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This is inaccurate. If you run your own instance... and have 20 users. That's 20 users that aren't hitting the main instance. One copy of the content is transmitted from the primary instance to your instance... Those 20 users are then hitting your instance. So instead of the main instance serving 20 people it's serving to one copy of the content. That is a 20 fold savings in bandwidth, cpu, and ram. The only thing that isn't saved is disk capacity... since the origin server needs to serve all the content on demand.
Now the 1-2 user instances, yes there's not much savings there. But once you get to 5-10 it's already a better deal.
My wording was poor. I ment that currently there is no way to contribute to reducing stress on an instance. Making your own instance might help prevent the problem from getting worse, but it is not the same as adding more cpu power or ram to an instance. If a instance is maxing out on it's CPU power, currently there is no way to allow other people to help disperse the current load.
On a slightly tangential point, I am not sure how sustainable it is to increase the number of possible users by increasing the number of instances. It is already a frustrating process finding the right instance to join. So imagine when there is 1 instance for every 100 users. With 100k users that is 1000 different instances to sort through. I think there needs to be better ways to scale Lemmy, especially the amount processing power it requires. Lemmy.ml will only be able to scale so big on a single vps instance, or even physical server.
Why would you sort through instances? The communities you want to interact with are still on the big instances... Just let the federation do the talking rather than directly communicating to the instance.
I see what you mean with the other point though. In that case people need to step off the lemmy.ml instance and move somewhere else to lighten the current load.
Based on figures I've seen from other instances though it doesn't take all that much cpu/ram to handle a metric boatload of users. The issue seems to be postgres tuning(which could be storage latency/bandwidth) and storage space.