Comment on The first GPT-4-class AI model anyone can download has arrived: Llama 405B

• modeler@lemmy.world ⁨1⁩ ⁨month⁩ ago

  Typically you need about 1GB graphics RAM for each billion parameters (i.e. one byte per parameter). This is a 408B parameter model. Ouch.

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  • cheddar@programming.dev ⁨1⁩ ⁨month⁩ ago

    Typically you need about 1GB graphics RAM for each billion parameters (i.e. one byte per parameter). This is a 405B parameter model.

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  • Deceptichum@quokk.au ⁨1⁩ ⁨month⁩ ago

    Or you could run it via cpu and ram at a much slower rate.

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    • errer@lemmy.world ⁨1⁩ ⁨month⁩ ago

      Yeah uh let me just put in my 512GB ram stick…

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      • Deceptichum@quokk.au ⁨1⁩ ⁨month⁩ ago

        Samsung do make them.

        Goodluck finding 512gb of VRAM.

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      • bruhduh@lemmy.world ⁨1⁩ ⁨month⁩ ago

        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$

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    • chiisana@lemmy.chiisana.net ⁨1⁩ ⁨month⁩ ago

      Finally! My dumb dumb 1TB ram server (4x E5-4640 + 32x32GB DDR3 ECC) can shine.

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  • Siegfried@lemmy.world ⁨1⁩ ⁨month⁩ ago

    At work we habe a small cluster totalling around 4TB of RAM.

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  • TipRing@lemmy.world ⁨1⁩ ⁨month⁩ ago

    When the 8 bit quants hit, you could probably lease a 128GB system on runpod.

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  • 1984@lemmy.today ⁨1⁩ ⁨month⁩ ago

    Can you run this in a distributed manner, like with kubernetes and lots of smaller machines?

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  • obbeel@lemmy.eco.br ⁨1⁩ ⁨month⁩ ago

    According to huggingface, you can run a 34B model using 22.4GBs of RAM max. That’s a RTX 3090 Ti.

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  • Longpork3@lemmy.nz ⁨1⁩ ⁨month⁩ ago

    Hmm, I probably have that much distributed across my network… maybe I should look into some way of distributing it across multiple gpu

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  • arefx@lemmy.ml ⁨1⁩ ⁨month⁩ ago

    Ypu mean my 4090 isn’t good enough 🤣😂

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• Blaster_M@lemmy.world ⁨1⁩ ⁨month⁩ ago

  As a general rule of thumb, you need about 1 GB per 1B parameters, so you’re looking at about 405 GB for the full size of the model.

  Quantization can compress it down to 1/2 or 1/4 that, but “makes it stupider” as a result.

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