1. people aren't using (GPU) RAM [1] for just running LLMs (in the local model sense), they are using it for other things... games, system displays, other models (Text-to-Speech, Speech Recognition, etc.);
2. being able to run models locally allows them to be used when the internet is down, use them on private data (e.g. medical records, proprietary company data, etc.), including training on that data;
3. the big AI companies are already storing unused GPUs on shelves waiting for their roll-out in data centres, as they can't build the capacity fast enough.
With point (3) that goes against Sam Altman's argument about utilization. The big companies have the hardware, but aren't utilizing it themselves.
[1] And if the argument extends to RAM in general you have all sorts of applications, especially on developer hardware -- editing and compiling software; running local servers; running CAD and other applications; etc.