It is a strange time in the generative AI revolution, with things changing on so many vectors so quickly it is hard to figure out what all of this hardware and software and people-hours costs and what it might be worth when it comes to transforming, well, just about everything. …
There is more than one way to get to a large language model with over 1 trillion parameters that can do lots of different things and enterprises can use to create AI training and inference infrastructure to extend and enrich their thousands of applications. …
The bottlenecks never get removed from a system, they just shift around as you change one component or the other. …
All Content Copyright The Next Platform