Repo author here. That's correct. The embeddings for Llama-3-8B are around 8KB-10KB. For Llama-3-70B they're around 32KB. These are small enough to send around between devices on a local network. For a SETI@home style network, latency will kill you if you go over the internet. That's why we're starting with local networks.
Ah yes. At first, I thought that since it is all one-way forward-only communication, latency would only affect the time to the first token.
But I guess the final output needs to be sent back to the first node before it can continue. So if there are 50 nodes with a latency of 40ms each, each token would take 2s to process.
Yeah, unfortunately the autoregressive nature of these models slows it down significantly with added device<->device latency. However, you can still max out on throughput with pipeline parallelism, where you overlap execution. See: https://pytorch.org/docs/stable/pipeline.html
For generating synthetic data you could have a SETI@Home setup if you consider each home as a node that generates some amount of data.
I mean, such a setup can be built with Exo, I wouldn’t suggest including it as part of Exo.
Out of curiosity, would you ever support training or at least fine-tuning?
