I know relatively little about the workings of LLMs, but I keep seeing projects like this that run massive MoE models using very modest amounts of RAM, perhaps excessively so. I wonder, is there a way to make the RAM usage tunable? I have a Macbook with 32 GB of RAM, and it'd be great if I could run the same model but take advantage of the additional RAM to make it run faster.
That's about the turning point for just using typical quants for me. Larger still and you can just do the full model. Smaller to this degree and you need all sorts of extra tricks to get anything.
I read a comment on here a few months back I wanna restate. Basically, there is a good chance that Apple is betting that the LLMs in the future will be so efficient that those that consumers will use everyday will be easily computed by the iPhone or even bigger ones on Macs. Honestly makes the most sense that we are heading that way in a few years latest.
The models themselves have far from plateaued. Maybe someone finds a way to get a really capable model down to, say, 12GB of ram. Then we'd be in business.
this is cool but like, are we just vibe coding NAND burners at this point? these decode times don't really tell the whole story, because prefill becomes the bottleneck.
half an hour to process 10k tokens on an M5 seems... not great
People will keep plugging away at this and figure out how to avoid wearing the hard drive, how to make it run faster, custom hardware buses etc.
Keep going! I personally can't wait for the day when a 1t param model runs off a $200 SSD instead of a $50k rack of Nvidia chips.
half an hour to process 10k tokens on an M5 seems... not great