Was running a qlora fine tune last week on our usual setup and something felt off. gpu utilization was sitting around 30% the entire run and epoch times were way slower than i expected. First thing i did was blame the model, thought maybe the lora rank was too high or i had some weird backward pass issue.

Nope, ran pytorch profiler and cuda was mostly idle just waiting on data. classic starvation, the loader was the bottleneck the whole time and i had been sitting there tuning the wrong thing. 

The config was embarrassingly default. num\_workers=0, no pin\_memory, no prefetch, no persistent workers. Basically the loader was single threaded doing disk read then serving one batch at a time while the gpu waited. fixed it with. 

DataLoader(

dataset,

num\_workers=8,

pin\_memory=True,

prefetch\_factor=2,

persistent\_workers=True,

)

Utilization went from \~30% to somewhere in the 80-90% range on same hardware, same model, same dataset. total training time dropped by about half. no code changes to the model at all and just the loader.

This was running on hyperai btw, does not really matter where you run it though, the fix is the same anywhere you're on pytorch. what actually mattered was profiling instead of guessing. I had spent like two hours before this trying to figure out if it was a rank issue or a batch size thing when the whole time cuda was just twiddling its thumbs waiting for the next tensor.

Lesson i keep relearning is if gpu util is low, it is almost always the data pipeline not the model(profile first, always). The harder cases are the ones where its network storage latency or slow decoding not just loader defaults, and i have not had to deal with distributed fs latency on this workload yet but i have heard horror stories from people who have hit that wall.

I am sure everyone has their own version of this. bet the sneakiest ones are not even the well known bottlenecks, it is always the thing you least expected.

Finally figured out why my qlora job was crawling and it wasn't the model at all