diff --git a/docs/source/en/_toctree.yml b/docs/source/en/_toctree.yml
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+++ b/docs/source/en/_toctree.yml
@@ -150,6 +150,8 @@
       title: Troubleshooting
     - local: tf_xla
       title: XLA Integration for TensorFlow Models
+    - local: perf_torch_compile
+      title: Optimize inference using `torch.compile()`
   title: Performance and scalability
 - sections:
     - local: contributing
diff --git a/docs/source/en/perf_torch_compile.md b/docs/source/en/perf_torch_compile.md
new file mode 100644
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+++ b/docs/source/en/perf_torch_compile.md
@@ -0,0 +1,354 @@
+<!--Copyright 2023 The HuggingFace Team. All rights reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+the License. You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+
+⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
+rendered properly in your Markdown viewer.
+
+-->
+
+# Optimize inference using torch.compile()
+
+This guide aims to provide a benchmark on the inference speed-ups introduced with [`torch.compile()`](https://pytorch.org/tutorials/intermediate/torch_compile_tutorial.html) for [computer vision models in 🤗 Transformers](https://huggingface.co/models?pipeline_tag=image-classification&library=transformers&sort=trending).
+
+## Benefits of torch.compile
+   
+Depending on the model and the GPU, `torch.compile()` yields up to 30% speed-up during inference. To use `torch.compile()`, simply install any version of `torch` above 2.0. 
+
+Compiling a model takes time, so it's useful if you are compiling the model only once instead of every time you infer.
+To compile any computer vision model of your choice, call `torch.compile()` on the model as shown below:
+
+```diff
+from transformers import AutoModelForImageClassification
+
+model = AutoModelForImageClassification.from_pretrained(MODEL_ID).to("cuda")
++ model = torch.compile(model)
+```
+
+`compile()` comes with multiple modes for compiling, which essentially differ in compilation time and inference overhead. `max-autotune` takes longer than `reduce-overhead` but results in faster inference. Default mode is fastest for compilation but is not as efficient compared to `reduce-overhead` for inference time. In this guide, we used the default mode. You can learn more about it [here](https://pytorch.org/get-started/pytorch-2.0/#user-experience).
+
+We benchmarked `torch.compile` with different computer vision models, tasks, types of hardware, and batch sizes on `torch` version 2.0.1.
+
+## Benchmarking code 
+
+Below you can find the benchmarking code for each task. We warm up the GPU before inference and take the mean time of 300 inferences, using the same image each time.
+
+### Image Classification with ViT
+
+```python 
+import torch
+from PIL import Image
+import requests
+import numpy as np
+from transformers import AutoImageProcessor, AutoModelForImageClassification
+
+url = 'http://images.cocodataset.org/val2017/000000039769.jpg'
+image = Image.open(requests.get(url, stream=True).raw)
+
+processor = AutoImageProcessor.from_pretrained("google/vit-base-patch16-224")
+model = AutoModelForImageClassification.from_pretrained("google/vit-base-patch16-224").to("cuda")
+model = torch.compile(model)
+
+processed_input = processor(image, return_tensors='pt').to(device="cuda")
+
+with torch.no_grad():
+    _ = model(**processed_input)
+
+```
+
+#### Object Detection with DETR
+
+```python 
+from transformers import AutoImageProcessor, AutoModelForObjectDetection
+
+processor = AutoImageProcessor.from_pretrained("facebook/detr-resnet-50")
+model = AutoModelForObjectDetection.from_pretrained("facebook/detr-resnet-50").to("cuda")
+model = torch.compile(model)
+
+texts = ["a photo of a cat", "a photo of a dog"]
+inputs = processor(text=texts, images=image, return_tensors="pt").to("cuda")
+
+with torch.no_grad():
+    _ = model(**inputs)
+```
+
+#### Image Segmentation with Segformer
+
+```python 
+from transformers import SegformerImageProcessor, SegformerForSemanticSegmentation
+
+processor = SegformerImageProcessor.from_pretrained("nvidia/segformer-b0-finetuned-ade-512-512")
+model = SegformerForSemanticSegmentation.from_pretrained("nvidia/segformer-b0-finetuned-ade-512-512").to("cuda")
+model = torch.compile(model)
+seg_inputs = processor(images=image, return_tensors="pt").to("cuda")
+
+with torch.no_grad():
+    _ = model(**seg_inputs)
+```
+
+Below you can find the list of the models we benchmarked.
+
+**Image Classification** 
+- [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224)
+- [microsoft/beit-base-patch16-224-pt22k-ft22k](https://huggingface.co/microsoft/beit-base-patch16-224-pt22k-ft22k)
+- [facebook/convnext-large-224](https://huggingface.co/facebook/convnext-large-224)
+- [microsoft/resnet-50](https://huggingface.co/)
+
+**Image Segmentation** 
+- [nvidia/segformer-b0-finetuned-ade-512-512](https://huggingface.co/nvidia/segformer-b0-finetuned-ade-512-512)
+- [facebook/mask2former-swin-tiny-coco-panoptic](https://huggingface.co/facebook/mask2former-swin-tiny-coco-panoptic)
+- [facebook/maskformer-swin-base-ade](https://huggingface.co/facebook/maskformer-swin-base-ade)
+- [google/deeplabv3_mobilenet_v2_1.0_513](https://huggingface.co/google/deeplabv3_mobilenet_v2_1.0_513)
+
+**Object Detection** 
+- [google/owlvit-base-patch32](https://huggingface.co/google/owlvit-base-patch32)
+- [facebook/detr-resnet-101](https://huggingface.co/facebook/detr-resnet-101)
+- [microsoft/conditional-detr-resnet-50](https://huggingface.co/microsoft/conditional-detr-resnet-50)
+
+Below you can find visualization of inference durations with and without `torch.compile()` and percentage improvements for each model in different hardware and batch sizes. 
+
+<div class="flex">
+  <div>
+    <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/torch_compile/a100_batch_comp.png" />
+  </div>
+  <div>
+    <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/torch_compile/v100_batch_comp.png" />
+  </div>
+   <div>
+    <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/torch_compile/t4_batch_comp.png" />
+  </div>
+</div>
+
+<div class="flex">
+  <div>
+    <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/torch_compile/A100_1_duration.png" />
+  </div>
+  <div>
+    <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/torch_compile/A100_1_percentage.png" />
+  </div>
+</div>
+
+
+![Duration Comparison on V100 with Batch Size of 1](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/torch_compile/v100_1_duration.png)
+
+![Percentage Improvement on T4 with Batch Size of 4](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/torch_compile/T4_4_percentage.png)
+
+Below you can find inference durations in milliseconds for each model with and without `compile()`. Note that OwlViT results in OOM in larger batch sizes.
+
+### A100 (batch size: 1)
+
+| **Task/Model** | **torch 2.0 - <br>no compile** | **torch 2.0 - <br>compile** |
+|:---:|:---:|:---:|
+| Image Classification/ViT | 9.325 | 7.584 | 
+| Image Segmentation/Segformer | 11.759 | 10.500 |
+| Object Detection/OwlViT | 24.978 | 18.420 |
+| Image Classification/BeiT | 11.282 | 8.448 | 
+| Object Detection/DETR | 34.619 | 19.040 |
+| Image Classification/ConvNeXT | 10.410 | 10.208 | 
+| Image Classification/ResNet | 6.531 | 4.124 |
+| Image Segmentation/Mask2former | 60.188 | 49.117 |
+| Image Segmentation/Maskformer | 75.764 | 59.487 | 
+| Image Segmentation/MobileNet | 8.583 | 3.974 |
+| Object Detection/Resnet-101 | 36.276 | 18.197 |
+| Object Detection/Conditional-DETR | 31.219 | 17.993 |
+
+
+### A100 (batch size: 4)
+
+| **Task/Model** | **torch 2.0 - <br>no compile** | **torch 2.0 - <br>compile** |
+|:---:|:---:|:---:|
+| Image Classification/ViT | 14.832 | 14.499 | 
+| Image Segmentation/Segformer | 18.838 | 16.476 |
+| Image Classification/BeiT | 13.205 | 13.048 | 
+| Object Detection/DETR | 48.657 | 32.418|
+| Image Classification/ConvNeXT | 22.940 | 21.631 | 
+| Image Classification/ResNet | 6.657 | 4.268 |
+| Image Segmentation/Mask2former | 74.277 | 61.781 |
+| Image Segmentation/Maskformer | 180.700 | 159.116 | 
+| Image Segmentation/MobileNet | 14.174 | 8.515 |
+| Object Detection/Resnet-101 | 68.101 | 44.998 |
+| Object Detection/Conditional-DETR | 56.470 | 35.552 |
+
+### A100 (batch size: 16)
+
+| **Task/Model** | **torch 2.0 - <br>no compile** | **torch 2.0 - <br>compile** |
+|:---:|:---:|:---:|
+| Image Classification/ViT | 40.944 | 40.010 | 
+| Image Segmentation/Segformer | 37.005 | 31.144 |
+| Image Classification/BeiT | 41.854 | 41.048 | 
+| Object Detection/DETR | 164.382 | 161.902 |
+| Image Classification/ConvNeXT | 82.258 | 75.561 | 
+| Image Classification/ResNet | 7.018 | 5.024 |
+| Image Segmentation/Mask2former | 178.945 | 154.814 |
+| Image Segmentation/Maskformer | 638.570 | 579.826 | 
+| Image Segmentation/MobileNet | 51.693 | 30.310 |
+| Object Detection/Resnet-101 | 232.887 | 155.021 |
+| Object Detection/Conditional-DETR | 180.491 | 124.032 |
+
+### V100 (batch size: 1)
+
+| **Task/Model** | **torch 2.0 - <br>no compile** | **torch 2.0 - <br>compile** |
+|:---:|:---:|:---:|
+| Image Classification/ViT | 10.495 | 6.00 | 
+| Image Segmentation/Segformer | 13.321 | 5.862 | 
+| Object Detection/OwlViT | 25.769 | 22.395 | 
+| Image Classification/BeiT | 11.347 | 7.234 | 
+| Object Detection/DETR | 33.951 | 19.388 |
+| Image Classification/ConvNeXT | 11.623 | 10.412 | 
+| Image Classification/ResNet | 6.484 | 3.820 |
+| Image Segmentation/Mask2former | 64.640 | 49.873 |
+| Image Segmentation/Maskformer | 95.532 | 72.207 | 
+| Image Segmentation/MobileNet | 9.217 | 4.753 |
+| Object Detection/Resnet-101 | 52.818 | 28.367 |
+| Object Detection/Conditional-DETR | 39.512 | 20.816 |
+
+### V100 (batch size: 4)
+
+| **Task/Model** | **torch 2.0 - <br>no compile** | **torch 2.0 - <br>compile** |
+|:---:|:---:|:---:|
+| Image Classification/ViT | 15.181 | 14.501 | 
+| Image Segmentation/Segformer | 16.787 | 16.188 |
+| Image Classification/BeiT | 15.171 | 14.753 | 
+| Object Detection/DETR | 88.529 | 64.195 |
+| Image Classification/ConvNeXT | 29.574 | 27.085 | 
+| Image Classification/ResNet | 6.109 | 4.731 |
+| Image Segmentation/Mask2former | 90.402 | 76.926 |
+| Image Segmentation/Maskformer | 234.261 | 205.456 | 
+| Image Segmentation/MobileNet | 24.623 | 14.816 |
+| Object Detection/Resnet-101 | 134.672 | 101.304 |
+| Object Detection/Conditional-DETR | 97.464 | 69.739 |
+
+### V100 (batch size: 16)
+
+| **Task/Model** | **torch 2.0 - <br>no compile** | **torch 2.0 - <br>compile** |
+|:---:|:---:|:---:|
+| Image Classification/ViT | 52.209 | 51.633 | 
+| Image Segmentation/Segformer | 61.013 | 55.499 |
+| Image Classification/BeiT | 53.938 | 53.581  |
+| Object Detection/DETR | OOM | OOM |
+| Image Classification/ConvNeXT | 109.682 | 100.771 | 
+| Image Classification/ResNet | 14.857 | 12.089 |
+| Image Segmentation/Mask2former | 249.605 | 222.801 |
+| Image Segmentation/Maskformer | 831.142 | 743.645 | 
+| Image Segmentation/MobileNet | 93.129 | 55.365 |
+| Object Detection/Resnet-101 | 482.425 | 361.843 |
+| Object Detection/Conditional-DETR | 344.661 | 255.298 |
+
+### T4 (batch size: 1)
+
+| **Task/Model** | **torch 2.0 - <br>no compile** | **torch 2.0 - <br>compile** |
+|:---:|:---:|:---:|
+| Image Classification/ViT | 16.520 | 15.786 | 
+| Image Segmentation/Segformer | 16.116 | 14.205 |
+| Object Detection/OwlViT | 53.634 | 51.105 |
+| Image Classification/BeiT | 16.464 | 15.710 | 
+| Object Detection/DETR | 73.100 | 53.99 |
+| Image Classification/ConvNeXT | 32.932 | 30.845 | 
+| Image Classification/ResNet | 6.031 | 4.321 |
+| Image Segmentation/Mask2former | 79.192 | 66.815 |
+| Image Segmentation/Maskformer | 200.026 | 188.268 | 
+| Image Segmentation/MobileNet | 18.908 | 11.997 |
+| Object Detection/Resnet-101 | 106.622 | 82.566 |
+| Object Detection/Conditional-DETR | 77.594 | 56.984 |
+
+### T4 (batch size: 4)
+
+| **Task/Model** | **torch 2.0 - <br>no compile** | **torch 2.0 - <br>compile** |
+|:---:|:---:|:---:|
+| Image Classification/ViT | 43.653 | 43.626 | 
+| Image Segmentation/Segformer | 45.327 | 42.445 |
+| Image Classification/BeiT | 52.007 | 51.354 | 
+| Object Detection/DETR | 277.850 | 268.003 |
+| Image Classification/ConvNeXT | 119.259 | 105.580 | 
+| Image Classification/ResNet | 13.039 | 11.388 |
+| Image Segmentation/Mask2former | 201.540 | 184.670 |
+| Image Segmentation/Maskformer | 764.052 | 711.280 | 
+| Image Segmentation/MobileNet | 74.289 | 48.677 |
+| Object Detection/Resnet-101 | 421.859 | 357.614 |
+| Object Detection/Conditional-DETR | 289.002 | 226.945 |
+
+### T4 (batch size: 16)
+
+| **Task/Model** | **torch 2.0 - <br>no compile** | **torch 2.0 - <br>compile** |
+|:---:|:---:|:---:|
+| Image Classification/ViT | 163.914 | 160.907 | 
+| Image Segmentation/Segformer | 192.412 | 163.620 |
+| Image Classification/BeiT | 188.978 | 187.976 | 
+| Object Detection/DETR | OOM | OOM |
+| Image Classification/ConvNeXT | 422.886 | 388.078 | 
+| Image Classification/ResNet | 44.114 | 37.604 |
+| Image Segmentation/Mask2former | 756.337 | 695.291 |
+| Image Segmentation/Maskformer | 2842.940 | 2656.88 | 
+| Image Segmentation/MobileNet | 299.003 | 201.942 |
+| Object Detection/Resnet-101 |  1619.505 | 1262.758 | 
+| Object Detection/Conditional-DETR | 1137.513 | 897.390|
+
+## PyTorch Nightly
+We also benchmarked on PyTorch nightly (2.1.0dev, find the wheel [here](https://download.pytorch.org/whl/nightly/cu118)) and observed improvement in latency both for uncompiled and compiled models. 
+
+### A100
+| **Task/Model** | **Batch Size** | **torch 2.0 - no compile** | **torch 2.0 -<br> compile** |
+|:---:|:---:|:---:|:---:|
+| Image Classification/BeiT | Unbatched | 12.462 | 6.954 | 
+| Image Classification/BeiT | 4 | 14.109 | 12.851 | 
+| Image Classification/BeiT | 16 | 42.179 | 42.147 | 
+| Object Detection/DETR | Unbatched | 30.484 | 15.221 |
+| Object Detection/DETR | 4 | 46.816 | 30.942 |
+| Object Detection/DETR | 16 | 163.749 | 163.706  |
+
+### T4
+| **Task/Model** | **Batch Size** | **torch 2.0 - <br>no compile** | **torch 2.0 - <br>compile** |
+|:---:|:---:|:---:|:---:|
+| Image Classification/BeiT | Unbatched | 14.408 | 14.052 | 
+| Image Classification/BeiT | 4 | 47.381 | 46.604 | 
+| Image Classification/BeiT | 16 | 42.179 | 42.147  | 
+| Object Detection/DETR | Unbatched | 68.382 | 53.481 |
+| Object Detection/DETR | 4 | 269.615 | 204.785 |
+| Object Detection/DETR | 16 | OOM | OOM   |
+
+### V100
+| **Task/Model** | **Batch Size** | **torch 2.0 - <br>no compile** | **torch 2.0 - <br>compile** |
+|:---:|:---:|:---:|:---:|
+| Image Classification/BeiT | Unbatched | 13.477 | 7.926 | 
+| Image Classification/BeiT | 4 | 15.103 | 14.378 | 
+| Image Classification/BeiT | 16 | 52.517 | 51.691  | 
+| Object Detection/DETR | Unbatched | 28.706 | 19.077 |
+| Object Detection/DETR | 4 | 88.402 | 62.949|
+| Object Detection/DETR | 16 | OOM | OOM  |
+
+
+## Reduce Overhead
+We benchmarked `reduce-overhead` compilation mode for A100 and T4 in Nightly.
+
+### A100
+| **Task/Model** | **Batch Size** | **torch 2.0 - <br>no compile** | **torch 2.0 - <br>compile** |
+|:---:|:---:|:---:|:---:|
+| Image Classification/ConvNeXT | Unbatched | 11.758 | 7.335 | 
+| Image Classification/ConvNeXT | 4 | 23.171 | 21.490 | 
+| Image Classification/ResNet | Unbatched | 7.435 | 3.801 | 
+| Image Classification/ResNet | 4 | 7.261 | 2.187 | 
+| Object Detection/Conditional-DETR | Unbatched | 32.823 | 11.627  | 
+| Object Detection/Conditional-DETR | 4 | 50.622 | 33.831  | 
+| Image Segmentation/MobileNet | Unbatched | 9.869 | 4.244 |
+| Image Segmentation/MobileNet | 4 | 14.385 | 7.946 |
+
+
+### T4
+| **Task/Model** | **Batch Size** | **torch 2.0 - <br>no compile** | **torch 2.0 - <br>compile** | 
+|:---:|:---:|:---:|:---:|
+| Image Classification/ConvNeXT | Unbatched | 32.137 | 31.84 | 
+| Image Classification/ConvNeXT | 4 | 120.944 | 110.209 | 
+| Image Classification/ResNet | Unbatched | 9.761 | 7.698 | 
+| Image Classification/ResNet | 4 | 15.215 | 13.871 | 
+| Object Detection/Conditional-DETR | Unbatched | 72.150 | 57.660  | 
+| Object Detection/Conditional-DETR | 4 | 301.494 | 247.543  | 
+| Image Segmentation/MobileNet | Unbatched | 22.266 | 19.339  |
+| Image Segmentation/MobileNet | 4 | 78.311 | 50.983 |
+
+