Theory: running AI workload spreaded across various devices using pipeline parallel inference In theory Exo provides a way to run memory heavy AI/LLM models workload onto many different devices spreading memory and computations across. They say: “Unify your existing devices into one powerful GPU: iPhone, iPad, Android, Mac, NVIDIA, Raspberry Pi, pretty much any device!“ People say: “It requires mlx but it is an Apple silicon-only library as far as I can tell. How is it supposed to be (I quote) “iPhone, iPad, Android, Mac, Linux, pretty much any device” ? Has it been tested on anything else than the
Due to previous failure of SSD drive from Goodram I was forced to use brand new 1TB HDD from Toshiba. It was not a problem because the system running on it mainly have been using writes with not too much reads. My SSD drive had some performance drops which could be because of the fact being run out of the same power socket shared with some DIY tools in garage. Now there is no power socket sharing I think that I may close server lid with too much force, so even brand new HDD failed. Proxmox reported failure of disk
In case you use ever changing outbound public IP connection like in Microsoft Azure, then you can try create machine with public IP and passing your local traffic to remote site via simplaproxy. L switch is for local and R is for remote. To make it durable you can try creating systemd service or keep it open on screen session.
In one of my previous posts I mentioned some troubles regarding reinstalling Ubuntu 22, loosing ability to select OS and to boot at all actually. I found that Ubuntu 20 recognizes properly my fresh Windows installation but Ubuntu 22 does not. So I stayed with version 20 however here was no OS selection, which translates to broken GRUB installation. After Ubuntu 20 installation finished it tried to put bootloader but failed to do this because of drives numbering. My first drive in Lenovo Thinkpad T420s is mSATA but computer and operating system thinks that this is my second drive. My