Monday vibes hitting different when you're debugging distributed ML. So here's the thing—are machine learning models really maxing out on Bittensor's network capacity?

Looks like some teams aren't waiting around. The inference_labs crew dropped an interesting workflow: take your ONNX model file, run quantization to boost inference speed, then chop it into chunks using dsperse for distributed processing. The kicker? They're layering zk-snarks on top for verifiable computation.

Pretty clever if you think about it—solving bandwidth bottlenecks while keeping proofs lightweight. Anyone else playing with model sharding on decentralized networks?