[Doc] Update installation doc for build from source and explain the dependency on torch/cuda version

docs/source/getting_started/installation.rst

@@ -19,7 +19,7 @@ You can install vLLM using pip:
 
 .. code-block:: console
 
-    $ # (Optional) Create a new conda environment.
+    $ # (Recommended) Create a new conda environment.
     $ conda create -n myenv python=3.9 -y
     $ conda activate myenv
 
@@ -28,24 +28,19 @@ You can install vLLM using pip:
 
 .. note::
 
-    As of now, vLLM's binaries are compiled on CUDA 12.1 by default.
-    However, you can install vLLM with CUDA 11.8 by running:
+    As of now, vLLM's binaries are compiled with CUDA 12.1 and public PyTorch release versions by default.
+    We also provide vLLM binaries compiled with CUDA 11.8 and public PyTorch release versions:
 
     .. code-block:: console
 
         $ # Install vLLM with CUDA 11.8.
-        $ export VLLM_VERSION=0.2.4
+        $ export VLLM_VERSION=0.4.0
         $ export PYTHON_VERSION=39
-        $ pip install https://github.com/vllm-project/vllm/releases/download/v${VLLM_VERSION}/vllm-${VLLM_VERSION}+cu118-cp${PYTHON_VERSION}-cp${PYTHON_VERSION}-manylinux1_x86_64.whl
+        $ pip install https://github.com/vllm-project/vllm/releases/download/v${VLLM_VERSION}/vllm-${VLLM_VERSION}+cu118-cp${PYTHON_VERSION}-cp${PYTHON_VERSION}-manylinux1_x86_64.whl --extra-index-url https://download.pytorch.org/whl/cu118
 
-        $ # Re-install PyTorch with CUDA 11.8.
-        $ pip uninstall torch -y
-        $ pip install torch --upgrade --index-url https://download.pytorch.org/whl/cu118
-
-        $ # Re-install xFormers with CUDA 11.8.
-        $ pip uninstall xformers -y
-        $ pip install --upgrade xformers --index-url https://download.pytorch.org/whl/cu118
+    In order to be performant, vLLM has to compile many cuda kernels. The compilation unfortunately introduces binary incompatibility with other CUDA versions and PyTorch versions, even for the same PyTorch version with different building configurations.
 
+    Therefore, it is recommended to install vLLM with a **fresh new** conda environment. If either you have a different CUDA version or you want to use an existing PyTorch installation, you need to build vLLM from source. See below for instructions.
 
 .. _build_from_source:
 
@@ -77,6 +72,20 @@ You can also build and install vLLM from source:
         $ # Use `--ipc=host` to make sure the shared memory is large enough.
         $ docker run --gpus all -it --rm --ipc=host nvcr.io/nvidia/pytorch:23.10-py3
 
+    If you don't want to use docker, it is recommended to have a full installation of CUDA Toolkit. You can download and install it from `the official website <https://developer.nvidia.com/cuda-12-1-0-download-archive>`_. After installation, set the environment variable `CUDA_HOME` to the installation path of CUDA Toolkit, and make sure that the `nvcc` compiler is in your `PATH`, e.g.:
+
+    .. code-block:: console
+
+        $ export CUDA_HOME=/usr/local/cuda
+        $ export PATH="${CUDA_HOME}/bin:$PATH"
+
+    Here is a sanity check to verify that the CUDA Toolkit is correctly installed:
+
+    .. code-block:: console
+
+        $ nvcc --version # verify that nvcc is in your PATH
+        $ ${CUDA_HOME}/bin/nvcc --version # verify that nvcc is in your CUDA_HOME
+
 .. note::
     If you are developing the C++ backend of vLLM, consider building vLLM with