Chinese open-weight AI models offer high capability at significantly lower costs than U.S. frontier systems, but face monetization and compute challenges, experts said at a Rest of World event in San Francisco.
Training these models still requires massive data centers, and using any of them that at all compare to frontier models for inference requires large numbers of GPUs
It does for now, but the methods for modifying an LLM are getting increasingly sophisticated. From blending models to doing brain surgery on them to fine tune the weights, there’s a lot of new techniques being developed all the time
There’s also recently been a breakthrough that cut the ram requirement to 1/6, eventually someone is going to figure out how to train models orderes of magnitude more efficiently
Especially once the AI bubble pops and no one is willing to dump billions into constant iterations
Training these models still requires massive data centers, and using any of them that at all compare to frontier models for inference requires large numbers of GPUs
It does for now, but the methods for modifying an LLM are getting increasingly sophisticated. From blending models to doing brain surgery on them to fine tune the weights, there’s a lot of new techniques being developed all the time
There’s also recently been a breakthrough that cut the ram requirement to 1/6, eventually someone is going to figure out how to train models orderes of magnitude more efficiently
Especially once the AI bubble pops and no one is willing to dump billions into constant iterations