LLMs perform valuable work, and by a projection there is a payback period for an investment in LLM. For Western frontier model, I would probably use much more than $10 per day, and for a Chinese model, around $5 per day. Depending on the prices and quantity of mechanical intellectual labor you use, you'll probably discover that you're doing more than $1000 worth of AI compute each year. To me, it's pretty much a nobrainer that you'd put in little money to get lots of value back.

Back in the day when you could get the ASUS GB10 for like $2000-3000 price, it was relatively cheap considering the value. Hell, I should have bought two, probably. I'd be running 256 GB range models with decent enough compute and bandwidth today. I regret not doing it now, but I had just bought Strix Halo box earlier that year with 128 GB and I felt weird about buying yet another 128 GB box for more AI stuff.

The real stuff for multiuser remains very costly -- $100k and up is in the ballpark for serving an organization, though with dozens to few hundred users, the math fundamentally doesn't change very much. We can probably bring the insane compute requirement in prefill down in future models, and turn the sequential inference into more like block inference with approaches such as dflash. This way, the models better match the limitations of common hardware, and these consumer boxes many of us have today become more useful.