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The phrase “unsustainable gigantic moral hazard” applies to the current pharmaceutical and health industry. You know, the one that killed millions with opioids.

As far as I am concerned, burn it all down and put the most unhinged cokehead VCs in charge, who cares at this point. Hell, add some crypto AI NFTs in the mix while we’re at it.

Literally cannot get worse than it is now.

I guess that would make AppleCare a bucket of compost.

Sell NVIDIA, buy buckets of worms, profit.

Literally all smartphones dying at once is preferable because they are all backed up into heavy duty databases. You can restore every phone from the cloud in this thought experiment.

You can not restore a data center from a pile of phones.

I didn’t comment earlier so I can’t move a goalpost I never set down. I agree with the root comment that there may be more instances of SQLite, the most important data is not in SQLite.

For what it’s worth, I have used any number of databases over the years and SQLite is very good for a number of things.

None of those things are the core infrastructure that stores your emails, money, and other must have, shared, high availability data.

There are different tools for different jobs, that’s fine.

The number of societal-wide problems that occur if your Lightroom database gets corrupted is zero.

The amount of hell that would be unleashed if the financial systems layers upon layers of database transactions got broken is impossible to comprehend.

So if you mean “important” as “necessary for society to function”, then no, your browser bookmark files, contact list, or the other two dozen things your laptop and phone use SQLite for are not important.

Yeah and the GitHub for .cpp links directly to ollama - it’s literally advertised by the core maintainer as a good project.

Yeah, I’m busy doing actual work - Ollama acting as a wrapper is exactly what I want.

I answered the financial thinking in another reply, but another factor is I need to know if the model today is exactly the same as tomorrow for reliable scientific benchmarking.

I need to tell if I change I made was impactful, but if the model just magically gets smarter or dumber at my tasks with no warning then I can’t tell if I made an improvement or a regression.

Whereas the model on my GPU doesn’t change unless I change it. So it’s one less variable and LLM are black box to start with.

I may be wrong for Gemini, but my impression is all the companies are constantly tweaking the big models. I know GPT on Monday is not always the same GPT on Thursday for example.

So my main motivation is not so much to have the lowest cost, but to have the most predictable cost.

Knowing up front this is my fixed ML budget gives me peace of mind and gives me room to try stupid ideas without worrying about it.

Whereas doing it in the cloud you can a) get slammed with some crazy bill by accident, b) have to think more about what resources testing an idea will take, or conversely c) getting GPU FOMO and thinking “if just upgrade a level all my problems will be solved”.

It works for me, everybody mileage varies but personally I like to budget; spend; and then totally focus on my goals and not my cloud spend.

I’m also from the pre-cloud era, so doing stuff on my own bare metal is second nature.

I’m doing non-interactive tasks, but in terms of the A6000 running llama3 70b in chat mode it’s as usable as any of the commercial offerings in terms of speed. I read quickly and it’s faster than I read.

Doing really nuanced classification of documents at very large scale. Needle in the haystack type problems.

Nope, powers some low-level infrastructure-ish stuff.

Absolutely phenomenal.