This is an abstract curiosity. Let’s say I want to use an old laptop to run a LLM AI. I assume I would still need pytorch, transformers, etc. What is the absolute minimum system configuration required to avoid overhead such as schedulers, kernel threads, virtual memory, etc. Are there options to expose the bare metal and use a networked machine to manage overhead? Maybe a way to connect the extra machine as if it is an extra CPU socket or NUMA module? Basically, I want to turn an entire system into a dedicated AI compute module.
That’s interesting, I spent a decade doing hpc and other optimizations for large software on 2 socket systems, there are degenerate cases, which can be fixed, I just doubt they’re here.
Freecad sounds like it was poorly written with a lot of hopping about ram with poor cache localization, which happens but is pretty ugly.
Ml tends to be better behaved, it’s actually very close to dsp code and the compilers try to enforce locality, more importantly a lot of the modules are hand coded for extreme performance.
I’m not trying to be that discouraging, I’m saying this as someone who originally looked for performance, and often found it in the os, but later found more performance in the loops themselves or the compiler, basically linux is a lot smarter than it used to be, and many applications are too.
Just my 2c, there are performance tools that can tell you how bad the os is vs other things, and you shouldn’t be swapping so much that it hurts you a lot in ml.