AI Platform Engineering Bottlenecks Lie in Distributed Systems, Not ML Models

A technical deep-dive published on DEV Community argues that the hardest challenges in AI platform engineering stem from distributed systems and scheduling rather than machine learning itself. The analysis focuses on four core technologies — GPUs, Ray, vLLM, and Kubernetes — and how their interaction creates infrastructure bottlenecks at scale. Kubernetes' default scheduler treats GPUs as generic resources, ignoring memory fragmentation and compute intensity, which can leave hardware running at as low as 30% utilization. Tools like NVIDIA's Device Plugin and custom Kube-scheduler policies offer solutions but require precise tuning to be effective. Additional concerns include thermal throttling under heavy GPU loads, multi-tenancy conflicts in shared clusters, and PCIe bus bottlenecks that degrade large language model inference throughput.