So if the Chinese version is so efficient, and is open source, then couldn’t openAI and anthropic run the same on their huge hardware and get enormous capacity out of it?
OpenAI could use less hardware to get similar performance if they used the Chinese version, but they already have enough hardware to run their model.
Theoretically the best move for them would be to train their own, larger model using the same technique (as to still fully utilize their hardware) but this is easier said than done.
Not necessarily… if I gave you my “faster car” for you to run on your private 7 lane highway, you can definitely squeeze every last bit of the speed the car gives, but no more.
DeepSeek works as intended on 1% of the hardware the others allegedly “require” (allegedly, remember this is all a super hype bubble)… if you run it on super powerful machines, it will perform nicer but only to a certain extend… it will not suddenly develop more/better qualities just because the hardware it runs on is better
Didn’t deepseek solve some of the data wall problems by creating good chain of thought data with an intermediate RL model. That approach should work with the tried and tested scaling laws just using much more compute.
So if the Chinese version is so efficient, and is open source, then couldn’t openAI and anthropic run the same on their huge hardware and get enormous capacity out of it?
OpenAI could use less hardware to get similar performance if they used the Chinese version, but they already have enough hardware to run their model.
Theoretically the best move for them would be to train their own, larger model using the same technique (as to still fully utilize their hardware) but this is easier said than done.
Just ask the ai to assimilate the model?
Not necessarily… if I gave you my “faster car” for you to run on your private 7 lane highway, you can definitely squeeze every last bit of the speed the car gives, but no more.
DeepSeek works as intended on 1% of the hardware the others allegedly “require” (allegedly, remember this is all a super hype bubble)… if you run it on super powerful machines, it will perform nicer but only to a certain extend… it will not suddenly develop more/better qualities just because the hardware it runs on is better
Didn’t deepseek solve some of the data wall problems by creating good chain of thought data with an intermediate RL model. That approach should work with the tried and tested scaling laws just using much more compute.
This makes sense, but it would still allow a hundred times more people to use the model without running into limits, no?
hence certain tech grifters going “oh shitt…”
Yes but have you considered that “china bad”?
It’s not multimodal so I’d have to imagine it wouldn’t be worth pursuing in that regard.
doesn’t deepseek work on that though with their janus models?