| ▲ | _joel 9 hours ago |
| Yep, that and SETI@Home. I loved the eye candy, even if I didn't know what it fully meant. |
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| ▲ | gregsadetsky 9 hours ago | parent | next [-] |
| That and project RC5 from the same time period..! :-) https://www.distributed.net/RC5 https://en.wikipedia.org/wiki/RSA_Secret-Key_Challenge I wonder what kind of performance would I get on a M1 computer today... haha EDIT: people are still participating in rc5-72...?? https://stats.distributed.net/projects.php?project_id=8 |
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| ▲ | seydor 9 hours ago | parent | prev [-] |
| How come we don't have AI@Home |
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| ▲ | throwup238 9 hours ago | parent | next [-] | | The network bandwidth between nodes is a bigger limitation than compute. The newest Nvidia cards come with 400gbit busses now to communicate between them, even on a single motherboard. Compared to SETI or Folding @Home, this would work glacially slow for AI models. | | |
| ▲ | fourthark 8 hours ago | parent [-] | | Seems like training would be a better match, where you need tons of compute but don’t care about latency. | | |
| ▲ | ronsor 37 minutes ago | parent [-] | | No, the problem is that with training, you do care about latency, and you need a crap-ton of bandwidth too! Think of the all_gather; think of the gradients! Inference is actually easier to distribute. |
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| ▲ | 6 hours ago | parent | prev [-] | | [deleted] |
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