The model loaded. The notebook worked. Then you increased context length, batch size, or both, and the whole GPU plan fell apart.
Why this happens so often
- a setup that fits at one context length can fail badly at another
- people test the smallest case and assume the real workload will behave the same way
- memory pressure climbs faster than most tutorials make it seem
- "it loaded once" and "it runs reliably" are completely different states
What people usually get wrong
A lot of people blame the code first. But a lot of the time the code is fine. The workload changed and the memory budget did not.
Then they jump straight to the biggest GPU. The better move is usually one practical step up, not a panic jump to the most expensive card.
Practical rule
- stay with RTX 4090 if the real workload still fits cleanly
- move to A100 80GB when longer context or memory-heavy runs keep breaking
- only evaluate H100 when the workload is already clearly huge
The simple takeaway
If the model loaded fine and context length broke the run later, the lesson is not "buy the biggest GPU."
The lesson is that your original memory assumption was too optimistic.
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