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343 points sillysaurusx | 2 comments | | HN request time: 0.535s | source
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v64 ◴[] No.35028738[source]
If anyone is interested in running this at home, please follow the llama-int8 project [1]. LLM.int8() is a recent development allowing LLMs to run in half the memory without loss of performance [2]. Note that at the end of [2]'s abstract, the authors state "This result makes such models much more accessible, for example making it possible to use OPT-175B/BLOOM on a single server with consumer GPUs. We open-source our software." I'm very thankful we have researchers like this further democratizing access to this data and prying it out of the hands of the gatekeepers who wish to monetize it.

[1] https://github.com/tloen/llama-int8

[2] https://arxiv.org/abs/2208.07339

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1. nextaccountic ◴[] No.35030214[source]
If the model weights are stored as int8, does this mean that the floating point capacity of the GPU is wasted? Or the int8 is converted to float in the GPU?
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2. woodson ◴[] No.35032440[source]
Well, tensor cores support int8 instructions (at least from Turing onwards), so the hardware is being used, if that’s your concern.