Slurm is absolutely ubiquitous in the high-performance computing (HPC) community. I believe its only similar competitors in the HPC space are the SGE [1] and Torque/PBS [2] resource schedulers.

I'm not sure of the exact numbers, but I would guess that an overwhelming majority of the Top 500 Supercomputers [3] are running Slurm. And as others have noted, research computing centers in academia all mostly run Slurm. And Slurm also dominates in the DoE national labs in the US.

Oh, and as a [potentially apocryphal] fun fact, the name "Simple Linux Utility for Resource Management (SLURM)" is a backronym from the soda in Futurama! [4]

[1] https://en.wikipedia.org/wiki/Oracle_Grid_Engine

[2] https://github.com/adaptivecomputing/torque

[3] https://www.top500.org/

[4] https://futurama.fandom.com/wiki/Slurm

According to Wikipedia, "Slurm is the workload manager on about 60% of the TOP500 supercomputers." I have used it as a job manager front end for most computational clusters in the last 10 years or so.

Llama 2 models were trained on slurm

related, has anyone had success moving from Slurm to Kubernetes for a physical (non-cloud) cluster primarily used for training large models on lots of GPUs?

It's used in most high-performance computing clusters (except for the folks that are still on Torque, I guess).

Many compute clouds provide SLURM env - definitely AWS and GCP do.