But why though? Cannot possibly be useful at such slow speeds, and costs a ton to perform that badly
The entire history of innovation is filled with people doing something just to see they can get it to work, even if badly, and then people continue to iterate on that until it works better, then works well, and then is so obvious people would never even question it. But it all starts with someone doing it to scratch an itch.
Neural networks, the foundation of our current AI revolution, used to fit well into the "neat, but practically useless" category.
Sure there are countless "but why though?" experiments that don't pan out, but that's just the cost of exploration. There can be no step-function innovation in a world where people only do things that make immediate practical sense.
What are you going to do with a computer? I've always hated this attitude. We do these things because they are interesting to us, for the fun of exploration, because we enjoy learning, because we want to iterate and improve, to make the world better, or any plethora of reasons that involve intellectual curiosity of some sort.
> Just imagine someone asking this question today.
to be fair, considering how young folks live whole life in a smartphone nowadays...
"We choose to go to the moon in this decade and do the other things, not because they are easy, but because they are hard".
These demos are maybe useless, but if open models keep progressing, there's going to be some break through that continues whittling down just how much needs to be kept in VRAM, and progressive degredation to regular system ram and to ssds.
Afterall, they're not writing anything to these, so saturing all bandwidth could bring models to the masses. all without any help from Zark Muckerberg.
I wanted to see whether Kimi runs at all on one machine with the full record published, and for long multi-table finance reasoning I wanted the strongest model I could keep on the machine.
I did some tests against Deep Seek v4 flash results on my reports and Kimi definitely has some advantages.
Doing something like that at 1 token per second may be useful.
because prefill currently reads each layer's experts once per 64-row pass (~9 TB of reads for a 1.4 TB model).
Fix is scheduling and it's the next thing being built; once prefill reads each expert once per layer, the one-token-out classifier pattern becomes the sweet spot rather than the worst case.
It is cool that they got it to work at all.
Such of a Claudism. Not criticizing, just noticing.