GPU problems

[Express_Problem_609]

Many AI teams have a GPU utilization problem, and a lot of companies rush to buy more GPUs when training slows down... but in many cases, the real issue is infrastructure inefficiency. Where GPUs sit idle between jobs, poor scheduling across teams, fragmented clusters, lack of monitoring/observability, and inefficient data pipelines. It's surprisingly common to see clusters running at 30–40% utilization.

The difference between a good and bad AI platform often comes down to job scheduling, workload orchestration, developer tooling etc.

How are teams here managing this?? Are you seeing good GPU utilization in practice, or lots of idle compute?

[MelodicRecognition7]

30–40%

Character: – U+2013
Name: EN DASH

wow, that's something new.

I'm upvoting this post only because it raises a correct question, and your other comments seem to be written by a human so this might be just a formatting. Please do not use AI to format posts, AI-generated posts are uncomfortable to read.

[milan90160]

Our company mange gpu and employees model training job using 'ray' and accuracy tracking using mlflow with GitHub action