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this post was submitted on 24 Jul 2024
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Typically you need about 1GB graphics RAM for each billion parameters (i.e. one byte per parameter). This is a 405B parameter model. Ouch.
Edit: you can try quantizing it. This reduces the amount of memory required per parameter to 4 bits, 2 bits or even 1 bit. As you reduce the size, the performance of the model can suffer. So in the extreme case you might be able to run this in under 64GB of graphics RAM.
Or you could run it via cpu and ram at a much slower rate.
Yeah uh let me just put in my 512GB ram stick…
Samsung do make them.
Goodluck finding 512gb of VRAM.
https://www.ebay.com/p/116332559 lga2011 motherboards quite cheap, insert 2 xeon 2696v4 44 threads each totalling at 88 threads and 8 ddr4 32gb sticks, it comes quite cheap actually, you can also install Nvidia p40 with 24gb each, you can max out this build for ai for under 2000$
Finally! My dumb dumb 1TB ram server (4x E5-4640 + 32x32GB DDR3 ECC) can shine.
At work we habe a small cluster totalling around 4TB of RAM
It has 4 cooling units, a m3 of PSUs and it must take something like 30 m2 of space
When the 8 bit quants hit, you could probably lease a 128GB system on runpod.
Can you run this in a distributed manner, like with kubernetes and lots of smaller machines?
According to huggingface, you can run a 34B model using 22.4GBs of RAM max. That's a RTX 3090 Ti.
Hmm, I probably have that much distributed across my network... maybe I should look into some way of distributing it across multiple gpu.
Frak, just counted and I only have 270gb installed. Approx 40gb more if I install some of the deprecated cards in any spare pcie slots i can find.
Ypu mean my 4090 isn't good enough 🤣😂