A lot of this will come from co-optimizing hardware and low level machine code for this specific use case… something apple is coincidently very good at.

Apple has worked very hard to make unified memory a feasible approach, and the benefits of that are pretty clear in apple silicon- that efficiency not only results in power and therefore thermal gains, but also in a significantly faster full loop per process: or a faster time to token. This is why even their single core mobile chips in the budget line Neo out perform PC processors with several times more threads and RAM[1]. Turns out, unified memory lets you have a whole lot more control over things like RAM bussing and core use for specific workflows.

Speculatively, a unified memory approach could also allow you to more easily integrate things like ReRAM to solve the current memory swapping bottleneck.

Let’s say a friend of mine works hardware at apple and works on exactly this… on device processing is the future I’m betting on.

[1] https://youtu.be/x26A28DoT-w?t=605

I could definitely image Apple embedding a kind of LLM-optimized FPGA: slow to load (update) an LLM, but blazing fast at computing tokens.

Who needs memory when your model is set in silicon ?

I think a major factor is memory bandwidth. Apple has raised it steadily for each M series generation, and that hasn't plateaued.

Nvidia leads in bandwidth and specialized architecture, but local inference takes off when it's usably fast at much lower cost and power consumption.

The models themselves have far from plateaued. Maybe someone finds a way to get a really capable model down to, say, 12GB of ram. Then we'd be in business.

It could be on software side too. OpenAI has certainly not plateaued.