Really cool experiment (the whole company).

Training pipelines are full of data preparation that are first written on CPU then moving to GPU and always thinking of what to keep on CPU and what to put on GPU, when is it worth to create a tensor, or should it be tiling instead. I guess your company is betting on solving problems like this (and async-await is needed for serving inference requests directly on the GPU for example).

My question is a little bit different: how do you want to handle the SIMD question: should a rust function be running on the warp as a machine with 32 long arrays as data types, or always ,,hope'' for autovectorization to work (especially with Rust's iter library helpers).