Comment by adrian_b

Fine-tuning for a specific task is even much less realistic than the benchmarks shown in TFA.

Most people who have computers could run inference for even the biggest LLMs, albeit very slowly when they do not fit in fast memory.

On the other hand, training or even fine tuning requires both more capable hardware and more competent users. Moreover the effort may not be worthwhile when diverse tasks must be performed.

Instead of attempting fine-tuning, a much simpler and more feasible strategy is to keep multiple open-weights LLMs and run them all for a given task, then choose the best solution.

This can be done at little cost with open-weights models, but it can be prohibitively expensive with proprietary models.