3 ms·
> so that it will be possible to do the inference in low ram doing something similar to "paging". I was also hoping for that, but my initial research suggests
by tarruda 3y ago
> so that it will be possible to do the inference in low ram doing something similar to "paging".
I was also hoping for that, but my initial research suggests that you need to fit everything into VRAM (or RAM when doing llama.cpp CPU inference).
The upside is that it should be as fast as running a single 7B model, which on llama.cpp should be in the ballpark of 10 tok/sec when 4-bit quantized.
For local usage, I'd say you will have an "OK" experience in a recent laptop with 64GB RAM, which is more realistic than having the necessary VRAM in a Nvidia GPU.
- tarruda 3y agoCorrection, it should have the performance of a single 12B parameter model: "Concretely, Mixtral has 45B total parameters but only uses 12B parameters per token. It, therefore, processes input and generates output at the same speed and for the same cost as a 12B model." https://mistral.ai/news/mixtral-of-experts/ https://mistral.ai/news/mixtral-of-experts/