diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index 5b283e9a6a..f88af5972e 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -16,7 +16,7 @@ repos:
       - id: no-commit-to-branch
         args: ['--branch', 'main']
   - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: v0.7.4
+    rev: v0.8.1
     hooks:
       - id: ruff
         args: [ --fix ]
diff --git a/docs/source/using_doctr/using_model_export.rst b/docs/source/using_doctr/using_model_export.rst
index db632701ba..073172efbc 100644
--- a/docs/source/using_doctr/using_model_export.rst
+++ b/docs/source/using_doctr/using_model_export.rst
@@ -31,7 +31,11 @@ Advantages:
         .. code:: python3
 
             import torch
-            predictor = ocr_predictor(reco_arch="crnn_mobilenet_v3_small", det_arch="linknet_resnet34", pretrained=True).cuda().half()
+            predictor = ocr_predictor(
+                reco_arch="crnn_mobilenet_v3_small",
+                det_arch="linknet_resnet34",
+                pretrained=True
+            ).cuda().half()
             res = predictor(doc)
 
     .. tab:: TensorFlow
@@ -41,8 +45,63 @@ Advantages:
             import tensorflow as tf
             from tensorflow.keras import mixed_precision
             mixed_precision.set_global_policy('mixed_float16')
-            predictor = ocr_predictor(reco_arch="crnn_mobilenet_v3_small", det_arch="linknet_resnet34", pretrained=True)
-
+            predictor = ocr_predictor(
+                reco_arch="crnn_mobilenet_v3_small",
+                det_arch="linknet_resnet34",
+                pretrained=True
+            )
+
+
+Compiling your models (PyTorch only)
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+**NOTE:**
+
+- This feature is only available if you use PyTorch as backend.
+- The recognition architecture `master` is not supported for model compilation yet.
+- We provide only official support for the default (`inductor`) backend, but you can try other backends, configurations depending on your hardware and requirements as well.
+
+Compiling your PyTorch models with `torch.compile` optimizes the model by converting it to a graph representation and applying backends that can improve performance.
+This process can make inference faster and reduce memory overhead during execution.
+
+Further information can be found in the `PyTorch documentation <https://pytorch.org/tutorials/intermediate/torch_compile_tutorial.html>`_.
+
+.. code::
+
+    import torch
+    from doctr.models import (
+        ocr_predictor,
+        vitstr_small,
+        fast_base,
+        mobilenet_v3_small_crop_orientation,
+        mobilenet_v3_small_page_orientation,
+        crop_orientation_predictor,
+        page_orientation_predictor
+    )
+
+    # Compile the models
+    detection_model = torch.compile(
+        fast_base(pretrained=True).eval()
+    )
+    recognition_model = torch.compile(
+        vitstr_small(pretrained=True).eval()
+    )
+    crop_orientation_model = torch.compile(
+        mobilenet_v3_small_crop_orientation(pretrained=True).eval()
+    )
+    page_orientation_model = torch.compile(
+        mobilenet_v3_small_page_orientation(pretrained=True).eval()
+    )
+
+    predictor = models.ocr_predictor(
+        detection_model, recognition_model, assume_straight_pages=False
+    )
+    # NOTE: Only required for non-straight pages (`assume_straight_pages=False`) and non-disabled orientation classification
+    # Set the orientation predictors
+    predictor.crop_orientation_predictor = crop_orientation_predictor(crop_orientation_model)
+    predictor.page_orientation_predictor = page_orientation_predictor(page_orientation_model)
+
+    compiled_out = predictor(doc)
 
 Export to ONNX
 ^^^^^^^^^^^^^^
@@ -64,7 +123,11 @@ It defines a common format for representing models, including the network struct
             input_shape = (3, 32, 128)
             model = vitstr_small(pretrained=True, exportable=True)
             dummy_input = torch.rand((batch_size, input_shape), dtype=torch.float32)
-            model_path = export_model_to_onnx(model, model_name="vitstr.onnx, dummy_input=dummy_input)
+            model_path = export_model_to_onnx(
+                model,
+                model_name="vitstr.onnx",
+                dummy_input=dummy_input
+            )
 
     .. tab:: TensorFlow
 
@@ -78,7 +141,11 @@ It defines a common format for representing models, including the network struct
             input_shape = (32, 128, 3)
             model = vitstr_small(pretrained=True, exportable=True)
             dummy_input = [tf.TensorSpec([batch_size, input_shape], tf.float32, name="input")]
-            model_path, output = export_model_to_onnx(model, model_name="vitstr.onnx", dummy_input=dummy_input)
+            model_path, output = export_model_to_onnx(
+                model,
+                model_name="vitstr.onnx",
+                dummy_input=dummy_input
+            )
 
 
 Using your ONNX exported model
diff --git a/docs/source/using_doctr/using_models.rst b/docs/source/using_doctr/using_models.rst
index 40a820cee5..e42dea0881 100644
--- a/docs/source/using_doctr/using_models.rst
+++ b/docs/source/using_doctr/using_models.rst
@@ -298,7 +298,7 @@ For instance, this snippet instantiates an end-to-end ocr_predictor working with
 
 .. code:: python3
 
-    from doctr.model import ocr_predictor
+    from doctr.models import ocr_predictor
     model = ocr_predictor('linknet_resnet18', pretrained=True, assume_straight_pages=False, preserve_aspect_ratio=True)
 
 
@@ -309,7 +309,7 @@ Additionally, you can change the batch size of the underlying detection and reco
 
 .. code:: python3
 
-    from doctr.model import ocr_predictor
+    from doctr.models import ocr_predictor
     model = ocr_predictor(pretrained=True, det_bs=4, reco_bs=1024)
 
 To modify the output structure you can pass the following arguments to the predictor which will be handled by the underlying `DocumentBuilder`:
@@ -322,7 +322,7 @@ For example to disable the automatic grouping of lines into blocks:
 
 .. code:: python3
 
-    from doctr.model import ocr_predictor
+    from doctr.models import ocr_predictor
     model = ocr_predictor(pretrained=True, resolve_blocks=False)
 
 
@@ -477,7 +477,7 @@ This will only have an effect with `assume_straight_pages=False` and/or `straigh
 
 .. code:: python3
 
-    from doctr.model import ocr_predictor
+    from doctr.models import ocr_predictor
     model = ocr_predictor(pretrained=True, assume_straight_pages=False, disable_page_orientation=True)
 
 
@@ -489,7 +489,7 @@ This will only have an effect with `assume_straight_pages=False` and/or `straigh
 
 .. code:: python3
 
-    from doctr.model import ocr_predictor
+    from doctr.models import ocr_predictor
     model = ocr_predictor(pretrained=True, assume_straight_pages=False, disable_crop_orientation=True)
 
 
@@ -497,7 +497,7 @@ This will only have an effect with `assume_straight_pages=False` and/or `straigh
 
 .. code:: python3
 
-    from doctr.model import ocr_predictor
+    from doctr.models import ocr_predictor
 
     class CustomHook:
         def __call__(self, loc_preds):
diff --git a/doctr/models/classification/zoo.py b/doctr/models/classification/zoo.py
index 7cce3083ea..c147e7f104 100644
--- a/doctr/models/classification/zoo.py
+++ b/doctr/models/classification/zoo.py
@@ -5,7 +5,7 @@
 
 from typing import Any
 
-from doctr.file_utils import is_tf_available
+from doctr.file_utils import is_tf_available, is_torch_available
 
 from .. import classification
 from ..preprocessor import PreProcessor
@@ -48,7 +48,14 @@ def _orientation_predictor(
         # Load directly classifier from backbone
         _model = classification.__dict__[arch](pretrained=pretrained)
     else:
-        if not isinstance(arch, classification.MobileNetV3):
+        allowed_archs = [classification.MobileNetV3]
+        if is_torch_available():
+            # Adding the type for torch compiled models to the allowed architectures
+            from doctr.models.utils import _CompiledModule
+
+            allowed_archs.append(_CompiledModule)
+
+        if not isinstance(arch, tuple(allowed_archs)):
             raise ValueError(f"unknown architecture: {type(arch)}")
         _model = arch
 
diff --git a/doctr/models/detection/differentiable_binarization/pytorch.py b/doctr/models/detection/differentiable_binarization/pytorch.py
index ddd8cfa595..cad6a74aaf 100644
--- a/doctr/models/detection/differentiable_binarization/pytorch.py
+++ b/doctr/models/detection/differentiable_binarization/pytorch.py
@@ -205,11 +205,16 @@ def forward(
             out["out_map"] = prob_map
 
         if target is None or return_preds:
+            # Disable for torch.compile compatibility
+            @torch.compiler.disable  # type: ignore[attr-defined]
+            def _postprocess(prob_map: torch.Tensor) -> list[dict[str, Any]]:
+                return [
+                    dict(zip(self.class_names, preds))
+                    for preds in self.postprocessor(prob_map.detach().cpu().permute((0, 2, 3, 1)).numpy())
+                ]
+
             # Post-process boxes (keep only text predictions)
-            out["preds"] = [
-                dict(zip(self.class_names, preds))
-                for preds in self.postprocessor(prob_map.detach().cpu().permute((0, 2, 3, 1)).numpy())
-            ]
+            out["preds"] = _postprocess(prob_map)
 
         if target is not None:
             thresh_map = self.thresh_head(feat_concat)
diff --git a/doctr/models/detection/fast/pytorch.py b/doctr/models/detection/fast/pytorch.py
index 91218dba11..595004ba98 100644
--- a/doctr/models/detection/fast/pytorch.py
+++ b/doctr/models/detection/fast/pytorch.py
@@ -196,11 +196,16 @@ def forward(
             out["out_map"] = prob_map
 
         if target is None or return_preds:
+            # Disable for torch.compile compatibility
+            @torch.compiler.disable  # type: ignore[attr-defined]
+            def _postprocess(prob_map: torch.Tensor) -> list[dict[str, Any]]:
+                return [
+                    dict(zip(self.class_names, preds))
+                    for preds in self.postprocessor(prob_map.detach().cpu().permute((0, 2, 3, 1)).numpy())
+                ]
+
             # Post-process boxes (keep only text predictions)
-            out["preds"] = [
-                dict(zip(self.class_names, preds))
-                for preds in self.postprocessor(prob_map.detach().cpu().permute((0, 2, 3, 1)).numpy())
-            ]
+            out["preds"] = _postprocess(prob_map)
 
         if target is not None:
             loss = self.compute_loss(logits, target)
diff --git a/doctr/models/detection/linknet/pytorch.py b/doctr/models/detection/linknet/pytorch.py
index 88cb24204b..ff6860718e 100644
--- a/doctr/models/detection/linknet/pytorch.py
+++ b/doctr/models/detection/linknet/pytorch.py
@@ -183,11 +183,16 @@ def forward(
             out["out_map"] = prob_map
 
         if target is None or return_preds:
-            # Post-process boxes
-            out["preds"] = [
-                dict(zip(self.class_names, preds))
-                for preds in self.postprocessor(prob_map.detach().cpu().permute((0, 2, 3, 1)).numpy())
-            ]
+            # Disable for torch.compile compatibility
+            @torch.compiler.disable  # type: ignore[attr-defined]
+            def _postprocess(prob_map: torch.Tensor) -> list[dict[str, Any]]:
+                return [
+                    dict(zip(self.class_names, preds))
+                    for preds in self.postprocessor(prob_map.detach().cpu().permute((0, 2, 3, 1)).numpy())
+                ]
+
+            # Post-process boxes (keep only text predictions)
+            out["preds"] = _postprocess(prob_map)
 
         if target is not None:
             loss = self.compute_loss(logits, target)
diff --git a/doctr/models/detection/zoo.py b/doctr/models/detection/zoo.py
index da33b846ff..6d87b6b1a9 100644
--- a/doctr/models/detection/zoo.py
+++ b/doctr/models/detection/zoo.py
@@ -56,7 +56,14 @@ def _predictor(arch: Any, pretrained: bool, assume_straight_pages: bool = True,
         if isinstance(_model, detection.FAST):
             _model = reparameterize(_model)
     else:
-        if not isinstance(arch, (detection.DBNet, detection.LinkNet, detection.FAST)):
+        allowed_archs = [detection.DBNet, detection.LinkNet, detection.FAST]
+        if is_torch_available():
+            # Adding the type for torch compiled models to the allowed architectures
+            from doctr.models.utils import _CompiledModule
+
+            allowed_archs.append(_CompiledModule)
+
+        if not isinstance(arch, tuple(allowed_archs)):
             raise ValueError(f"unknown architecture: {type(arch)}")
 
         _model = arch
diff --git a/doctr/models/recognition/crnn/pytorch.py b/doctr/models/recognition/crnn/pytorch.py
index 1c0641bba2..bc77c98a24 100644
--- a/doctr/models/recognition/crnn/pytorch.py
+++ b/doctr/models/recognition/crnn/pytorch.py
@@ -213,8 +213,13 @@ def forward(
             out["out_map"] = logits
 
         if target is None or return_preds:
+            # Disable for torch.compile compatibility
+            @torch.compiler.disable  # type: ignore[attr-defined]
+            def _postprocess(logits: torch.Tensor) -> list[tuple[str, float]]:
+                return self.postprocessor(logits)
+
             # Post-process boxes
-            out["preds"] = self.postprocessor(logits)
+            out["preds"] = _postprocess(logits)
 
         if target is not None:
             out["loss"] = self.compute_loss(logits, target)
diff --git a/doctr/models/recognition/master/pytorch.py b/doctr/models/recognition/master/pytorch.py
index 3de463d09b..01b1df0735 100644
--- a/doctr/models/recognition/master/pytorch.py
+++ b/doctr/models/recognition/master/pytorch.py
@@ -209,7 +209,13 @@ def forward(
             out["out_map"] = logits
 
         if return_preds:
-            out["preds"] = self.postprocessor(logits)
+            # Disable for torch.compile compatibility
+            @torch.compiler.disable  # type: ignore[attr-defined]
+            def _postprocess(logits: torch.Tensor) -> list[tuple[str, float]]:
+                return self.postprocessor(logits)
+
+            # Post-process boxes
+            out["preds"] = _postprocess(logits)
 
         return out
 
diff --git a/doctr/models/recognition/parseq/pytorch.py b/doctr/models/recognition/parseq/pytorch.py
index 414739436a..a2ee524ced 100644
--- a/doctr/models/recognition/parseq/pytorch.py
+++ b/doctr/models/recognition/parseq/pytorch.py
@@ -372,8 +372,13 @@ def forward(
             out["out_map"] = logits
 
         if target is None or return_preds:
+            # Disable for torch.compile compatibility
+            @torch.compiler.disable  # type: ignore[attr-defined]
+            def _postprocess(logits: torch.Tensor) -> list[tuple[str, float]]:
+                return self.postprocessor(logits)
+
             # Post-process boxes
-            out["preds"] = self.postprocessor(logits)
+            out["preds"] = _postprocess(logits)
 
         if target is not None:
             out["loss"] = loss
diff --git a/doctr/models/recognition/sar/pytorch.py b/doctr/models/recognition/sar/pytorch.py
index caf1900575..fbbae121d4 100644
--- a/doctr/models/recognition/sar/pytorch.py
+++ b/doctr/models/recognition/sar/pytorch.py
@@ -262,8 +262,13 @@ def forward(
             out["out_map"] = decoded_features
 
         if target is None or return_preds:
+            # Disable for torch.compile compatibility
+            @torch.compiler.disable  # type: ignore[attr-defined]
+            def _postprocess(decoded_features: torch.Tensor) -> list[tuple[str, float]]:
+                return self.postprocessor(decoded_features)
+
             # Post-process boxes
-            out["preds"] = self.postprocessor(decoded_features)
+            out["preds"] = _postprocess(decoded_features)
 
         if target is not None:
             out["loss"] = self.compute_loss(decoded_features, gt, seq_len)
diff --git a/doctr/models/recognition/utils.py b/doctr/models/recognition/utils.py
index f6b299ade8..daa3c6d2d0 100644
--- a/doctr/models/recognition/utils.py
+++ b/doctr/models/recognition/utils.py
@@ -22,7 +22,7 @@ def merge_strings(a: str, b: str, dil_factor: float) -> str:
         A merged character sequence.
 
     Example::
-        >>> from doctr.model.recognition.utils import merge_sequences
+        >>> from doctr.models.recognition.utils import merge_sequences
         >>> merge_sequences('abcd', 'cdefgh', 1.4)
         'abcdefgh'
         >>> merge_sequences('abcdi', 'cdefgh', 1.4)
@@ -70,7 +70,7 @@ def merge_multi_strings(seq_list: list[str], dil_factor: float) -> str:
         A merged character sequence
 
     Example::
-        >>> from doctr.model.recognition.utils import merge_multi_sequences
+        >>> from doctr.models.recognition.utils import merge_multi_sequences
         >>> merge_multi_sequences(['abc', 'bcdef', 'difghi', 'aijkl'], 1.4)
         'abcdefghijkl'
     """
diff --git a/doctr/models/recognition/vitstr/pytorch.py b/doctr/models/recognition/vitstr/pytorch.py
index db763d53c7..7bd57793cc 100644
--- a/doctr/models/recognition/vitstr/pytorch.py
+++ b/doctr/models/recognition/vitstr/pytorch.py
@@ -107,8 +107,13 @@ def forward(
             out["out_map"] = decoded_features
 
         if target is None or return_preds:
+            # Disable for torch.compile compatibility
+            @torch.compiler.disable  # type: ignore[attr-defined]
+            def _postprocess(decoded_features: torch.Tensor) -> list[tuple[str, float]]:
+                return self.postprocessor(decoded_features)
+
             # Post-process boxes
-            out["preds"] = self.postprocessor(decoded_features)
+            out["preds"] = _postprocess(decoded_features)
 
         if target is not None:
             out["loss"] = self.compute_loss(decoded_features, gt, seq_len)
diff --git a/doctr/models/recognition/zoo.py b/doctr/models/recognition/zoo.py
index f60431441c..56327f8a65 100644
--- a/doctr/models/recognition/zoo.py
+++ b/doctr/models/recognition/zoo.py
@@ -5,7 +5,7 @@
 
 from typing import Any
 
-from doctr.file_utils import is_tf_available
+from doctr.file_utils import is_tf_available, is_torch_available
 from doctr.models.preprocessor import PreProcessor
 
 from .. import recognition
@@ -35,9 +35,14 @@ def _predictor(arch: Any, pretrained: bool, **kwargs: Any) -> RecognitionPredict
             pretrained=pretrained, pretrained_backbone=kwargs.get("pretrained_backbone", True)
         )
     else:
-        if not isinstance(
-            arch, (recognition.CRNN, recognition.SAR, recognition.MASTER, recognition.ViTSTR, recognition.PARSeq)
-        ):
+        allowed_archs = [recognition.CRNN, recognition.SAR, recognition.MASTER, recognition.ViTSTR, recognition.PARSeq]
+        if is_torch_available():
+            # Adding the type for torch compiled models to the allowed architectures
+            from doctr.models.utils import _CompiledModule
+
+            allowed_archs.append(_CompiledModule)
+
+        if not isinstance(arch, tuple(allowed_archs)):
             raise ValueError(f"unknown architecture: {type(arch)}")
         _model = arch
 
diff --git a/doctr/models/utils/pytorch.py b/doctr/models/utils/pytorch.py
index c9ea3a43ca..0582a4de2b 100644
--- a/doctr/models/utils/pytorch.py
+++ b/doctr/models/utils/pytorch.py
@@ -18,8 +18,12 @@
     "export_model_to_onnx",
     "_copy_tensor",
     "_bf16_to_float32",
+    "_CompiledModule",
 ]
 
+# torch compiled model type
+_CompiledModule = torch._dynamo.eval_frame.OptimizedModule
+
 
 def _copy_tensor(x: torch.Tensor) -> torch.Tensor:
     return x.clone().detach()
diff --git a/tests/pytorch/test_models_classification_pt.py b/tests/pytorch/test_models_classification_pt.py
index b3d25af173..5441181a1c 100644
--- a/tests/pytorch/test_models_classification_pt.py
+++ b/tests/pytorch/test_models_classification_pt.py
@@ -9,7 +9,7 @@
 
 from doctr.models import classification
 from doctr.models.classification.predictor import OrientationPredictor
-from doctr.models.utils import export_model_to_onnx
+from doctr.models.utils import _CompiledModule, export_model_to_onnx
 
 
 def _test_classification(model, input_shape, output_size, batch_size=2):
@@ -152,6 +152,19 @@ def test_crop_orientation_model(mock_text_box):
     with pytest.raises(ValueError):
         _ = classification.crop_orientation_predictor(classification.textnet_tiny(pretrained=True))
 
+    # Test torch compilation
+    compiled_model = torch.compile(classification.mobilenet_v3_small_crop_orientation(pretrained=True))
+    compiled_classifier = classification.crop_orientation_predictor(compiled_model)
+
+    assert isinstance(compiled_model, _CompiledModule)
+    assert isinstance(compiled_classifier, OrientationPredictor)
+    assert compiled_classifier([text_box_0, text_box_270, text_box_180, text_box_90])[0] == [0, 1, 2, 3]
+    assert compiled_classifier([text_box_0, text_box_270, text_box_180, text_box_90])[1] == [0, -90, 180, 90]
+    assert all(
+        isinstance(pred, float)
+        for pred in compiled_classifier([text_box_0, text_box_270, text_box_180, text_box_90])[2]
+    )
+
 
 def test_page_orientation_model(mock_payslip):
     text_box_0 = cv2.imread(mock_payslip)
@@ -184,6 +197,19 @@ def test_page_orientation_model(mock_payslip):
     with pytest.raises(ValueError):
         _ = classification.page_orientation_predictor(classification.textnet_tiny(pretrained=True))
 
+    # Test torch compilation
+    compiled_model = torch.compile(classification.mobilenet_v3_small_page_orientation(pretrained=True))
+    compiled_classifier = classification.page_orientation_predictor(compiled_model)
+
+    assert isinstance(compiled_model, _CompiledModule)
+    assert isinstance(compiled_classifier, OrientationPredictor)
+    assert compiled_classifier([text_box_0, text_box_270, text_box_180, text_box_90])[0] == [0, 1, 2, 3]
+    assert compiled_classifier([text_box_0, text_box_270, text_box_180, text_box_90])[1] == [0, -90, 180, 90]
+    assert all(
+        isinstance(pred, float)
+        for pred in compiled_classifier([text_box_0, text_box_270, text_box_180, text_box_90])[2]
+    )
+
 
 @pytest.mark.parametrize(
     "arch_name, input_shape, output_size",
diff --git a/tests/pytorch/test_models_detection_pt.py b/tests/pytorch/test_models_detection_pt.py
index 247cdb2880..a8598eb2fa 100644
--- a/tests/pytorch/test_models_detection_pt.py
+++ b/tests/pytorch/test_models_detection_pt.py
@@ -8,11 +8,12 @@
 import torch
 
 from doctr.file_utils import CLASS_NAME
+from doctr.io import DocumentFile
 from doctr.models import detection
 from doctr.models.detection._utils import dilate, erode
 from doctr.models.detection.fast.pytorch import reparameterize
 from doctr.models.detection.predictor import DetectionPredictor
-from doctr.models.utils import export_model_to_onnx
+from doctr.models.utils import _CompiledModule, export_model_to_onnx
 
 
 @pytest.mark.parametrize("train_mode", [True, False])
@@ -186,3 +187,39 @@ def test_models_onnx_export(arch_name, input_shape, output_size):
         assert np.allclose(pt_logits, ort_outs[0], atol=1e-4)
     except AssertionError:
         pytest.skip(f"Output of {arch_name}:\nMax element-wise difference: {np.max(np.abs(pt_logits - ort_outs[0]))}")
+
+
+@pytest.mark.parametrize(
+    "arch_name",
+    [
+        "db_resnet34",
+        "db_resnet50",
+        "db_mobilenet_v3_large",
+        "linknet_resnet18",
+        "linknet_resnet34",
+        "linknet_resnet50",
+        "fast_tiny",
+        "fast_small",
+        "fast_base",
+    ],
+)
+def test_torch_compiled_models(arch_name, mock_payslip):
+    doc = DocumentFile.from_images([mock_payslip])
+    predictor = detection.zoo.detection_predictor(arch_name, pretrained=True)
+    assert isinstance(predictor, DetectionPredictor)
+    out, seg_maps = predictor(doc, return_maps=True)
+
+    # Compile the model
+    compiled_model = torch.compile(detection.__dict__[arch_name](pretrained=True).eval())
+    assert isinstance(compiled_model, _CompiledModule)
+    compiled_predictor = detection.zoo.detection_predictor(compiled_model)
+    compiled_out, seg_maps = compiled_predictor(doc, return_maps=True)
+
+    # Compare
+    assert all(
+        np.allclose(out_boxes[CLASS_NAME], compiled_out_boxes[CLASS_NAME], atol=1e-4)
+        for out_boxes, compiled_out_boxes in zip(out, compiled_out)
+    )
+    assert all(
+        np.allclose(seg_map, compiled_seg_map, atol=1e-4) for seg_map, compiled_seg_map in zip(seg_maps, seg_maps)
+    )
diff --git a/tests/pytorch/test_models_recognition_pt.py b/tests/pytorch/test_models_recognition_pt.py
index e4df34060b..194089a37b 100644
--- a/tests/pytorch/test_models_recognition_pt.py
+++ b/tests/pytorch/test_models_recognition_pt.py
@@ -7,6 +7,7 @@
 import pytest
 import torch
 
+from doctr.io import DocumentFile
 from doctr.models import recognition
 from doctr.models.recognition.crnn.pytorch import CTCPostProcessor
 from doctr.models.recognition.master.pytorch import MASTERPostProcessor
@@ -14,7 +15,7 @@
 from doctr.models.recognition.predictor import RecognitionPredictor
 from doctr.models.recognition.sar.pytorch import SARPostProcessor
 from doctr.models.recognition.vitstr.pytorch import ViTSTRPostProcessor
-from doctr.models.utils import export_model_to_onnx
+from doctr.models.utils import _CompiledModule, export_model_to_onnx
 
 system_available_memory = int(psutil.virtual_memory().available / 1024**3)
 
@@ -154,3 +155,33 @@ def test_models_onnx_export(arch_name, input_shape):
         assert np.allclose(pt_logits, ort_outs[0], atol=1e-4)
     except AssertionError:
         pytest.skip(f"Output of {arch_name}:\nMax element-wise difference: {np.max(np.abs(pt_logits - ort_outs[0]))}")
+
+
+@pytest.mark.parametrize(
+    "arch_name",
+    [
+        "crnn_vgg16_bn",
+        "crnn_mobilenet_v3_small",
+        "crnn_mobilenet_v3_large",
+        "sar_resnet31",
+        # "master",  NOTE: MASTER model isn't 100% safe compilable yet (pytorch v2.5.1) - sometimes it fails to compile.
+        "vitstr_small",
+        "vitstr_base",
+        "parseq",
+    ],
+)
+def test_torch_compiled_models(arch_name, mock_text_box):
+    doc = DocumentFile.from_images([mock_text_box])
+    predictor = recognition.zoo.recognition_predictor(arch_name, pretrained=True)
+    assert isinstance(predictor, RecognitionPredictor)
+    out = predictor(doc)
+
+    # Compile the model
+    compiled_model = torch.compile(recognition.__dict__[arch_name](pretrained=True).eval())
+    assert isinstance(compiled_model, _CompiledModule)
+    compiled_predictor = recognition.zoo.recognition_predictor(compiled_model)
+    compiled_out = compiled_predictor(doc)
+
+    # Compare
+    assert out[0][0] == compiled_out[0][0]
+    assert np.allclose(out[0][1], compiled_out[0][1], atol=1e-4)
diff --git a/tests/pytorch/test_models_zoo_pt.py b/tests/pytorch/test_models_zoo_pt.py
index 3ea22ca9b6..868842ce82 100644
--- a/tests/pytorch/test_models_zoo_pt.py
+++ b/tests/pytorch/test_models_zoo_pt.py
@@ -1,5 +1,6 @@
 import numpy as np
 import pytest
+import torch
 from torch import nn
 
 from doctr import models
@@ -412,3 +413,38 @@ def test_zoo_models(det_arch, reco_arch):
     # passing detection model as recognition model
     with pytest.raises(ValueError):
         models.kie_predictor(reco_arch=det_model, pretrained=True)
+
+
+@pytest.mark.parametrize(
+    "det_arch, reco_arch",
+    [
+        ["fast_base", "crnn_vgg16_bn"],
+    ],
+)
+def test_end_to_end_torch_compile(det_arch, reco_arch, mock_payslip):
+    doc = DocumentFile.from_images(mock_payslip)
+    predictor = models.ocr_predictor(det_arch, reco_arch, pretrained=True, assume_straight_pages=False)
+    out = predictor(doc)
+
+    assert isinstance(out, Document)
+
+    # Compile the models
+    detection_model = torch.compile(detection.__dict__[det_arch](pretrained=True).eval())
+    recognition_model = torch.compile(recognition.__dict__[reco_arch](pretrained=True).eval())
+    crop_orientation_model = torch.compile(mobilenet_v3_small_crop_orientation(pretrained=True).eval())
+    page_orientation_model = torch.compile(mobilenet_v3_small_page_orientation(pretrained=True).eval())
+
+    predictor = models.ocr_predictor(detection_model, recognition_model, assume_straight_pages=False)
+    # Set the orientation predictors
+    # NOTE: only required for non-straight pages and non-disabled orientation classification
+    predictor.crop_orientation_predictor = crop_orientation_predictor(crop_orientation_model)
+    predictor.page_orientation_predictor = page_orientation_predictor(page_orientation_model)
+    compiled_out = predictor(doc)
+
+    # Check that the number of word detections is the same
+    assert len(out.pages[0].blocks[0].lines[0].words) == len(compiled_out.pages[0].blocks[0].lines[0].words)
+    # Check that the words are the same
+    assert all(
+        word.value == compiled_out.pages[0].blocks[0].lines[0].words[i].value
+        for i, word in enumerate(out.pages[0].blocks[0].lines[0].words)
+    )