The code is fine. The dataset is fine. The tutorial looked easy.
Then your run dies because the model does not fit where you thought it would.
Why this keeps happening
- tutorials make the setup look smaller than it really is
- batch size, context length, and adapters change the memory story fast
- people optimize for model hype instead of GPU fit
- "it runs" and "it trains comfortably" are not the same thing
What to do instead of guessing
Start with the smallest GPU that can actually hold the run.
If LoRA or QLoRA gets the job done on a 4090, that is the right answer.
Move to A100 80GB when memory becomes the real blocker.
Use H100 only when you already know the smaller cards cannot hold the workload.
The common mistake
A lot of people burn money after the first VRAM error.
They jump from "this failed on my current setup" to "I need the biggest GPU available."
Usually the better move is one step up, not three.
The practical rule
A VRAM error does not mean "rent the most expensive GPU."
It means your current setup is too small for the job.
Fix that gap with the cheapest reliable step up.
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