Same, esp. if you factor in the cost of renting. Even if you run 24/7 it's hard to see it paying off in half the time it will take to be obsolete
But to get commercially competitive models you need 5 figures of hardware, and then need to actually run it securely and reliably. Pay as you go with multiple vendors as fallback is a better option right now if you don't need harder privacy.
I think sooner or later I'll break down and buy a server for local inference even if the ROI is upside down because it would be a fun project. I also find that these thing fall in the "You don't know what you will do with it until you have it and it starts unlocking things in your mind"-category. I'm sure there are things I would have it grind on overnight just to test/play with an idea which is something I'd be less likely to do on a paid API.
Exactly. Once the price and performance get to the level where buying stuff for local training and inferencing… that is when we will start to see the LLM break out of its current “corporate lawyer safe” stage and really begin to shake things up.
Core Ultra Arc iGPU boxes are pretty neat too for being standalone and can be loaded up with DDR5 shared memory, efficient and usable in terms of speed, though that's definitely low end performance, plus SYCL and IPEX are a bit eh.
But you should still play with proxmox, just not for this purpose. My recommendation would be to get an i7 HP Elitedesk. I have multiple racks in my basement, hundreds of gigs of ram, multiple 2U 2x processor enterprise servers etc.... but at this point all of it is turned off and a single HP Elitedesk with a 2nd NIC added and 64GB of ram is doing everything I ever needed and more.
I have OpenWebUI and LibreChat running on my local “app server” and I’m quite enjoying that but every time I price out a beefier box I feel like the ROI just isn’t there, especially for an industry that is moving so fast.
Privacy is not something to ignore at all but the cost of inference online is very hard to beat, especially when I’m still learning how best to use LLMs.