The OpenAI fine-tuning api is pretty good - you need to label an evaluation benchmark anyway to systematically iterate on prompts and context, and it’s often creates good results if you give it a 50-100 examples, either beating frontier models or allowing a far cheaper and faster model to catch up.

It requires no local gpus, just creating a json and posting to OpenAI

https://platform.openai.com/docs/guides/model-optimization

This website loads at impressive speeds (from Europe)! Rarely seen anything more snappy. Dynamic loading of content as you scroll, small compressed images without looking like it (webp). Well crafted!

Fine tuning by pretraining over a RL tuned model is dumb AF. RL task tuning works quite well.

Fine-tuning is a good technique to have in a toolbox, but in reality, it is feasible only in some use cases. On one hand, many NLP tasks are already easy enough for LLMs to have near perfect accuracy and fine tuning is not needed. On the other hand, really complex tasks are really difficult to fine-tune and clevem data collection might be pretty expensive. Fine-tuning can help with the use cases somewhere in the middle, not too simple, not too complex, feasible for data collection, etc.

A couple of examples I have seen recently which makes me agree with OP:

- PaddleOCR, a 0.9B model that reaches SOTA accuracy across text, tables, formulas, charts & handwriting. [0]

- A 3B and 8B model which performs HTML to json extraction at GPT-5 level accuracy at 40-80x less cost, and faster inference. [1]

I think it makes sense to fine tune when you're optimizing for a specific task.

[0] https://huggingface.co/papers/2510.14528

[1] https://www.reddit.com/r/LocalLLaMA/comments/1o8m0ti/we_buil...