GPU clouds for fine-tuning: choose by experiment size

Separate adapters from full-model training

LoRA changes the trainable parameter set, so its resource profile differs from updating every model weight. Both still need a base model, a dataset and enough working memory for the chosen sequence length. Avoid buying a cluster simply because the base model has a large parameter count.

Run a small pilot and record peak memory, ordinary step time, evaluation overhead and checkpoint size. Those measurements tell you whether the experiment fits on one device and whether the projected duration is affordable.

References: Hugging Face PEFT: LoRA

A shortlist by operating requirement

Lambda documents Linux GPU instances, Runpod documents customizable Pods, and CoreWeave and Nebius document cluster-oriented infrastructure. The recommended fit below is an editorial starting point; it does not establish which one trains your model fastest.

References: Lambda on-demand instances · Runpod Pods overview · CoreWeave documentation · Nebius Compute documentation

Put the checkpoint path into the quote

Ask where checkpoints will be stored and whether they can be restored onto another machine. Include storage capacity and write performance in the pilot. A fast GPU can spend a meaningful part of training waiting for checkpoint writes.

Keep evaluation artifacts too. The cheapest completed run is not a useful outcome if the team cannot establish whether the model improved or reproduce the result later.

When to expand beyond one device

Add GPUs after identifying a memory limit or a measured throughput benefit. Track how much training time falls when the device count increases and how much the hourly bill rises. Communication can make the cost per completed experiment worse even when elapsed time improves.

If a deadline justifies that extra expense, state it explicitly. Otherwise use the smaller deployment and spend the remaining budget on a better dataset or more controlled experiments.

Questions readers ask