I wonder how long it'll be until A100's depreciate that amount.
I see articles from "Serve The Home" pop up now often and I've yet to see one discussing hardware that I've ever come across in... someone's home.
I wonder how long it'll be until A100's depreciate that amount.
I see articles from "Serve The Home" pop up now often and I've yet to see one discussing hardware that I've ever come across in... someone's home.
We do have some content for homes. Check out our TinyMiniMicro series as an example.
I have never (until reading this) thought of it in that context. Was that the original meaning for the name, or did it shift over time?
I always get confused by why folks ask why we have server content. The Wall Street Journal covers more than just one road in NYC.
It's probably possible to engineer a home-scale inferencing network like that, but I've had plenty of success just running GPT-Neo on off-the-shelf ARM chips. It's not worth the extra $800, exploding power bill and engineering effort to get diminishing returns.
I do also look at various benchmarks and comparisons over and over because I would like to build a home gpu server for some machine learning fun and I find it interesting that the TFLOPS/$ numbers always come out very much linear... A card that gives 2x performance costs 2x more.... if cards depreciate 50%, it's because new cards have been launched that have double performance for the same price... etc... Even simpler, the CUDA cores-per-dollar is pretty much a constant.
What isn't taken into account there is various optimizations new cards have that speed up training or inference, like better use of the tensor cores. At some point incompatibility with newer CUDA compute levels makes the older cards much less useable.
Faster, cheaper, crippled by AMDs poor software support.
They even have community fiber! https://www.chelanpud.org/my-pud-services/residential-servic...
You're just not visiting the right homes ...