[hereonout2]

I've worked professionally over the last 12 months hosting quite a few foundation models and fine tuned LLMs on our own hardware, aws + azure vms and also a variety of newer "inference serving" type services that are popping up everywhere.

I don't do any work with the output, I'm just the MLOps guy (ahem, DevOps).

You mention expense but on a purely financial basis I find any of these hosted solutions really hard to justify against GPT 3.5 turbo prices, including building your own rig. $5k + electricity is loads of 3.5 Turbo tokens.

Of course none of the data scientists or researchers I work with want to use that though - it's not their job to host these things or worry about the costs.

[hehdhdjehehegwv]

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.