diff --git a/docs/getting_started/installation/gpu/cuda.inc.md b/docs/getting_started/installation/gpu/cuda.inc.md
index 01c5f5fc02f3e..9e64c6f2540af 100644
--- a/docs/getting_started/installation/gpu/cuda.inc.md
+++ b/docs/getting_started/installation/gpu/cuda.inc.md
@@ -165,7 +165,19 @@ There are scenarios where the PyTorch dependency cannot be easily installed with
 - Building vLLM with PyTorch nightly or a custom PyTorch build.
 - Building vLLM with aarch64 and CUDA (GH200), where the PyTorch wheels are not available on PyPI. Currently, only the PyTorch nightly has wheels for aarch64 with CUDA. You can run `uv pip install --index-url https://download.pytorch.org/whl/nightly/cu128 torch torchvision torchaudio` to [install PyTorch nightly](https://pytorch.org/get-started/locally/) and then build vLLM on top of it.
 
-To build vLLM using an existing PyTorch installation, it is recommended to use `uv`, because it has [a unique mechanism](https://docs.astral.sh/uv/concepts/projects/config/#disabling-build-isolation) for disabling build isolation for specific packages and vLLM leverages this mechanism to specify `torch` as the package to disable build isolation.
+To build vLLM using an existing PyTorch installation:
+
+```bash
+# install PyTorch first, either from PyPI or from source
+git clone https://github.com/vllm-project/vllm.git
+cd vllm
+python use_existing_torch.py
+uv pip install -r requirements/build.txt
+uv pip install --no-build-isolation -e .
+```
+
+Alternatively: if you are exclusively using `uv` to create and manage virtual environments, it has [a unique mechanism](https://docs.astral.sh/uv/concepts/projects/config/#disabling-build-isolation)
+for disabling build isolation for specific packages. vLLM can leverage this mechanism to specify `torch` as the package to disable build isolation for:
 
 ```bash
 # install PyTorch first, either from PyPI or from source