diff --git a/.azure/gpu-tests-pytorch.yml b/.azure/gpu-tests-pytorch.yml
index dd1a3d4abc..15ce2f6ace 100644
--- a/.azure/gpu-tests-pytorch.yml
+++ b/.azure/gpu-tests-pytorch.yml
@@ -55,6 +55,9 @@ jobs:
         "Lightning | latest":
           image: "pytorchlightning/pytorch_lightning:base-cuda-py3.11-torch2.3-cuda12.1.0"
           PACKAGE_NAME: "lightning"
+        "Lightning | future":
+          image: "pytorchlightning/pytorch_lightning:base-cuda-py3.11-torch2.4-cuda12.1.0"
+          PACKAGE_NAME: "lightning"
     pool: lit-rtx-3090
     variables:
       DEVICES: $( python -c 'print("$(Agent.Name)".split("_")[-1])' )
@@ -76,9 +79,12 @@ jobs:
           echo "##vso[task.setvariable variable=TORCH_URL]https://download.pytorch.org/whl/cu${cuda_ver}/torch_stable.html"
           scope=$(python -c 'n = "$(PACKAGE_NAME)" ; print(dict(pytorch="pytorch_lightning").get(n, n))')
           echo "##vso[task.setvariable variable=COVERAGE_SOURCE]$scope"
+          python_ver=$(python -c "import sys; print(f'{sys.version_info.major}{sys.version_info.minor}')")
+          echo "##vso[task.setvariable variable=PYTHON_VERSION_MM]$python_ver"
         displayName: "set env. vars"
       - bash: |
-          echo "##vso[task.setvariable variable=TORCH_URL]https://download.pytorch.org/whl/test/cu${CUDA_VERSION_MM}/torch_test.html"
+          echo "##vso[task.setvariable variable=TORCH_URL]https://download.pytorch.org/whl/test/cu${CUDA_VERSION_MM}"
+          echo "##vso[task.setvariable variable=TORCHVISION_URL]https://download.pytorch.org/whl/test/cu124/torchvision-0.19.0%2Bcu124-cp${PYTHON_VERSION_MM}-cp${PYTHON_VERSION_MM}-linux_x86_64.whl"
         condition: endsWith(variables['Agent.JobName'], 'future')
         displayName: "set env. vars 4 future"
 
@@ -107,7 +113,7 @@ jobs:
 
       - bash: |
           extra=$(python -c "print({'lightning': 'pytorch-'}.get('$(PACKAGE_NAME)', ''))")
-          pip install -e ".[${extra}dev]" pytest-timeout -U --find-links="${TORCH_URL}"
+          pip install -e ".[${extra}dev]" pytest-timeout -U --find-links="${TORCH_URL}" --find-links="${TORCHVISION_URL}"
         displayName: "Install package & dependencies"
 
       - bash: pip uninstall -y lightning
diff --git a/.github/checkgroup.yml b/.github/checkgroup.yml
index 3774a56e2f..9e0a537e70 100644
--- a/.github/checkgroup.yml
+++ b/.github/checkgroup.yml
@@ -142,11 +142,15 @@ subprojects:
       - "build-cuda (3.10, 2.2, 12.1.0)"
       - "build-cuda (3.11, 2.1, 12.1.0)"
       - "build-cuda (3.11, 2.2, 12.1.0)"
+      - "build-cuda (3.11, 2.3, 12.1.0)"
+      - "build-cuda (3.11, 2.4, 12.1.0)"
       #- "build-NGC"
       - "build-pl (3.10, 2.1, 12.1.0)"
       - "build-pl (3.10, 2.2, 12.1.0)"
       - "build-pl (3.11, 2.1, 12.1.0)"
       - "build-pl (3.11, 2.2, 12.1.0)"
+      - "build-pl (3.11, 2.3, 12.1.0)"
+      - "build-pl (3.11, 2.4, 12.1.0)"
 
   # SECTION: lightning_fabric
 
diff --git a/.github/workflows/ci-tests-pytorch.yml b/.github/workflows/ci-tests-pytorch.yml
index b75b6e73d9..6ff9fbf05b 100644
--- a/.github/workflows/ci-tests-pytorch.yml
+++ b/.github/workflows/ci-tests-pytorch.yml
@@ -53,6 +53,9 @@ jobs:
           - { os: "macOS-14", pkg-name: "lightning", python-version: "3.10", pytorch-version: "2.3" }
           - { os: "ubuntu-20.04", pkg-name: "lightning", python-version: "3.10", pytorch-version: "2.3" }
           - { os: "windows-2022", pkg-name: "lightning", python-version: "3.10", pytorch-version: "2.3" }
+          - { os: "macOS-14", pkg-name: "lightning", python-version: "3.11", pytorch-version: "2.4" }
+          - { os: "ubuntu-20.04", pkg-name: "lightning", python-version: "3.11", pytorch-version: "2.4" }
+          - { os: "windows-2022", pkg-name: "lightning", python-version: "3.11", pytorch-version: "2.4" }
           # only run PyTorch latest with Python latest, use PyTorch scope to limit dependency issues
           - { os: "macOS-12", pkg-name: "pytorch", python-version: "3.11", pytorch-version: "2.1" }
           - { os: "ubuntu-22.04", pkg-name: "pytorch", python-version: "3.11", pytorch-version: "2.1" }
@@ -82,7 +85,7 @@ jobs:
       PACKAGE_NAME: ${{ matrix.pkg-name }}
       TORCH_URL: "https://download.pytorch.org/whl/cpu/torch_stable.html"
       TORCH_URL_STABLE: "https://download.pytorch.org/whl/cpu/torch_stable.html"
-      TORCH_URL_TEST: "https://download.pytorch.org/whl/test/cpu/torch_test.html"
+      TORCH_URL_TEST: "https://download.pytorch.org/whl/test/cpu/torch"
       FREEZE_REQUIREMENTS: ${{ ! (github.ref == 'refs/heads/master' || startsWith(github.ref, 'refs/heads/release/')) }}
       PYPI_CACHE_DIR: "_pip-wheels"
       # TODO: Remove this - Enable running MPS tests on this platform
@@ -124,11 +127,13 @@ jobs:
       - name: Env. variables
         run: |
           # Switch PyTorch URL
-          python -c "print('TORCH_URL=' + str('${{env.TORCH_URL_TEST}}' if '${{ matrix.pytorch-version }}' == '2.3' else '${{env.TORCH_URL_STABLE}}'))" >> $GITHUB_ENV
+          python -c "print('TORCH_URL=' + str('${{env.TORCH_URL_TEST}}' if '${{ matrix.pytorch-version }}' == '2.4' else '${{env.TORCH_URL_STABLE}}'))" >> $GITHUB_ENV
           # Switch coverage scope
           python -c "print('COVERAGE_SCOPE=' + str('lightning' if '${{matrix.pkg-name}}' == 'lightning' else 'pytorch_lightning'))" >> $GITHUB_ENV
           # if you install mono-package set dependency only for this subpackage
           python -c "print('EXTRA_PREFIX=' + str('' if '${{matrix.pkg-name}}' != 'lightning' else 'pytorch-'))" >> $GITHUB_ENV
+          # Avoid issue on Windows with PyTorch 2.4: "RuntimeError: use_libuv was requested but PyTorch was build without libuv support"
+          python -c "print('USE_LIBUV=0' if '${{matrix.os}}' == 'windows-2022' and '${{matrix.pytorch-version}}' == '2.4' else '')" >> $GITHUB_ENV
 
       - name: Install package & dependencies
         timeout-minutes: 20
diff --git a/.github/workflows/docker-build.yml b/.github/workflows/docker-build.yml
index 0891205421..6fa9d0d64d 100644
--- a/.github/workflows/docker-build.yml
+++ b/.github/workflows/docker-build.yml
@@ -47,6 +47,8 @@ jobs:
           - { python_version: "3.10", pytorch_version: "2.2", cuda_version: "12.1.0" }
           - { python_version: "3.11", pytorch_version: "2.1", cuda_version: "12.1.0" }
           - { python_version: "3.11", pytorch_version: "2.2", cuda_version: "12.1.0" }
+          - { python_version: "3.11", pytorch_version: "2.3", cuda_version: "12.1.0" }
+          - { python_version: "3.11", pytorch_version: "2.4", cuda_version: "12.1.0" }
     steps:
       - uses: actions/checkout@v4
         with:
@@ -74,7 +76,7 @@ jobs:
           tags = [f"latest-py{py_ver}-torch{pt_ver}-cuda{cuda_ver}"]
           if ver:
             tags += [f"{ver}-py{py_ver}-torch{pt_ver}-cuda{cuda_ver}"]
-          if py_ver == '3.10' and pt_ver == '2.1' and cuda_ver == '12.1.0':
+          if py_ver == '3.11' and pt_ver == '2.3' and cuda_ver == '12.1.0':
             tags += ["latest"]
 
           tags = [f"{repo}:{tag}" for tag in tags]
@@ -108,6 +110,7 @@ jobs:
           - { python_version: "3.11", pytorch_version: "2.1", cuda_version: "12.1.0" }
           - { python_version: "3.11", pytorch_version: "2.2", cuda_version: "12.1.0" }
           - { python_version: "3.11", pytorch_version: "2.3", cuda_version: "12.1.0" }
+          - { python_version: "3.11", pytorch_version: "2.4", cuda_version: "12.1.0" }
           # - { python_version: "3.12", pytorch_version: "2.2", cuda_version: "12.1.0" }  # todo: pending on `onnxruntime`
     steps:
       - uses: actions/checkout@v4
diff --git a/dockers/base-cuda/Dockerfile b/dockers/base-cuda/Dockerfile
index b8c29d01b0..9153fd33eb 100644
--- a/dockers/base-cuda/Dockerfile
+++ b/dockers/base-cuda/Dockerfile
@@ -20,7 +20,7 @@ FROM nvidia/cuda:${CUDA_VERSION}-runtime-ubuntu${UBUNTU_VERSION}
 
 ARG PYTHON_VERSION=3.10
 ARG PYTORCH_VERSION=2.1
-ARG MAX_ALLOWED_NCCL=2.17.1
+ARG MAX_ALLOWED_NCCL=2.22.3
 
 SHELL ["/bin/bash", "-c"]
 # https://techoverflow.net/2019/05/18/how-to-fix-configuring-tzdata-interactive-input-when-building-docker-images/
@@ -92,7 +92,8 @@ RUN \
       -r requirements/pytorch/test.txt \
       -r requirements/pytorch/strategies.txt \
       --find-links="https://download.pytorch.org/whl/cu${CUDA_VERSION_MM//'.'/''}/torch_stable.html" \
-      --find-links="https://download.pytorch.org/whl/test/cu${CUDA_VERSION_MM//'.'/''}/torch_test.html"
+      --find-links="https://download.pytorch.org/whl/test/cu${CUDA_VERSION_MM//'.'/''}/torch" \
+      --find-links="https://download.pytorch.org/whl/test/cu${CUDA_VERSION_MM//'.'/''}/pytorch-triton"
 
 RUN \
     # Show what we have
diff --git a/docs/source-pytorch/versioning.rst b/docs/source-pytorch/versioning.rst
index ebae1f920a..d923b01c7e 100644
--- a/docs/source-pytorch/versioning.rst
+++ b/docs/source-pytorch/versioning.rst
@@ -79,6 +79,18 @@ The table below indicates the coverage of tested versions in our CI. Versions ou
      - ``torch``
      - ``torchmetrics``
      - Python
+   * - 2.4
+     - 2.4
+     - 2.4
+     - ≥2.1, ≤2.4
+     - ≥0.7.0
+     - ≥3.9, ≤3.12
+   * - 2.3
+     - 2.3
+     - 2.3
+     - ≥2.0, ≤2.3
+     - ≥0.7.0
+     - ≥3.8, ≤3.11
    * - 2.2
      - 2.2
      - 2.2
diff --git a/requirements/pytorch/base.txt b/requirements/pytorch/base.txt
index cd71466551..5aae9ae1cb 100644
--- a/requirements/pytorch/base.txt
+++ b/requirements/pytorch/base.txt
@@ -2,7 +2,7 @@
 #  in case you want to preserve/enforce restrictions on the latest compatible version, add "strict" as an in-line comment
 
 numpy >=1.21.0, <1.27.0
-torch >=2.1.0, <2.4.0
+torch >=2.1.0, <2.5.0
 tqdm >=4.57.0, <4.67.0
 PyYAML >=5.4, <6.1.0
 fsspec[http] >=2022.5.0, <2024.4.0
diff --git a/requirements/pytorch/examples.txt b/requirements/pytorch/examples.txt
index e4b1bc31e9..9a6ae7e47d 100644
--- a/requirements/pytorch/examples.txt
+++ b/requirements/pytorch/examples.txt
@@ -2,7 +2,7 @@
 #  in case you want to preserve/enforce restrictions on the latest compatible version, add "strict" as an in-line comment
 
 requests <2.32.0
-torchvision >=0.16.0, <0.19.0
+torchvision >=0.16.0, <0.20.0
 ipython[all] <8.15.0
 torchmetrics >=0.10.0, <1.3.0
 lightning-utilities >=0.8.0, <0.12.0
diff --git a/src/lightning/fabric/utilities/cloud_io.py b/src/lightning/fabric/utilities/cloud_io.py
index 49b02cd095..7ecc9eea50 100644
--- a/src/lightning/fabric/utilities/cloud_io.py
+++ b/src/lightning/fabric/utilities/cloud_io.py
@@ -33,6 +33,7 @@ log = logging.getLogger(__name__)
 def _load(
     path_or_url: Union[IO, _PATH],
     map_location: _MAP_LOCATION_TYPE = None,
+    weights_only: bool = False,
 ) -> Any:
     """Loads a checkpoint.
 
@@ -46,15 +47,21 @@ def _load(
         return torch.load(
             path_or_url,
             map_location=map_location,  # type: ignore[arg-type] # upstream annotation is not correct
+            weights_only=weights_only,
         )
     if str(path_or_url).startswith("http"):
         return torch.hub.load_state_dict_from_url(
             str(path_or_url),
             map_location=map_location,  # type: ignore[arg-type]
+            weights_only=weights_only,
         )
     fs = get_filesystem(path_or_url)
     with fs.open(path_or_url, "rb") as f:
-        return torch.load(f, map_location=map_location)  # type: ignore[arg-type]
+        return torch.load(
+            f,
+            map_location=map_location,  # type: ignore[arg-type]
+            weights_only=weights_only,
+        )
 
 
 def get_filesystem(path: _PATH, **kwargs: Any) -> AbstractFileSystem:
diff --git a/src/lightning/pytorch/plugins/precision/amp.py b/src/lightning/pytorch/plugins/precision/amp.py
index c0a309f070..f086de6974 100644
--- a/src/lightning/pytorch/plugins/precision/amp.py
+++ b/src/lightning/pytorch/plugins/precision/amp.py
@@ -19,6 +19,7 @@ from typing_extensions import override
 
 import lightning.pytorch as pl
 from lightning.fabric.plugins.precision.amp import _optimizer_handles_unscaling
+from lightning.fabric.utilities.imports import _TORCH_GREATER_EQUAL_2_4
 from lightning.fabric.utilities.types import Optimizable
 from lightning.pytorch.plugins.precision.precision import Precision
 from lightning.pytorch.utilities import GradClipAlgorithmType
@@ -39,7 +40,7 @@ class MixedPrecision(Precision):
         self,
         precision: Literal["16-mixed", "bf16-mixed"],
         device: str,
-        scaler: Optional[torch.cuda.amp.GradScaler] = None,
+        scaler: Optional["torch.cuda.amp.GradScaler"] = None,
     ) -> None:
         if precision not in ("16-mixed", "bf16-mixed"):
             raise ValueError(
@@ -49,7 +50,7 @@ class MixedPrecision(Precision):
 
         self.precision = precision
         if scaler is None and self.precision == "16-mixed":
-            scaler = torch.cuda.amp.GradScaler()
+            scaler = torch.amp.GradScaler(device=device) if _TORCH_GREATER_EQUAL_2_4 else torch.cuda.amp.GradScaler()
         if scaler is not None and self.precision == "bf16-mixed":
             raise MisconfigurationException(f"`precision='bf16-mixed'` does not use a scaler, found {scaler}.")
         self.device = device
diff --git a/src/lightning/pytorch/profilers/pytorch.py b/src/lightning/pytorch/profilers/pytorch.py
index 9bdffad8de..a26b3d321d 100644
--- a/src/lightning/pytorch/profilers/pytorch.py
+++ b/src/lightning/pytorch/profilers/pytorch.py
@@ -28,6 +28,7 @@ from torch.utils.hooks import RemovableHandle
 from typing_extensions import override
 
 from lightning.fabric.accelerators.cuda import is_cuda_available
+from lightning.fabric.utilities.imports import _TORCH_GREATER_EQUAL_2_4
 from lightning.pytorch.profilers.profiler import Profiler
 from lightning.pytorch.utilities.exceptions import MisconfigurationException
 from lightning.pytorch.utilities.rank_zero import WarningCache, rank_zero_warn
@@ -295,7 +296,7 @@ class PyTorchProfiler(Profiler):
         self._emit_nvtx = emit_nvtx
         self._export_to_chrome = export_to_chrome
         self._row_limit = row_limit
-        self._sort_by_key = sort_by_key or f"{'cuda' if profiler_kwargs.get('use_cuda', False) else 'cpu'}_time_total"
+        self._sort_by_key = sort_by_key or _default_sort_by_key(profiler_kwargs)
         self._record_module_names = record_module_names
         self._profiler_kwargs = profiler_kwargs
         self._table_kwargs = table_kwargs if table_kwargs is not None else {}
@@ -403,10 +404,16 @@ class PyTorchProfiler(Profiler):
         activities: List[ProfilerActivity] = []
         if not _KINETO_AVAILABLE:
             return activities
-        if self._profiler_kwargs.get("use_cpu", True):
+        if _TORCH_GREATER_EQUAL_2_4:
             activities.append(ProfilerActivity.CPU)
-        if self._profiler_kwargs.get("use_cuda", is_cuda_available()):
-            activities.append(ProfilerActivity.CUDA)
+            if is_cuda_available():
+                activities.append(ProfilerActivity.CUDA)
+        else:
+            # `use_cpu` and `use_cuda` are deprecated in PyTorch >= 2.4
+            if self._profiler_kwargs.get("use_cpu", True):
+                activities.append(ProfilerActivity.CPU)
+            if self._profiler_kwargs.get("use_cuda", is_cuda_available()):
+                activities.append(ProfilerActivity.CUDA)
         return activities
 
     @override
@@ -565,3 +572,13 @@ class PyTorchProfiler(Profiler):
         self._recording_map = {}
 
         super().teardown(stage=stage)
+
+
+def _default_sort_by_key(profiler_kwargs: dict) -> str:
+    activities = profiler_kwargs.get("activities", [])
+    is_cuda = (
+        profiler_kwargs.get("use_cuda", False)  # `use_cuda` is deprecated in PyTorch >= 2.4
+        or (activities and ProfilerActivity.CUDA in activities)
+        or (not activities and is_cuda_available())
+    )
+    return f"{'cuda' if is_cuda else 'cpu'}_time_total"
diff --git a/src/lightning/pytorch/strategies/launchers/multiprocessing.py b/src/lightning/pytorch/strategies/launchers/multiprocessing.py
index 7431927df2..05e3fed561 100644
--- a/src/lightning/pytorch/strategies/launchers/multiprocessing.py
+++ b/src/lightning/pytorch/strategies/launchers/multiprocessing.py
@@ -254,7 +254,7 @@ class _MultiProcessingLauncher(_Launcher):
         """
         # NOTE: `get_extra_results` needs to be called before
         callback_metrics_bytes = extra["callback_metrics_bytes"]
-        callback_metrics = torch.load(io.BytesIO(callback_metrics_bytes))
+        callback_metrics = torch.load(io.BytesIO(callback_metrics_bytes), weights_only=True)
         trainer.callback_metrics.update(callback_metrics)
 
     @override
diff --git a/src/lightning/pytorch/strategies/model_parallel.py b/src/lightning/pytorch/strategies/model_parallel.py
index a136a1406e..fb45166378 100644
--- a/src/lightning/pytorch/strategies/model_parallel.py
+++ b/src/lightning/pytorch/strategies/model_parallel.py
@@ -38,7 +38,7 @@ from lightning.fabric.utilities.distributed import (
     _sync_ddp_if_available,
 )
 from lightning.fabric.utilities.distributed import group as _group
-from lightning.fabric.utilities.imports import _TORCH_GREATER_EQUAL_2_3
+from lightning.fabric.utilities.imports import _TORCH_GREATER_EQUAL_2_4
 from lightning.fabric.utilities.init import _materialize_distributed_module
 from lightning.fabric.utilities.load import _METADATA_FILENAME
 from lightning.fabric.utilities.optimizer import _optimizers_to_device
@@ -64,7 +64,7 @@ class ModelParallelStrategy(ParallelStrategy):
     Currently supports up to 2D parallelism. Specifically, it supports the combination of
     Fully Sharded Data-Parallel 2 (FSDP2) with Tensor Parallelism (DTensor). These PyTorch APIs are currently still
     experimental in PyTorch (see https://pytorch.org/docs/stable/distributed.tensor.parallel.html).
-    Requires PyTorch 2.3 or newer.
+    Requires PyTorch 2.4 or newer.
 
     Arguments:
         data_parallel_size: The number of devices within a data-parallel group. Defaults to ``"auto"``, which
@@ -86,8 +86,8 @@ class ModelParallelStrategy(ParallelStrategy):
         timeout: Optional[timedelta] = default_pg_timeout,
     ) -> None:
         super().__init__()
-        if not _TORCH_GREATER_EQUAL_2_3:
-            raise ImportError(f"{type(self).__name__} requires PyTorch 2.3 or higher.")
+        if not _TORCH_GREATER_EQUAL_2_4:
+            raise ImportError(f"{type(self).__name__} requires PyTorch 2.4 or higher.")
         self._data_parallel_size = data_parallel_size
         self._tensor_parallel_size = tensor_parallel_size
         self._save_distributed_checkpoint = save_distributed_checkpoint
@@ -170,7 +170,7 @@ class ModelParallelStrategy(ParallelStrategy):
         if any(isinstance(mod, FullyShardedDataParallel) for mod in self.model.modules()):
             raise TypeError(
                 "Found modules that are wrapped with `torch.distributed.fsdp.FullyShardedDataParallel`."
-                f" The `{self.__class__.__name__}` only supports the new FSDP2 APIs in PyTorch >= 2.3."
+                f" The `{self.__class__.__name__}` only supports the new FSDP2 APIs in PyTorch >= 2.4."
             )
 
         _materialize_distributed_module(self.model, self.root_device)
diff --git a/tests/tests_pytorch/conftest.py b/tests/tests_pytorch/conftest.py
index 97c17c4e46..78e81c7c5f 100644
--- a/tests/tests_pytorch/conftest.py
+++ b/tests/tests_pytorch/conftest.py
@@ -156,6 +156,7 @@ def thread_police_duuu_daaa_duuu_daaa():
         elif (
             thread.name == "QueueFeederThread"  # tensorboardX
             or thread.name == "QueueManagerThread"  # torch.compile
+            or "(_read_thread)" in thread.name  # torch.compile
         ):
             thread.join(timeout=20)
         elif isinstance(thread, TMonitor):
diff --git a/tests/tests_pytorch/models/test_torchscript.py b/tests/tests_pytorch/models/test_torchscript.py
index a5783f143f..993085729e 100644
--- a/tests/tests_pytorch/models/test_torchscript.py
+++ b/tests/tests_pytorch/models/test_torchscript.py
@@ -19,6 +19,7 @@ import pytest
 import torch
 from fsspec.implementations.local import LocalFileSystem
 from lightning.fabric.utilities.cloud_io import get_filesystem
+from lightning.fabric.utilities.imports import _IS_WINDOWS, _TORCH_GREATER_EQUAL_2_4
 from lightning.pytorch.core.module import LightningModule
 from lightning.pytorch.demos.boring_classes import BoringModel
 
@@ -26,6 +27,7 @@ from tests_pytorch.helpers.advanced_models import BasicGAN, ParityModuleRNN
 from tests_pytorch.helpers.runif import RunIf
 
 
+@pytest.mark.skipif(_IS_WINDOWS and _TORCH_GREATER_EQUAL_2_4, reason="not close on Windows + PyTorch 2.4")
 @pytest.mark.parametrize("modelclass", [BoringModel, ParityModuleRNN, BasicGAN])
 def test_torchscript_input_output(modelclass):
     """Test that scripted LightningModule forward works."""
@@ -45,6 +47,7 @@ def test_torchscript_input_output(modelclass):
     assert torch.allclose(script_output, model_output)
 
 
+@pytest.mark.skipif(_IS_WINDOWS and _TORCH_GREATER_EQUAL_2_4, reason="not close on Windows + PyTorch 2.4")
 @pytest.mark.parametrize("modelclass", [BoringModel, ParityModuleRNN, BasicGAN])
 def test_torchscript_example_input_output_trace(modelclass):
     """Test that traced LightningModule forward works with example_input_array."""
diff --git a/tests/tests_pytorch/plugins/precision/test_amp_integration.py b/tests/tests_pytorch/plugins/precision/test_amp_integration.py
index 10257531d5..bc9f779079 100644
--- a/tests/tests_pytorch/plugins/precision/test_amp_integration.py
+++ b/tests/tests_pytorch/plugins/precision/test_amp_integration.py
@@ -15,6 +15,7 @@ from unittest.mock import Mock
 
 import torch
 from lightning.fabric import seed_everything
+from lightning.fabric.utilities.imports import _TORCH_GREATER_EQUAL_2_4
 from lightning.pytorch import Trainer
 from lightning.pytorch.demos.boring_classes import BoringModel
 from lightning.pytorch.plugins.precision import MixedPrecision
@@ -28,7 +29,8 @@ class FusedOptimizerParityModel(BoringModel):
         self.fused = fused
 
     def configure_optimizers(self):
-        assert isinstance(self.trainer.precision_plugin.scaler, torch.cuda.amp.GradScaler)
+        scaler_cls = torch.amp.GradScaler if _TORCH_GREATER_EQUAL_2_4 else torch.cuda.amp.GradScaler
+        assert isinstance(self.trainer.precision_plugin.scaler, scaler_cls)
         return torch.optim.Adam(self.parameters(), lr=1.0, fused=self.fused)
 
 
diff --git a/tests/tests_pytorch/profilers/test_profiler.py b/tests/tests_pytorch/profilers/test_profiler.py
index 4d5c9762cc..44ce7c4a3a 100644
--- a/tests/tests_pytorch/profilers/test_profiler.py
+++ b/tests/tests_pytorch/profilers/test_profiler.py
@@ -21,6 +21,7 @@ from unittest.mock import patch
 import numpy as np
 import pytest
 import torch
+from lightning.fabric.utilities.imports import _TORCH_GREATER_EQUAL_2_4
 from lightning.pytorch import Callback, Trainer
 from lightning.pytorch.callbacks import EarlyStopping, StochasticWeightAveraging
 from lightning.pytorch.demos.boring_classes import BoringModel, ManualOptimBoringModel
@@ -430,7 +431,8 @@ def test_pytorch_profiler_trainer(fn, step_name, boring_model_cls, tmp_path):
 
 def test_pytorch_profiler_nested(tmp_path):
     """Ensure that the profiler handles nested context."""
-    pytorch_profiler = PyTorchProfiler(use_cuda=False, dirpath=tmp_path, filename="profiler", schedule=None)
+    kwargs = {} if _TORCH_GREATER_EQUAL_2_4 else {"use_cuda": False}
+    pytorch_profiler = PyTorchProfiler(dirpath=tmp_path, filename="profiler", schedule=None, **kwargs)
 
     with pytorch_profiler.profile("a"):
         a = torch.ones(42)
@@ -475,13 +477,14 @@ def test_pytorch_profiler_multiple_loggers(tmp_path):
 
 def test_register_record_function(tmp_path):
     use_cuda = torch.cuda.is_available()
+    kwargs = {} if _TORCH_GREATER_EQUAL_2_4 else {"use_cuda": torch.cuda.is_available()}
     pytorch_profiler = PyTorchProfiler(
         export_to_chrome=False,
-        use_cuda=use_cuda,
         dirpath=tmp_path,
         filename="profiler",
         schedule=None,
         on_trace_ready=None,
+        **kwargs,
     )
 
     class TestModel(BoringModel):
diff --git a/tests/tests_pytorch/strategies/test_deepspeed.py b/tests/tests_pytorch/strategies/test_deepspeed.py
index 9d7f01096b..be9428ff75 100644
--- a/tests/tests_pytorch/strategies/test_deepspeed.py
+++ b/tests/tests_pytorch/strategies/test_deepspeed.py
@@ -28,7 +28,7 @@ from lightning.pytorch.callbacks import Callback, LearningRateMonitor, ModelChec
 from lightning.pytorch.demos.boring_classes import BoringModel, RandomDataset, RandomIterableDataset
 from lightning.pytorch.loggers import CSVLogger
 from lightning.pytorch.plugins import DeepSpeedPrecision
-from lightning.pytorch.strategies.deepspeed import _DEEPSPEED_AVAILABLE, DeepSpeedStrategy
+from lightning.pytorch.strategies.deepspeed import DeepSpeedStrategy
 from lightning.pytorch.utilities.exceptions import MisconfigurationException
 from lightning.pytorch.utilities.imports import _TORCHMETRICS_GREATER_EQUAL_0_11 as _TM_GE_0_11
 from torch import Tensor, nn
@@ -38,11 +38,6 @@ from torchmetrics import Accuracy
 from tests_pytorch.helpers.datamodules import ClassifDataModule
 from tests_pytorch.helpers.runif import RunIf
 
-if _DEEPSPEED_AVAILABLE:
-    import deepspeed
-    from deepspeed.runtime.zero.stage_1_and_2 import DeepSpeedZeroOptimizer
-    from deepspeed.utils.zero_to_fp32 import convert_zero_checkpoint_to_fp32_state_dict
-
 
 class ModelParallelBoringModel(BoringModel):
     def __init__(self):
@@ -245,6 +240,7 @@ def test_deepspeed_auto_batch_size_config_select(_, __, tmp_path, dataset_cls, v
 def test_deepspeed_run_configure_optimizers(tmp_path):
     """Test end to end that deepspeed works with defaults (without ZeRO as that requires compilation), whilst using
     configure_optimizers for optimizers and schedulers."""
+    from deepspeed.runtime.zero.stage_1_and_2 import DeepSpeedZeroOptimizer
 
     class TestCB(Callback):
         def on_train_start(self, trainer, pl_module) -> None:
@@ -284,6 +280,7 @@ def test_deepspeed_run_configure_optimizers(tmp_path):
 def test_deepspeed_config(tmp_path, deepspeed_zero_config):
     """Test to ensure deepspeed works correctly when passed a DeepSpeed config object including optimizers/schedulers
     and saves the model weights to load correctly."""
+    from deepspeed.runtime.zero.stage_1_and_2 import DeepSpeedZeroOptimizer
 
     class TestCB(Callback):
         def on_train_start(self, trainer, pl_module) -> None:
@@ -397,6 +394,8 @@ def test_deepspeed_custom_activation_checkpointing_params():
 def test_deepspeed_custom_activation_checkpointing_params_forwarded(tmp_path):
     """Ensure if we modify the activation checkpointing parameters, we pass these to deepspeed.checkpointing.configure
     correctly."""
+    import deepspeed
+
     ds = DeepSpeedStrategy(
         partition_activations=True,
         cpu_checkpointing=True,
@@ -453,6 +452,8 @@ def test_deepspeed_assert_config_zero_offload_disabled(tmp_path, deepspeed_zero_
 @RunIf(min_cuda_gpus=2, standalone=True, deepspeed=True)
 def test_deepspeed_multigpu(tmp_path):
     """Test to ensure that DeepSpeed with multiple GPUs works and deepspeed distributed is initialized correctly."""
+    import deepspeed
+
     model = BoringModel()
     trainer = Trainer(
         default_root_dir=tmp_path,
@@ -978,6 +979,8 @@ def test_deepspeed_strategy_env_variables(mock_deepspeed_distributed, tmp_path,
 
 
 def _assert_save_model_is_equal(model, tmp_path, trainer):
+    from deepspeed.utils.zero_to_fp32 import convert_zero_checkpoint_to_fp32_state_dict
+
     checkpoint_path = os.path.join(tmp_path, "model.pt")
     checkpoint_path = trainer.strategy.broadcast(checkpoint_path)
     trainer.save_checkpoint(checkpoint_path)
diff --git a/tests/tests_pytorch/strategies/test_fsdp.py b/tests/tests_pytorch/strategies/test_fsdp.py
index 94e00f35ff..aec01b83e9 100644
--- a/tests/tests_pytorch/strategies/test_fsdp.py
+++ b/tests/tests_pytorch/strategies/test_fsdp.py
@@ -217,6 +217,7 @@ def test_custom_mixed_precision():
     assert strategy.mixed_precision_config == config
 
 
+@pytest.mark.filterwarnings("ignore::FutureWarning")
 @RunIf(min_cuda_gpus=2, skip_windows=True, standalone=True)
 def test_strategy_sync_batchnorm(tmp_path):
     """Test to ensure that sync_batchnorm works when using FSDP and GPU, and all stages can be run."""
@@ -233,6 +234,7 @@ def test_strategy_sync_batchnorm(tmp_path):
     _run_multiple_stages(trainer, model, os.path.join(tmp_path, "last.ckpt"))
 
 
+@pytest.mark.filterwarnings("ignore::FutureWarning")
 @RunIf(min_cuda_gpus=1, skip_windows=True)
 def test_modules_without_parameters(tmp_path):
     """Test that TorchMetrics get moved to the device despite not having any parameters."""
@@ -263,6 +265,7 @@ def test_modules_without_parameters(tmp_path):
     trainer.fit(model)
 
 
+@pytest.mark.filterwarnings("ignore::FutureWarning")
 @RunIf(min_cuda_gpus=2, skip_windows=True, standalone=True)
 @pytest.mark.parametrize("precision", ["16-mixed", pytest.param("bf16-mixed", marks=RunIf(bf16_cuda=True))])
 @pytest.mark.parametrize("state_dict_type", ["sharded", "full"])
@@ -284,6 +287,7 @@ def custom_auto_wrap_policy(
     return nonwrapped_numel >= 2
 
 
+@pytest.mark.filterwarnings("ignore::FutureWarning")
 @RunIf(min_cuda_gpus=2, skip_windows=True, standalone=True)
 @pytest.mark.parametrize("wrap_min_params", [2, 1024, 100000000])
 def test_strategy_full_state_dict(tmp_path, wrap_min_params):
@@ -319,6 +323,7 @@ def test_strategy_full_state_dict(tmp_path, wrap_min_params):
     assert all(_ex == _co for _ex, _co in zip(full_state_dict.keys(), correct_state_dict.keys()))
 
 
+@pytest.mark.filterwarnings("ignore::FutureWarning")
 @RunIf(min_cuda_gpus=2, skip_windows=True, standalone=True)
 @pytest.mark.parametrize(
     ("model", "strategy", "strategy_cfg"),
@@ -552,6 +557,7 @@ def test_strategy_load_optimizer_states_multiple(_, tmp_path):
         strategy.load_checkpoint(tmp_path / "one-state.ckpt")
 
 
+@pytest.mark.filterwarnings("ignore::FutureWarning")
 @RunIf(min_cuda_gpus=2, skip_windows=True, standalone=True)
 @pytest.mark.parametrize("wrap_min_params", [2, 1024, 100000000])
 def test_strategy_save_optimizer_states(tmp_path, wrap_min_params):
@@ -610,6 +616,7 @@ def test_strategy_save_optimizer_states(tmp_path, wrap_min_params):
     trainer.strategy.barrier()
 
 
+@pytest.mark.filterwarnings("ignore::FutureWarning")
 @RunIf(min_cuda_gpus=2, skip_windows=True, standalone=True)
 @pytest.mark.parametrize("wrap_min_params", [2, 1024, 100000000])
 def test_strategy_load_optimizer_states(wrap_min_params, tmp_path):
@@ -808,6 +815,7 @@ class TestFSDPCheckpointModel(BoringModel):
             torch.testing.assert_close(p0, p1, atol=0, rtol=0, equal_nan=True)
 
 
+@pytest.mark.filterwarnings("ignore::FutureWarning")
 @RunIf(min_cuda_gpus=2, standalone=True)
 def test_save_load_sharded_state_dict(tmp_path):
     """Test FSDP saving and loading with the sharded state dict format."""
@@ -917,11 +925,20 @@ def test_module_init_context(precision, expected_dtype, tmp_path):
     _run_setup_assertions(empty_init=True, expected_device=torch.device("meta"))
 
 
+@pytest.mark.filterwarnings("ignore::FutureWarning")
 @RunIf(min_cuda_gpus=2, standalone=True, min_torch="2.3.0")
 def test_save_sharded_and_consolidate_and_load(tmp_path):
     """Test the consolidation of a FSDP-sharded checkpoint into a single file."""
 
-    model = BoringModel()
+    class CustomModel(BoringModel):
+        def configure_optimizers(self):
+            # Use Adam instead of SGD for this test because it has state
+            # In PyTorch >= 2.4, saving an optimizer with empty state would result in a `KeyError: 'state'`
+            # when loading the optimizer state-dict back.
+            # TODO: To resolve this, switch to the new `torch.distributed.checkpoint` APIs in FSDPStrategy
+            return torch.optim.Adam(self.parameters(), lr=0.1)
+
+    model = CustomModel()
     trainer = Trainer(
         default_root_dir=tmp_path,
         accelerator="cuda",
@@ -942,7 +959,7 @@ def test_save_sharded_and_consolidate_and_load(tmp_path):
         torch.save(checkpoint, checkpoint_path_full)
     trainer.strategy.barrier()
 
-    model = BoringModel()
+    model = CustomModel()
     trainer = Trainer(
         default_root_dir=tmp_path,
         accelerator="cuda",
diff --git a/tests/tests_pytorch/strategies/test_model_parallel.py b/tests/tests_pytorch/strategies/test_model_parallel.py
index 4f30ae8fef..731da66d4a 100644
--- a/tests/tests_pytorch/strategies/test_model_parallel.py
+++ b/tests/tests_pytorch/strategies/test_model_parallel.py
@@ -28,20 +28,20 @@ from lightning.pytorch.strategies import ModelParallelStrategy
 from tests_pytorch.helpers.runif import RunIf
 
 
-@mock.patch("lightning.pytorch.strategies.model_parallel._TORCH_GREATER_EQUAL_2_3", False)
-def test_torch_greater_equal_2_3():
-    with pytest.raises(ImportError, match="ModelParallelStrategy requires PyTorch 2.3 or higher"):
+@mock.patch("lightning.pytorch.strategies.model_parallel._TORCH_GREATER_EQUAL_2_4", False)
+def test_torch_greater_equal_2_4():
+    with pytest.raises(ImportError, match="ModelParallelStrategy requires PyTorch 2.4 or higher"):
         ModelParallelStrategy()
 
 
-@RunIf(min_torch="2.3")
+@RunIf(min_torch="2.4")
 def test_device_mesh_access():
     strategy = ModelParallelStrategy()
     with pytest.raises(RuntimeError, match="Accessing the device mesh .* not allowed"):
         _ = strategy.device_mesh
 
 
-@RunIf(min_torch="2.3")
+@RunIf(min_torch="2.4")
 @pytest.mark.parametrize(
     ("num_nodes", "devices", "invalid_dp_size", "invalid_tp_size"),
     [
@@ -69,7 +69,7 @@ def test_validate_device_mesh_dimensions(num_nodes, devices, invalid_dp_size, in
         strategy.setup_environment()
 
 
-@RunIf(min_torch="2.3")
+@RunIf(min_torch="2.4")
 def test_fsdp_v1_modules_unsupported():
     """Test that the strategy won't allow setting up a module wrapped with the legacy FSDP API."""
     from torch.distributed.fsdp import FullyShardedDataParallel
@@ -85,11 +85,11 @@ def test_fsdp_v1_modules_unsupported():
     strategy._lightning_module = model
     strategy._accelerator = Mock()
 
-    with pytest.raises(TypeError, match="only supports the new FSDP2 APIs in PyTorch >= 2.3"):
+    with pytest.raises(TypeError, match="only supports the new FSDP2 APIs in PyTorch >= 2.4"):
         strategy.setup(Mock())
 
 
-@RunIf(min_torch="2.3")
+@RunIf(min_torch="2.4")
 def test_configure_model_required():
     class Model1(LightningModule):
         pass
@@ -114,7 +114,7 @@ def test_configure_model_required():
     strategy.setup(Mock())
 
 
-@RunIf(min_torch="2.3")
+@RunIf(min_torch="2.4")
 def test_save_checkpoint_storage_options(tmp_path):
     """Test that the strategy does not accept storage options for saving checkpoints."""
     strategy = ModelParallelStrategy()
@@ -124,7 +124,7 @@ def test_save_checkpoint_storage_options(tmp_path):
         strategy.save_checkpoint(checkpoint=Mock(), filepath=tmp_path, storage_options=Mock())
 
 
-@RunIf(min_torch="2.3")
+@RunIf(min_torch="2.4")
 @mock.patch("lightning.pytorch.strategies.model_parallel.ModelParallelStrategy.broadcast", lambda _, x: x)
 @mock.patch("lightning.fabric.plugins.io.torch_io._atomic_save")
 @mock.patch("lightning.pytorch.strategies.model_parallel.shutil")
@@ -174,7 +174,7 @@ def test_save_checkpoint_path_exists(shutil_mock, torch_save_mock, tmp_path):
     assert path.is_dir()
 
 
-@RunIf(min_torch="2.3")
+@RunIf(min_torch="2.4")
 @mock.patch("lightning.fabric.strategies.model_parallel._has_dtensor_modules", return_value=True)
 def test_load_unknown_checkpoint_type(_, tmp_path):
     """Test that the strategy validates the contents at the checkpoint path."""
@@ -187,7 +187,7 @@ def test_load_unknown_checkpoint_type(_, tmp_path):
         strategy.load_checkpoint(checkpoint_path=path)
 
 
-@RunIf(min_torch="2.3")
+@RunIf(min_torch="2.4")
 @mock.patch("lightning.pytorch.strategies.model_parallel._setup_device_mesh")
 @mock.patch("torch.distributed.init_process_group")
 def test_set_timeout(init_process_group_mock, _):
@@ -207,7 +207,7 @@ def test_set_timeout(init_process_group_mock, _):
     )
 
 
-@RunIf(min_torch="2.3")
+@RunIf(min_torch="2.4")
 def test_meta_device_materialization():
     """Test that the `setup()` method materializes meta-device tensors in the LightningModule."""
 
diff --git a/tests/tests_pytorch/strategies/test_model_parallel_integration.py b/tests/tests_pytorch/strategies/test_model_parallel_integration.py
index 5aff576554..5947277b05 100644
--- a/tests/tests_pytorch/strategies/test_model_parallel_integration.py
+++ b/tests/tests_pytorch/strategies/test_model_parallel_integration.py
@@ -111,7 +111,7 @@ class FSDP2TensorParallelModel(TemplateModel):
         _parallelize_feed_forward_fsdp2_tp(self.model, device_mesh=self.device_mesh)
 
 
-@RunIf(min_torch="2.3", standalone=True, min_cuda_gpus=4)
+@RunIf(min_torch="2.4", standalone=True, min_cuda_gpus=4)
 def test_setup_device_mesh():
     from torch.distributed.device_mesh import DeviceMesh
 
@@ -168,7 +168,7 @@ def test_setup_device_mesh():
     trainer.fit(model)
 
 
-@RunIf(min_torch="2.3", standalone=True, min_cuda_gpus=2)
+@RunIf(min_torch="2.4", standalone=True, min_cuda_gpus=2)
 def test_tensor_parallel():
     from torch.distributed._tensor import DTensor
 
@@ -209,7 +209,7 @@ def test_tensor_parallel():
     trainer.fit(model)
 
 
-@RunIf(min_torch="2.3", standalone=True, min_cuda_gpus=4)
+@RunIf(min_torch="2.4", standalone=True, min_cuda_gpus=4)
 def test_fsdp2_tensor_parallel():
     from torch.distributed._tensor import DTensor
 
@@ -266,7 +266,7 @@ def test_fsdp2_tensor_parallel():
     trainer.fit(model)
 
 
-@RunIf(min_torch="2.3", min_cuda_gpus=2, standalone=True)
+@RunIf(min_torch="2.4", min_cuda_gpus=2, standalone=True)
 def test_modules_without_parameters(tmp_path):
     """Test that TorchMetrics get moved to the device despite not having any parameters."""
 
@@ -297,7 +297,7 @@ def test_modules_without_parameters(tmp_path):
     trainer.fit(model)
 
 
-@RunIf(min_torch="2.3", min_cuda_gpus=2, standalone=True)
+@RunIf(min_torch="2.4", min_cuda_gpus=2, standalone=True)
 @pytest.mark.parametrize(
     ("precision", "expected_dtype"),
     [
@@ -343,7 +343,7 @@ def test_module_init_context(precision, expected_dtype, tmp_path):
     _run_setup_assertions(empty_init=True, expected_device=torch.device("meta"))
 
 
-@RunIf(min_torch="2.3", min_cuda_gpus=2, skip_windows=True, standalone=True)
+@RunIf(min_torch="2.4", min_cuda_gpus=2, skip_windows=True, standalone=True)
 @pytest.mark.parametrize("save_distributed_checkpoint", [True, False])
 def test_strategy_state_dict(tmp_path, save_distributed_checkpoint):
     """Test that the strategy returns the correct state dict of the LightningModule."""
@@ -377,7 +377,7 @@ def test_strategy_state_dict(tmp_path, save_distributed_checkpoint):
         assert list(state_dict.keys()) == list(correct_state_dict.keys())
 
 
-@RunIf(min_torch="2.3", min_cuda_gpus=2, skip_windows=True, standalone=True)
+@RunIf(min_torch="2.4", min_cuda_gpus=2, skip_windows=True, standalone=True)
 def test_load_full_state_checkpoint_into_regular_model(tmp_path):
     """Test that a full-state checkpoint saved from a distributed model can be loaded back into a regular model."""
 
diff --git a/tests/tests_pytorch/trainer/connectors/test_accelerator_connector.py b/tests/tests_pytorch/trainer/connectors/test_accelerator_connector.py
index 32f16c92f1..65c5777e28 100644
--- a/tests/tests_pytorch/trainer/connectors/test_accelerator_connector.py
+++ b/tests/tests_pytorch/trainer/connectors/test_accelerator_connector.py
@@ -1067,7 +1067,7 @@ def test_bitsandbytes_precision_cuda_required(monkeypatch):
         _AcceleratorConnector(accelerator="cpu", plugins=BitsandbytesPrecision(mode="int8"))
 
 
-@RunIf(min_torch="2.3")
+@RunIf(min_torch="2.4")
 @pytest.mark.parametrize(
     ("precision", "raises"),
     [("32-true", False), ("16-true", False), ("bf16-true", False), ("16-mixed", True), ("bf16-mixed", False)],
diff --git a/tests/tests_pytorch/utilities/test_compile.py b/tests/tests_pytorch/utilities/test_compile.py
index 9da6c390e5..67f992421f 100644
--- a/tests/tests_pytorch/utilities/test_compile.py
+++ b/tests/tests_pytorch/utilities/test_compile.py
@@ -11,16 +11,18 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
+import os
 import sys
+from contextlib import nullcontext
 from unittest import mock
 
 import pytest
 import torch
-from lightning.fabric.utilities.imports import _TORCH_GREATER_EQUAL_2_2
+from lightning.fabric.utilities.imports import _TORCH_GREATER_EQUAL_2_2, _TORCH_GREATER_EQUAL_2_4
 from lightning.pytorch import LightningModule, Trainer
 from lightning.pytorch.demos.boring_classes import BoringModel
 from lightning.pytorch.utilities.compile import from_compiled, to_uncompiled
-from lightning_utilities.core import module_available
+from lightning_utilities.core.imports import RequirementCache
 
 from tests_pytorch.conftest import mock_cuda_count
 from tests_pytorch.helpers.runif import RunIf
@@ -67,10 +69,20 @@ def test_trainer_compiled_model(_, tmp_path, monkeypatch, mps_count_0):
     assert trainer.model._compiler_ctx is None
 
     # some strategies do not support it
-    if module_available("deepspeed"):
+    if RequirementCache("deepspeed"):
         compiled_model = torch.compile(model)
         mock_cuda_count(monkeypatch, 2)
-        trainer = Trainer(strategy="deepspeed", accelerator="cuda", **trainer_kwargs)
+
+        # TODO: Update deepspeed to avoid deprecation warning for `torch.cuda.amp.custom_fwd` on import
+        warn_context = (
+            pytest.warns(FutureWarning, match="torch.cuda.amp.*is deprecated")
+            if _TORCH_GREATER_EQUAL_2_4
+            else nullcontext()
+        )
+
+        with warn_context:
+            trainer = Trainer(strategy="deepspeed", accelerator="cuda", **trainer_kwargs)
+
         with pytest.raises(RuntimeError, match="Using a compiled model is incompatible with the current strategy.*"):
             trainer.fit(compiled_model)
 
@@ -122,6 +134,7 @@ def test_compile_uncompile():
     sys.platform == "win32" and _TORCH_GREATER_EQUAL_2_2, strict=False, reason="RuntimeError: Failed to import"
 )
 @RunIf(dynamo=True)
+@mock.patch.dict(os.environ, {})
 def test_trainer_compiled_model_that_logs(tmp_path):
     class MyModel(BoringModel):
         def training_step(self, batch, batch_idx):
@@ -152,6 +165,7 @@ def test_trainer_compiled_model_that_logs(tmp_path):
     sys.platform == "win32" and _TORCH_GREATER_EQUAL_2_2, strict=False, reason="RuntimeError: Failed to import"
 )
 @RunIf(dynamo=True)
+@mock.patch.dict(os.environ, {})
 def test_trainer_compiled_model_test(tmp_path):
     model = BoringModel()
     compiled_model = torch.compile(model)