If you're running a massive model for logic you're probably better off not putting it in the robot. And it'll be a long time before there's enough robots to make up a significant share of usage.

More basic movement control doesn't need loads of ram as far as I know.

The same logic for why self-driving cars can't be cloud based, applies for robots. Something cannot be in the middle of a delicate operation and then "oops!", no network, it just stops.

The larger the context window, the better with models. Having a few TB of RAM would be exceptionally helpful.

All this just made me realise something however. Having your robot dormant and charging, is a bit of a waste. You could have robots dormant, but its compute in use to act as a compute node. If the distribution of robots is similar world-wide, we'd need a fraction of the datacenters we have now.

Using such nodes for training purposes would be beyond advantageous. And the company which can slice up the work and having training done in batches would get the big bucks. And actually, with consumer facing products soon all laden with extra ram and gpu for local compute, that applies there too.

Imagine leasing out idle time on your desktop or even laptop for cash. There may be a market here, especially with the cost of new datacenters. Any company able to securely package compute without risking data safety is going to make a mint.

Anyone have any ideas?