You can do that with Onnx. You can graft the preprocessing layers to the actual model [1] and then serve that. Honestly, I already thought that ONNX (CPU at least) was already low level code and already very optimized.

@Author - if you see this is it possible to add comparisons (ie "vanilla" inference latencies vs timber)?

[1] https://gist.github.com/msteiner-google/5f03534b0df58d32abcc... <-- A gist I put together in the past that goes from PyTorch to ONNX and grafts the preprocessing layers to the model, so you can pass the raw input.

I'll check this out as soon as I am at my desk.