>Today a little cell phone spends much of its time accessing hardware in huge rooms. The vast majority of this behavior are basic CRUD operations. The iPhone's

>Today a little cell phone spends much of its time accessing hardware in huge rooms. The vast majority of this behavior are basic CRUD operations. The iPhone's single thread speed is likely faster than the server it's calling. For example, the Apple A16 contains 16 billion transistors - which is significantly more than a Zen4 CCD. >Even if that weren't true and you look exclusively at on-prem and in-hand software, it took 25-30 years to shrink supercomputers to pocket size. True but technology moves at a faster pace now. Chips get exponentially faster, not linearly. Do I expect a cell phone in 2027 to be as fast as a server room in 2023? No. But I expect the forces I outlined above to make it so that local LLMs will be useful enough for people to keep on their phone or computer? Yes. That's my bet. >The real issue with models is training. If you want to train locally you need access to a huge dataset. The assumption is that we will be downloading and updating models similar to how apps are currently deployed.