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31 changes: 15 additions & 16 deletions README.md
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Expand Up @@ -103,22 +103,6 @@ pip install torchao

Please see the [torchao compability table](https://github.com/pytorch/ao/issues/2919) for version requirements for dependencies.

## 🔗 Integrations

TorchAO is integrated into some of the leading open-source libraries including:

* Unsloth for QAT, blog post coming soon!
* HuggingFace transformers with a [builtin inference backend](https://huggingface.co/docs/transformers/main/quantization/torchao) and [low bit optimizers](https://github.com/huggingface/transformers/pull/31865)
* HuggingFace diffusers best practices with `torch.compile` and TorchAO in a standalone repo [diffusers-torchao](https://github.com/huggingface/diffusers/blob/main/docs/source/en/quantization/torchao.md)
* vLLM for LLM serving: [usage](https://docs.vllm.ai/en/latest/features/quantization/torchao.html), [detailed docs](https://docs.pytorch.org/ao/main/torchao_vllm_integration.html)
* Integration with [FBGEMM](https://github.com/pytorch/FBGEMM/tree/main/fbgemm_gpu/experimental/gen_ai) for SOTA kernels on server GPUs
* Integration with [ExecuTorch](https://github.com/pytorch/executorch/) for edge device deployment
* Axolotl for [QAT](https://docs.axolotl.ai/docs/qat.html) and [PTQ](https://docs.axolotl.ai/docs/quantize.html)
* TorchTitan for [float8 pre-training](https://github.com/pytorch/torchtitan/blob/main/docs/float8.md)
* HuggingFace PEFT for LoRA using TorchAO as their [quantization backend](https://huggingface.co/docs/peft/en/developer_guides/quantization#torchao-pytorch-architecture-optimization)
* TorchTune for our NF4 [QLoRA](https://docs.pytorch.org/torchtune/main/tutorials/qlora_finetune.html), [QAT](https://docs.pytorch.org/torchtune/main/recipes/qat_distributed.html), and [float8 quantized fine-tuning](https://github.com/pytorch/torchtune/pull/2546) recipes
* SGLang for LLM serving: [usage](https://docs.sglang.ai/advanced_features/quantization.html#online-quantization)

## 🔎 Inference

TorchAO delivers substantial performance gains with minimal code changes:
Expand Down Expand Up @@ -265,6 +249,21 @@ We've added support for authoring and releasing [custom ops](./torchao/csrc/) th
If you believe there's other CUDA kernels we should be taking a closer look at please leave a comment on [this issue](https://github.com/pytorch/ao/issues/697) or feel free to contribute directly to the repo.
-->

## 🔗 Integrations

TorchAO is integrated into some of the leading open-source libraries including:

* Unsloth for QAT, blog post coming soon!
* HuggingFace transformers with a [builtin inference backend](https://huggingface.co/docs/transformers/main/quantization/torchao) and [low bit optimizers](https://github.com/huggingface/transformers/pull/31865)
* HuggingFace diffusers best practices with `torch.compile` and TorchAO in a standalone repo [diffusers-torchao](https://github.com/huggingface/diffusers/blob/main/docs/source/en/quantization/torchao.md)
* vLLM for LLM serving: [usage](https://docs.vllm.ai/en/latest/features/quantization/torchao.html), [detailed docs](https://docs.pytorch.org/ao/main/torchao_vllm_integration.html)
* Integration with [FBGEMM](https://github.com/pytorch/FBGEMM/tree/main/fbgemm_gpu/experimental/gen_ai) for SOTA kernels on server GPUs
* Integration with [ExecuTorch](https://github.com/pytorch/executorch/) for edge device deployment
* Axolotl for [QAT](https://docs.axolotl.ai/docs/qat.html) and [PTQ](https://docs.axolotl.ai/docs/quantize.html)
* TorchTitan for [float8 pre-training](https://github.com/pytorch/torchtitan/blob/main/docs/float8.md)
* HuggingFace PEFT for LoRA using TorchAO as their [quantization backend](https://huggingface.co/docs/peft/en/developer_guides/quantization#torchao-pytorch-architecture-optimization)
* TorchTune for our NF4 [QLoRA](https://docs.pytorch.org/torchtune/main/tutorials/qlora_finetune.html), [QAT](https://docs.pytorch.org/torchtune/main/recipes/qat_distributed.html), and [float8 quantized fine-tuning](https://github.com/pytorch/torchtune/pull/2546) recipes
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@jainapurva jainapurva Oct 21, 2025

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nit: Just checking, if we want to show Torchtune here, or maybe something in legacy integrations?

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@jerryzh168 jerryzh168 Oct 22, 2025 •
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cc @andrewor14 maybe you can take a look after we merge this, I think it makes sense to move some of the integrations to details as well

* SGLang for LLM serving: [usage](https://docs.sglang.ai/advanced_features/quantization.html#online-quantization)

## 🎥 Videos
* [Keynote talk at GPU MODE IRL](https://youtu.be/FH5wiwOyPX4?si=VZK22hHz25GRzBG1&t=1009)
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