Your LoRA Fit Yesterday. Today the Dataset Did Not.

Yesterday the LoRA run looked fine. Today the dataset got bigger, sequence length changed, and the same GPU suddenly felt too small.

Why this keeps happening

• people assume one successful run means the setup is future-proof
• dataset growth quietly changes memory and runtime behavior
• batch size, context length, and checkpointing can shift the cost fast
• LoRA is cheap compared to full fine-tuning, but it still punishes bad GPU sizing

The mistake

A lot of people go from one failed run to "I need an H100 now." Usually the better move is to step up only as far as the workload actually forces you.

Practical rule

• keep using RTX 4090 if smaller LoRA or QLoRA work still fits
• move to A100 80GB when dataset growth and sequence length keep pushing memory
• only evaluate H100 when the fine-tune is already obviously huge

The simple takeaway

If yesterday's LoRA fit and today's does not, the problem is usually not magic. The workload changed, and now the old GPU choice is being honest with you.

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