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A single 3090, or any 24GB GPU. Just barely. Yi 34B is a much better fit. I can cram 75K context onto 24GB without brutalizing the model with <3bpw quantizatio
by brucethemoose2 3y ago
A single 3090, or any 24GB GPU. Just barely.
Yi 34B is a much better fit. I can cram 75K context onto 24GB without brutalizing the model with <3bpw quantization, like you have to do with 70B for 4K context.
- speedgoose 3y agoCan it produce any meaningful outputs with such an extreme quantisation?
- brucethemoose2 3y agoYeah, quite good actually, especially if you quantize it on text close to what you are trying to output. Llama 70B is a huge compromise at 2.65bpw... This does make the much "dumber." Yi 34B is much better, as you can quantize it at ~4bpw and still have a huge context.
- lossolo 3y agoHow would you compare mistral-7b-instruct 16fp (or similar 7b/13b model like llama2 etc) to Yi-34b quantized?
- brucethemoose2 3y ago34B is better. Quantization hurts some, especially in "pro" non chat use cases like RAG, but the increased parameter count makes models so much smarter in comparison. The perplexity graph here is a pretty good illustration: https://github.com/ggerganov/llama.cpp/pull/1684 https://github.com/ggerganov/llama.cpp/pull/1684 YMMV, as Mistral and Yi are not necessarily comparable like different sizes of llama, and it depends on the task.