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.
K, for that look at a kernel subsystem/feature called cpu_isol, friend of mine implemented/upstreamed, basically you take cores half out of Linux and can use them for heavy workloads.
But I doubt you’d see more than 1% improvement, linux doesn’t do that much without you asking.
You can try setting rt priority but I’ve never found that to matter much.
Listen, this is the kind of thing I would have tried a decade ago, but the thing to remember is: time spent improving algorithm is generally more effective than time trying to optimize kernel overhead that millions of people have been trying to optimize for decades.