Clusters
GPU clusters are groups of GPU-accelerated servers connected by high-bandwidth interconnects — forming the compute backbone of AI training and inference at scale. Understanding cluster architecture is essential to evaluating AI infrastructure investments.
A GPU cluster is built from GPU nodes (servers containing 4–8 GPUs each), connected by a network fabric (InfiniBand or 400G Ethernet), with shared access to a parallel storage system. A small cluster might have 16 GPUs (2 nodes), while frontier-scale clusters exceed 10,000 GPUs (1,250+ nodes).
Each GPU node contains: 8 GPUs (e.g., prior-generation GPU or frontier GPU), 2 CPUs for orchestration, 1–2 TB of system RAM, high-bandwidth GPU interconnect interconnect within the node (900 GB/s), 4–8 high-speed network interfaces for inter-node communication, and 8–16 TB of local NVMe storage for fast data access.
GPU cluster networking uses a fat-tree topology — a hierarchical switch arrangement that ensures non-blocking communication between any pair of GPUs. In a well-designed fat-tree, any GPU can send data to any other GPU at full bandwidth with minimal congestion. This is critical for distributed training, where all GPUs must synchronise model parameters after each training step.
InfiniBand (HDR 200 Gb/s or NDR 400 Gb/s) is the standard for AI clusters, providing lower latency (1–2 microseconds) and higher bandwidth than Ethernet. Some clusters use 400G RoCE (RDMA over Converged Ethernet) as a cost optimisation, accepting slightly higher latency for reduced networking costs.
A 1,000-GPU cluster (125 nodes) requires approximately USD 35–40 million in GPU and server hardware, USD 5–8 million in networking, USD 3–5 million in storage, and USD 10–20 million in facility infrastructure — a total capital investment of USD 55–75 million. At 70% utilisation and current GPUaaS pricing, annual revenue exceeds USD 12–15 million, yielding a 4–6 year payback.
Cluster economics improve with scale: larger clusters have lower per-GPU networking costs (switches are shared across more GPUs), better utilisation (larger workloads fill capacity more efficiently), and lower operational overhead per GPU. This is why hyperscale clusters outperform small deployments on unit economics.
A GPU cluster is a group of servers containing GPU accelerators, connected by high-speed networking (InfiniBand or 400G Ethernet) and shared storage, designed to work together on AI training or inference workloads. Clusters range from 16 GPUs (small) to 10,000+ GPUs (frontier-scale).
A 1,000-GPU cluster costs approximately USD 55–75 million including GPU servers, networking, storage, and facility infrastructure. At current GPUaaS pricing and 70% utilisation, it generates USD 12–15 million in annual revenue, yielding a 4–6 year capital payback.
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