We present TorchAO, a PyTorch-native model optimization framework leveraging quantization and sparsity to provide an end-to-end, training-to-serving workflow for AI models. TorchAO supports a variety of popular model optimization techniques, including FP8 quantized training, quantization-aware training (QAT), post-training quantization (PTQ), and 2:4 sparsity, and leverages a novel tensor subclass abstraction to represent a variety of widely-used, backend agnostic low precision data types, including INT4, INT8, FP8, MXFP4, MXFP6, and MXFP8. TorchAO integrates closely with the broader ecosystem at each step of the model optimization pipeline, from pre-training (TorchTitan) to fine-tuning (TorchTune, Axolotl) to serving (HuggingFace, vLLM, SGLang, ExecuTorch), connecting an otherwise fragmented space in a single, unified workflow. TorchAO has enabled recent launches of the quantized Llama 3.2 1B/3B and LlamaGuard3-8B models and is open-source at https://github.com/pytorch/ao/.
@article{arxiv.2507.16099,
title = {TorchAO: PyTorch-Native Training-to-Serving Model Optimization},
author = {Andrew Or and Apurva Jain and Daniel Vega-Myhre and Jesse Cai and Charles David Hernandez and Zhenrui Zheng and Driss Guessous and Vasiliy Kuznetsov and Christian Puhrsch and Mark Saroufim and Supriya Rao and Thien Tran and Aleksandar Samardžić},
journal= {arXiv preprint arXiv:2507.16099},
year = {2025}
}