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The answer is RAM and llama.cpp my friend.
Mixtral fits easily in 64 Gb of RAM with llama.cpp, and that is much cheaper than VRAM. You can offload a bit of extra processing to your GPU to help and get tolerable speed from Mixtral. That's the beauty of the MoE approach, it's quicker to answer than other models for the same memory footprint. I get about 3 t/s on a 3070 with 64Gb of relatively slow RAM, less than that but still tolerable when it has to ingest a big chunk of context first.
I appreciate the advice and I try to keep up with local developments, but I'm too conditioned by big-dick corpo models, it's hard to quit digital crack and I've had a lot of time to build a habit. I've managed to get tired of straight cooming for the time being and started trying more esoteric stuff like playing "text adventures", which requires a lot of cognitive oomph on the model's behalf, and corpo models are obviously leaps and bounds ahead in capabilities at the moment. As long as corpos continue to be clueless enough to allow locusts like me to wrangle access in some roundabout way (technically neither OpenAI nor Claude is available in my country), I'll stick to that.
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