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# F.A.Q.

Q: Pytorch supports quantization-aware training. Why should I use Brevitas?

A: Quantization in Pytorch is designed to target specific backends supported by PyTorch itself (currently FBGEMM and qnnpack).

Brevitas is designed as a platform to implement novel quantization algorithms to target a variety of hardware backends adhering to a loose set of assumptions (i.e. uniform affine quantization).

Q: How can I train X/Y and run it on hardware W/Z? I can’t find any documentation.

A: Brevitas is still sparsely documented. Until the situation improves, feel free to open an issue or ask on our gitter channel.

Q: Training with Brevitas is slow and/or I can’t fit the same batch size as with floating-point training. Why? What can I do?

A: Quantization-aware training involves a lot of element-wise operations, which carry low arithmetic intensity and contribute to a more involved computational graph during backpropragation. As such, it typically ends up being slower and more resource-intensive than standard floating-point training.

Brevitas in particular is biased towards greater flexibility, at the cost of some training-time effieciency. The general principle is that it’s trading off more complexity at training time for more efficiency at inference time.

To mitigate somewhat the slow-down, try enabling BREVITAS_JIT as reported in the Settings section.

Q: Inference with Brevitas is slow. I thought the point of quantization was to make my model faster at inference time. What I am doing wrong?

A: Brevitas is concerned with modelling a reduced precision data-path, it does not provide inference-time acceleration on its own. To achieve acceleration, you should export your Brevitas model to a downstream toolchain / backend. See the

Q: My (C/G/T)PU supports float16 / bfloat16 / bfloat19 training. Can I use it to train with Brevitas?

A: Datatypes outside of float32 at training time have not been tested. That includes training on TPU / Pytorch-XLA. Do the math in terms of which reduced-precision integers can reasonably fit in a reduced-precision floating-point format at training time, and use at your own risk.