morganherlocker 5 days ago

There's currently talk of adding gigawatts of data center capacity to the grid just for use cases where python dominates development. While a lot of that will be compiled into optimized kernels on CPU or GPU, it only takes a little bit of 1000x slower code to add up to a significant chunk of processing time at training or inference time.

  • MobiusHorizons 4 days ago

    What percentage of the CPU cycles are actually spent running Python though? My impression is _very_ low in production LLM workloads. I think significantly less than 1%. There are almost certainly better places to spend the effort, and if it did matter, I think they would replace Python with something like C++ or Rust.

  • [removed] 5 days ago
    [deleted]
lucb1e 5 days ago

Might as well take the work that's already done though? I can't think of a logical reason why you'd want to run it at potentially half the speed (depending on the hot code specifics how much, if any, speedup you get of course)