It builds on top of Nx, Numerical Elixir, which allows for models to be compiled for cpu or gpu. Done this way, the model does not have to run by executing BEAM bytecode. Additionally, Liveview and Livebook enables the equivalent of Jupyter notebooks. All of this has been happening for the past couple years.

The advantage here is data being fed into the model can use frameworks like Broadway (streaming ingestion) or Membrane (realtime media streaming), or Nerves (BEAM at edge / IoT), or as a controller for unreliable agents.

Digging into the source of its dependencies a bit, it looks like it uses NIFs (inlined functions) to bind into your choice of a couple of different potential C backends, the big ones being Torch and Google's XLA compiler.

So performance shouldn't be an issue at all, when compared to similar "high-level language calling low-level language for heavy tasks" situations, like Tensorflow or PyTorch. It does, however, come at the cost of sacrificing the extreme stability BEAM applications typically enjoy: misbehavior by the backend can cause hangs or even crashes of the BEAM node they're running in.