There are a lot of discussions happening in AI infrastructure right now. On one side, we have researchers who trained on Slurm in grad school, comfortable with sbatch train_model.sh and the predictability of academic HPC clusters. On the other side, we have platform engineers who’ve spent the last several years of their career mastering Kubernetes, building sophisticated cloud-native architectures for web-scale applications. The problem? Modern AI workloads don’t fit cleanly into either w...
