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I just won an auction for 25 computers. What should I setup on them? (reddthat.com)

submitted 2 years ago* (last edited 2 years ago) by Trainguyrom@reddthat.com to c/selfhosted@lemmy.world

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I placed a low bid on an auction for 25 Elitedesk 800 G1s on a government auction and unexpectedly won (ultimately paying less than $20 per computer)

In the long run I plan on selling 15 or so of them to friends and family for cheap, and I'll probably have 4 with Proxmox, 3 for a lab cluster and 1 for the always-on home server and keep a few for spares and random desktops around the house where I could use one.

But while I have all 25 of them what crazy clustering software/configurations should I run? Any fun benchmarks I should know about that I could run for the lolz?

Edit to add:

Specs based on the auction listing and looking computer models:

4th gen i5s (probably i5-4560s or similar)
8GB of DDR3 RAM
256GB SSDs
Windows 10 Pro (no mention of licenses, so that remains to be seen)
Looks like 3 PCIe Slots (2 1x and 2 16x physically, presumably half-height)

Possible projects I plan on doing:

Proxmox cluster
Baremetal Kubernetes cluster
Harvester HCI cluster (which has the benefit of also being a Rancher cluster)
Automated Windows Image creation, deployment and testing
Pentesting lab
Multi-site enterprise network setup and maintenance
Linpack benchmark then compare to previous TOP500 lists

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[–] Diabolo96@lemmy.dbzer0.com 1 points 2 years ago

Completely forgot to tell you to only use quantized models. Your pc can run 4bit quantized versions of the models I mentioned. That's the key for running llms on at consumer level hardware. You can later read further about the different quantizations and toy with other ones like Q5_K_M and such.

Just read phi-3 got released and apparently it's a 4B that reach gpt 3.5 level. Follow the news and wait for it to be add to ollama/llama.ccp

Thank you so much for taking the time to help me with that! I'm very new to the whole LLM things, and sorta figuring it out as I go

I became fascinated with llms after the first AI booms but all this knowledge is basically useless where I live, so might as well make it useful by teaching people what i know.