12 ms·

Nit but bandwidth isn’t related to memory capacity. Bandwidth is a function of cache/access pattern and is independent of total memory size.

• touisteur 5y ago
  The generous interpretation here would be that having huge amounts of ram that you can't access quickly enough wouldn't be that useful. I'm curious though, apart from huge NNs and raytracing scenes, what use cases call for so much RAM. I mean, apart from content lookup tables?
  • mirker 5y ago
    I believe the applications are mostly what you describe. You take a very general algorithm like a NN or raytracing and scale the model and/or data. For NN, it’s quite easy to use a huge amount of memory by making back-propagation chains huge, which is natural for deep models or a recurrent architecture. The model doesn’t have to be huge in a conventional sense; it just has to maintain state e.g., the recurrence. So, a big image model on video classification/segmentation (e.g., self-driving cars) is probably the ideal combination of memory consumption.