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Comment by mikewarot | Hacker News Reader

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mikewarot·

Yes, so you would have a vector about 8k values long to be transferred on each token generated.

You could do that easily with any modern network.

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That's exciting. So we could build a SETI@home style network of even the largest models.

I wonder if training could be done in this way too.

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

how does it compare to https://github.com/bigscience-workshop/petals ?

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?

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