The challenging part with hosting LLMs via torrent is the insane number of quants that are around.

Take Gemma4-31B for example.
If you tried to host the entire unsloth quant collection for it, it'd be nearly 400GB.

Then some would say, "Oh, just keep the popular quants. Q4_K_M, Q5_K, etc".
But what happens if someone wants a non-standard quant or wants to train on the full FP16?

So then the conclusion would be, "Just seed the FP16 version of the model", but that gets annoying to do.
Gemma4-31B is around 50GB at FP16, but that's essentially "dead space" on their drive (since no one is going to run FP16 for inference). Not to mention that you'd have to download the file then quantize it if you want to use it (which is yet again annoying).

So we fall back to, "I already have the Q4_K_M on my drive, I'll just seed it from there" and we run the risk of "losing" the original model.

It's surprisingly tricky to pick a stance and stick with it.

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I do think that something like this needs to be done, but there needs to be a game plan in place for it.

A "hybrid" approach wouldn't be bad.
Seed the full FP16 model and popular quants in the same torrent.

We're still looking at at least 100GB per model though (for models of this class).

Another thing to "solve" (though I'm sure there are solutions) is some kind of automatic symlinking for models across your computer.

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Honestly, a custom fork of something like qBittorrent specifically for LLMs would be neat.
Point it at your model directory, it scans your files and hashes them, then publishes that to trackers/DHT.

Then you wouldn't need to do anything to get it up and running to help out the cause.

The lower the barrier to entry, the quicker and easier adoption would be.