Compute is only the first layer
GPU and accelerator capacity receives most of the attention, but production AI requires an ecosystem around that compute. Training systems need fast data access and high-bandwidth communication, while inference systems need predictable latency and efficient utilization.
Infrastructure design therefore starts with the workload: model size, training frequency, concurrency, response-time requirements and expected growth.
Storage and networking
AI systems move data between object storage, databases, training environments and production applications. Poor data architecture can leave expensive compute waiting for input.
Large training jobs also distribute work across many accelerators, so the network connecting those accelerators can directly affect how efficiently compute is used.
Power density and thermal design
AI hardware can create significantly higher rack densities than conventional enterprise deployments. That changes requirements for electrical distribution and heat removal.
Facilities designed for AI need to evaluate higher-density cooling approaches, power availability, redundancy and how quickly new capacity can be commissioned.