Not trivial for the general public at all, and furthermore, you need much more memory for finetuning than for inference, often making it infeasible for many machine/model combinations.
If you are running a local LLM already (which no one in the "general public is") then the bar is really not that much higher for fine-tuning (either for an individual or community member to do).
And you don't need any additional equipment at all. When I say trivial, I really do mean it - you can go to https://www.together.ai/pricing and see for yourself - a 10M token 3 epoch fine tune on a 7B model will cost you about $10-15 right now. Upload your dataset, download your fine tune weights (or serve via their infrastructure). This is only going to get easier (compare how difficult it was to inference local models last year to what you can do with plug and play solutions like Ollama, LM Studio, or Jan today).
Note also that tuning is a one-time outlay, and merges are even less resource intensive/easier to do.
To put things in perspective, tell me how much cost and effort it would be to tune a model where you don't have the weights at all in comparison.
Running a local LLM - downloading LM studio, installing on Windows, using the search function to search for a popular LLM, click "download", click the button to load the model, chat.
Fine-tuning - obtaining a dataset for your task (this in itself is not trivial), figuring out how the service you linked works (after figuring out that it exists at all), uploading the dataset, paying, downloading the weights - OK, now how do you load them into LM studio?
It's all subjective, of course, but for me there's a considerable difficulty jump there.
