CPU + GPU: Why AI Platform Engineering Is A Heterogeneous Infrastructure Problem
CNCF, Friday, September 4th, 2026
Optimizing only the GPU misses the point: AI pipelines are CPU, memory, storage and network problems too.
AI platform engineering is presented as a heterogeneous infrastructure problem rather than a GPU procurement exercise.
Production inference pipelines chain interdependent stages -- data preparation, inference, post-processing -- each with distinct CPU, GPU, memory, storage and networking profiles
The author's rule of thumb is to follow the workload, not the GPU, and optimize the complete pipeline as one system. Kubernetes features such as Dynamic Resource Allocation help match specialized hardware to specific stages, while observability at every handoff point is what actually surfaces bottlenecks and wasted capacity.