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Everyone with “only” 64GB of RAM is pouting today, including me
by gymbeaux 4y ago
Everyone with “only” 64GB of RAM is pouting today, including me
- Taek 4y agoYou can run llama using 4 bits per parameter, 64 GB of RAM is more than enough
- geysersam 4y ago4 bits is ridiculously little. I'm very curious what makes these models so robust to quantization.
- MacsHeadroom 4y agoRead The Case for 4 Bit Precision. https://arxiv.org/abs/2212.09720 https://arxiv.org/abs/2212.09720 Spoiler: it's the parameter count. As parameter count goes up, but depth matters less. It just so happens that at around 10B+ parameters you can quantize down to 4bit with essentially no downsides. Models are that big now. So there's no need to waste RAM by having unnecessary precision for each parameter.
- Taek 4y agoFor completeness, there's also another paper that demonstrated you get more power/accuracy per-bit at 4 bits than at any other level of precision (including 2 bits and 3 bits)
- MacsHeadroom 4y agoThat's the paper I referenced. But newer research is already challenging it. 'Int-4 llama is not enough [0] - Int-3 and beyond' suggests 3-bit is best for models larger than ~10B parameters when combining binning and GPTQ. [0] https://nolanoorg.substack.com/p/int-4-llama-is-not-enough-int-3-and https://nolanoorg.substack.com/p/int-4-llama-is-not-enough-i...
- metadat 4y agoWhat if you have around 400GB of RAM? Would this be enough?
- gymbeaux 4y agoWhat I'm referring to requires around 67GB of RAM. With 400GB I would imagine you are in good shape for running most of these GPT-type models.
- numpad0 4y agoMore like finally "proven right" to have needlessly kept feeding 4/5th of 64GB to Chrome since 2018