To make the most of these architectures I think the key is essentially moving more of the knowledge/capabilities out of the "weights" and into the complimentary parts of the system in a way that's proportionate to the capabilities of the hardware.

In the past couple months there's been a kind of explosion in small-models that are occupying a niche in this kind of AI-transcoding space. What I'm hoping we're right on the cusp of achieving is a similar explosion in what I'd call tool-adaptation, where an LLM paired with some mostly-fixed suite of tools and problem cases can trade off some generality for a specialized (potentially hyper-specialized to the company or user) role.

The thing about more transcoding-related tasks is that they in general stay in sync with what the user of the device is actively doing, which will also typically be closely aligned with the capabilities of the user's hardware and what they want to do with their computer. So most people aren't being intentional about this kind of stuff right now, partly out of habit I think, because only just now does it make sense to think of personal computer as "stranded hardware" now that they can be steered/programmed somewhat autonomously.

I'm wondering if with the right approach to MoE on local devices (which local llms are heading towards) we could basically amortize the expensive hit from loading weights in and out of VRAM through some kind of extreme batch use case that users still find useful enough to be worth the latency. LoRa is already really useful for this but obviously sometimes you need more expertise/specialization than just a few layers' difference. Experimenting with this right now. It's the same basic principle as in the paper except less of a technical optimization and more workload optimization. Also it's literally the beginning of machine culture so that's kind of cool