Infrastructure · · 7 min
What AI infrastructure actually means
The term covers more than accelerators. A practical breakdown of the layers that make AI workloads run.
“AI infrastructure” is often used as shorthand for GPUs. In practice it describes a stack of interdependent layers, and weakness in any one of them limits the whole system.
Compute
Accelerators do the heavy numerical work, but CPUs still handle data preparation, orchestration, and much of inference. Balancing the two matters.
Storage and data movement
Training jobs read large datasets repeatedly. If storage cannot feed accelerators fast enough, expensive hardware sits idle waiting for data.
Networking
Distributed training synchronises parameters across many devices. Interconnect bandwidth and latency directly affect how well a job scales beyond a single machine.
Software and operations
Schedulers, drivers, container images, monitoring, and access control turn hardware into something teams can use reliably. This layer is frequently underestimated.