Case Study / AI & Machine Learning
Cutting model training time with a right-sized GPU cluster
A ai & machine learning client
A machine learning team outgrew single-GPU workstations and needed multi-node training that actually scaled.
9 hours
Time to train a full model
was 4.5 days
~92%
GPU utilization during training
was ~40%
Challenge
Training runs on single-GPU workstations were taking days, and adding GPUs without matching interconnect bandwidth had previously failed to speed things up.
Approach
We deployed a multi-node GPU cluster with high-bandwidth interconnect and parallel filesystem storage, sized so throughput scaled with GPU count instead of plateauing.
Infrastructure Deployed
“We stopped waiting on training runs to plan the next experiment.”