aimet

Why we included this project

A trained PyTorch or ONNX model that is too slow or heavy to run on a phone or laptop is a common bottleneck, and AIMET targets that problem directly. The toolkit applies post-training and fine-tuning techniques for quantization and compression, cutting memory footprint and compute load while keeping accuracy loss modest. It also offers pruning, mixed-precision, and blockwise quantization, so you can adjust bit-width per layer instead of accepting a single global setting. Workflows are hands-on: you can simulate the quantized model, iterate on calibration, then export a deployable ONNX file for ONNX Runtime or Qualcomm's QNN. That makes it a strong option for teams preparing existing models for edge deployment who want to manage the accuracy-versus-efficiency trade-off.