NVIDIA RTX Series, if they provide more, any AI LLM is dependent on VRAM. Right now, the NVIDIA RTX Series 5090 comes with only 32 GB VRAM. But luckily, Apple has come up with their Mac Studios which have unified memory where we could use that unified memory as VRAM. If unified memory systems come into the picture, then NVIDIA might lose its value. Nowadays, people are buying Mac Minis which have unified memory to run local AI, local LLMs. Still, they are not as fast as NVIDIA, but there is a chance. It is a horse race; you don't know which horse will win next. It all depends on each hardware's capability, how many Tensor Cores it has, and at what frequency it is running. So I'm not sure how I can assess it; it all depends on how the architecture is and how fast your system is.
NVIDIA RTX Series, if they provide more, any AI LLM is dependent on VRAM. Right now, the NVIDIA RTX Series 5090 comes with only 32 GB VRAM. But luckily, Apple has come up with their Mac Studios which have unified memory where we could use that unified memory as VRAM. If unified memory systems come into the picture, then NVIDIA might lose its value. Nowadays, people are buying Mac Minis which have unified memory to run local AI, local LLMs. Still, they are not as fast as NVIDIA, but there is a chance. It is a horse race; you don't know which horse will win next. It all depends on each hardware's capability, how many Tensor Cores it has, and at what frequency it is running. So I'm not sure how I can assess it; it all depends on how the architecture is and how fast your system is.