Why didn't you take into account batching, input tokens, different costs of electricity, and the fact that a laptop can still hold a decent % of its resale value, and is useful for many other tasks than running an LLM?

▲ bigyabai an hour ago | parent [-]

> Why didn't you take into account [...] the fact that a laptop can still hold a decent % of its resale value, and is useful for many other tasks than running an LLM?

Because that wasn't what they claimed to research?

  >> for inference it's definitely not worth it.

It's entirely fine if you enjoy local LLMs on your computer, there are people doing horribly inefficient inference on smartphones now. But for pure inference tasks, it's pretty obvious why M5s and Mac Studios aren't replacing TPUs and GPUs.

	▲	joefourier an hour ago \| parent [-]
		Who is going to buy a $4299 M5 Max MBP with 64GB of RAM just to run Gemma 4 31b? Firstly you don't need 64GB for that model. Secondly if you want a machine that sits in the corner and does nothing but LLM inference, you don't buy a MacBook Pro, you buy some GPUs which are going to cost you a fraction of that (~$1k for ~64GB of VRAM is possible). The people buying Apple Silicon for inference general aim for the Mac Studios with enormous amounts of RAM (128-512GB), to run very large models. The idea is obviously to be running the LLM on your work laptop. As a developer I'd need a laptop with 24GB of RAM for work anyway, and 48GB, which is enough for a very good quant of Gemini, is just $400 extra.