[chenxi9649]

Imo it feels more like 1-2 years away. Smaller 7B, 34B and 70B models are becoming a lot better, with more context length. Faster inference methods are coming out day by day. Better ways to quantize/distill models. All of that on top of chip advancements we saw a couple days ago with M4, Qualcomm/Googles arm chips...

I can't imagine more than 2 years for GPT4 level LLM on edge devices.

The question is, will ppl want GPT4 on the edge when GPT 6 is one request away?

[RetroTechie]

Hehe, this caught my eye:

"According to Precedence Research of Canada, the AI chip market is poised for explosive growth, with projections indicating a rise from $30 billion this year to over $200 billion by 2032.

Another research firm DataHorizzon Research even projected the AI Chip market to be 1,114.3 billion by 2032."

Ok, few here will deny the AI (chip) market is exploding. And CEOs will have visions of datacenters packed with expensive AI chips, extracting value from consumers. But if I were to take a guess:

AI models will be optimized the heck out of. Their architecture is only at the beginning of a long road of innovation (and in particular: simplification - aka race to the bottom). With so many heavyweights getting into the game, competition will be fierce.

Bottom line: yes, AI chips will be everywhere. But they'll also be cheap(-ish). Not unlike current day cpu's, flash, RAM etc.

With that in place, the next revolution: local-running AI models in everybody's hands. Cheap, easy to use & tinker with like mobile/PC apps today.