Raven Protocol just dropped Ravnest - a distributed deep learning training framework that turns consumer PCs into a training cluster. No data center needed.
The architecture lets you split model training across whatever hardware people already have sitting around. Think of it as federated learning meets compute sharing - you're essentially building a training mesh from idle GPUs.
This matters because training costs are brutal right now. If they can actually coordinate gradient syncing and handle the network latency between random consumer machines, this could be a real alternative to renting H100s for smaller teams.
Key technical question: How are they handling fault tolerance when nodes drop mid-training? Consumer hardware isn't exactly reliable infrastructure.
The architecture lets you split model training across whatever hardware people already have sitting around. Think of it as federated learning meets compute sharing - you're essentially building a training mesh from idle GPUs.
This matters because training costs are brutal right now. If they can actually coordinate gradient syncing and handle the network latency between random consumer machines, this could be a real alternative to renting H100s for smaller teams.
Key technical question: How are they handling fault tolerance when nodes drop mid-training? Consumer hardware isn't exactly reliable infrastructure.