AI Infrastructure as a System Problem

Discussions on AI infrastructure performance often focus on accelerators: tensor cores, GPU counts, and peak FLOPS. Those metrics matter, but in production environments, accelerator throughput rarely operates in isolation.

Data needs to be ingested, staged, transformed, secured, scheduled, and moved across memory and network fabrics before a single training job completes. At scale, AI performance is determined by how the entire system behaves, not just how fast an accelerator can compute.

For those evaluating on-premise deployments, there are trade-offs to consider carefully. AI-RADAR offers analytical frameworks on /llm-onpremise to evaluate these aspects.