), and the orchestration layer (Kubernetes, Slurm, or custom schedulers).
Each layer must be co-designed — a bottleneck in any layer throttles the entire system. For example, a GPU cluster with insufficient storage bandwidth will starve GPUs during data loading, reducing utilisation from 90% to 50% or lower. This interdependency is why purpose-built AI clouds outperform retrofitted traditional clouds by 3–5x on AI workload efficiency.
Modern AI cloud architecture uses GPU virtualisation technologies — GPU partitioning (Multi-Instance GPU), vGPU, and time-slicing — to partition GPU resources across multiple tenants. This enables fractional GPU billing (per-GPU-hour), increases utilisation, and makes AI compute accessible to organisations that cannot justify dedicated GPU ownership.
Constellation's AI cloud architecture supports both dedicated (bare-metal) GPU allocation for enterprise and government clients with security requirements, and multi-tenant GPUaaS for research and development workloads where cost efficiency is prioritised.
AI training workloads require non-blocking, low-latency interconnects between GPU nodes. The standard architecture uses high-bandwidth GPU interconnect within a node (900 GB/s) and InfiniBand or 400G RoCE between nodes, arranged in a fat-tree topology that ensures any GPU can communicate with any other GPU with predictable, microsecond-level latency.
Network fabric design is the single most overlooked component of AI cloud architecture — a poorly designed fabric can reduce training throughput by 40% even with identical GPU counts. Constellation's deployments use full-fat-tree InfiniBand topologies engineered for frontier-scale training.
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