AI companies aren't valued on the GPUs they own.

I think he meant NVIDIA is the winner in the end.

And GPUs anyone can buy.

And if the market teaches us one thing, it is that GPU prices will drop over time. The current absurd prices is attracting competitors, and the more competitors, the lower the price.

I would not be surprised if, in 5 years, there's a neat, low-power GPU in high end laptops, alternatively, a cheap, say 500 USD or so, GPU in a box, that you can use for your AI-center at home or in the office.

MoE models can be largely offloaded to system RAM - recent versions of llama.cpp automatically distribute parts of the model, with all the dense parts going into VRAM and just the routed experts residing in system RAM.

I'm currently running Qwen3 Coder Next, which is a 80B A3B model, at 15tps on a 8GB 7700S and 64GB RAM.

It's an idiot (no Thinking), so it has to go over its work several times to get it right, but it's the largest idiot I can feasibly run at the moment.

Also by accident I'm undercutting the only provider of this model on OpenRouter by 40%, doing $0.48/mTok.

Of course at my token rate it would take an entire day to even reach a million tokens, but it's the principle that matters.