Doesn’t mean it isn’t possible

It's exciting to see that ChatGPT's cost is 1/10th that of GPT-3 API, which is a huge advantage for developers who are looking for high-quality language models at an affordable price. OpenAI's commitment to providing top-notch AI tools while keeping costs low is commendable and will undoubtedly attract more developers to the platform. It's clear that ChatGPT is a superior option for developers, and OpenAI's dedication to innovation and affordability is sure to make it a top choice for many in the AI community.

Just make a company account and transfer the app to the company account with the western name

You should change your developer name. Seeing Chinese characters on an app listing is a huge red flag for westerners. Come up with some English pen-name

As soon as we can fine tune it to our problem space, we are 100% putting it as a help bot in our commercial software. It’s ready, it just needs tuning.

Is this just training? What about inferences? How does chatGPT serve millions of people so quickly if it needs such enterprise hardware per request

but potentially useful for say learning a new framework.

Go pick up a new tool, and walk through it and it can explain some concepts, like react hooks and stateful management and why the code does what it does

It is better as an interactive teacher than a code writer today.

Desktop

I don't even think you can use CPU to make images using stable diffusion, but maybe you can.

Yeah my M1 Pro takes about 25-30 seconds per image, some of that has to do with configuration. But my RTX 3070 cranks them about in about 4 to 5 seconds per image.

By blazing fast he means as fast as a gtx 1060. My 3070 is 5x faster than my M1 Pro

I'll add the value of machine learning is the dynamic nature of the solution. In a production situation most likely, retraining quickly with weaker hyperparameters every day would lead to a higher total applied accuracy than retraining once a month with hyperparam tuning. IF the hyperparam solution is actually better, then the problem space is very static, and you might want to rethink your ML approach

hyperparameter tuning should be a last step, not really necessary for 99% of production workloads, and really only for getting results publishable for papers.

I'd avoid it if possible and just go with reasonable hyperparameters. If you reach a breaking point where you can't get any better without tuning, then decide if you are trying to publish and need more accuracy, then bite the bullet and wait to publish until you finish the search, or if it is a business case, try to determine if the extra revenue from extra accuracy could offset the cost of extra compute.