LocalLLaMA
Welcome to LocalLLaMA! Here we discuss running and developing machine learning models at home. Lets explore cutting edge open source neural network technology together.
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Rules:
Rule 1 - No harassment or personal character attacks of community members. I.E no namecalling, no generalizing entire groups of people that make up our community, no baseless personal insults.
Rule 2 - No comparing artificial intelligence/machine learning models to cryptocurrency. I.E no comparing the usefulness of models to that of NFTs, no comparing the resource usage required to train a model is anything close to maintaining a blockchain/ mining for crypto, no implying its just a fad/bubble that will leave people with nothing of value when it burst.
Rule 3 - No comparing artificial intelligence/machine learning to simple text prediction algorithms. I.E statements such as "llms are basically just simple text predictions like what your phone keyboard autocorrect uses, and they're still using the same algorithms since <over 10 years ago>.
Rule 4 - No implying that models are devoid of purpose or potential for enriching peoples lives.
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The new $3000 NVidia Digit has 128 GB of fast RAM in an Apple-M4-like unified-memory configuration, reportedly. NVidia claims it is twice as fast as an apple stack at least at inference. Four of these stacked can run a 405B model, again according to NVidia.
In my case I want the graphics power of an GPU and VRAM for other purposes as well. So I'd rather buy a graphics card. But regarding a 90B model, I do wonder if it is possible with two A6000 at 64 GB and a 3 bit quant.
Huh so basicly sidestepping the gpu issue entirly and essentially just using some other special piece of silicon with fast (but conventional ram). I still dont understand why u cant distribute a large llm over many different processors each holding a section of the parameters in memory.
Not exactly. Digits still uses a Blackwell GPU, only it uses unified RAM as virtual VRAM instead of actual VRAM. The GPU is probably a down clocked Blackwell. Speculation I've seen is that these are defective and repurposed Blackwells; good for us. By defective I mean they can't run at full speed or are projected to have the cracking die problem, etc.
Because each weight in a layer influences each weight in the next layer, which means the bandwidth requirements are enormous and regular networking solutions are insufficient for that.