doc: update instructions on installing from renovated nightly index

Signed-off-by: Shengqi Chen <harry-chen@outlook.com>

Author: Harry-Chen

docs/getting_started/installation/cpu.md

@@ -46,7 +46,10 @@ vLLM is a Python library that supports the following CPU variants. Select your C
 
 ### Pre-built wheels
 
-Currently, there are no pre-built CPU wheels.
+Please refer to the instructions for [pre-built wheels on GPU](./gpu.md#pre-built-wheels).
+
+When specifying the index URL, please make sure to use the `cpu` variant subdirectory.
+For example, the nightly build index is: `https://wheels.vllm.ai/nightly/cpu/`.
 
 ### Build wheel from source
 

docs/getting_started/installation/gpu.cuda.inc.md

@@ -26,43 +26,50 @@ uv pip install vllm --torch-backend=auto
 
 ??? console "pip"
     ```bash
-    # Install vLLM with CUDA 12.8.
-    pip install vllm --extra-index-url https://download.pytorch.org/whl/cu128
+    # Install vLLM with CUDA 12.9.
+    pip install vllm --extra-index-url https://download.pytorch.org/whl/cu129
     ```
 
-We recommend leveraging `uv` to [automatically select the appropriate PyTorch index at runtime](https://docs.astral.sh/uv/guides/integration/pytorch/#automatic-backend-selection) by inspecting the installed CUDA driver version via `--torch-backend=auto` (or `UV_TORCH_BACKEND=auto`). To select a specific backend (e.g., `cu126`), set `--torch-backend=cu126` (or `UV_TORCH_BACKEND=cu126`). If this doesn't work, try running `uv self update` to update `uv` first.
+We recommend leveraging `uv` to [automatically select the appropriate PyTorch index at runtime](https://docs.astral.sh/uv/guides/integration/pytorch/#automatic-backend-selection) by inspecting the installed CUDA driver version via `--torch-backend=auto` (or `UV_TORCH_BACKEND=auto`). To select a specific backend (e.g., `cu128`), set `--torch-backend=cu128` (or `UV_TORCH_BACKEND=cu128`). If this doesn't work, try running `uv self update` to update `uv` first.
 
 !!! note
     NVIDIA Blackwell GPUs (B200, GB200) require a minimum of CUDA 12.8, so make sure you are installing PyTorch wheels with at least that version. PyTorch itself offers a [dedicated interface](https://pytorch.org/get-started/locally/) to determine the appropriate pip command to run for a given target configuration.
 
-As of now, vLLM's binaries are compiled with CUDA 12.8 and public PyTorch release versions by default. We also provide vLLM binaries compiled with CUDA 12.6, 11.8, and public PyTorch release versions:
+As of now, vLLM's binaries are compiled with CUDA 12.9 and public PyTorch release versions by default. We also provide vLLM binaries compiled with CUDA 12.8, 13.0, and public PyTorch release versions:
 
 ```bash
-# Install vLLM with a specific CUDA version (e.g., 11.8 or 12.6).
+# Install vLLM with a specific CUDA version (e.g., 13.0).
 export VLLM_VERSION=$(curl -s https://api.github.com/repos/vllm-project/vllm/releases/latest | jq -r .tag_name | sed 's/^v//')
-export CUDA_VERSION=118 # or 126
-uv pip install https://github.com/vllm-project/vllm/releases/download/v${VLLM_VERSION}/vllm-${VLLM_VERSION}+cu${CUDA_VERSION}-cp38-abi3-manylinux1_x86_64.whl --extra-index-url https://download.pytorch.org/whl/cu${CUDA_VERSION}
+export CUDA_VERSION=130 # or other
+uv pip install https://github.com/vllm-project/vllm/releases/download/v${VLLM_VERSION}/vllm-${VLLM_VERSION}+cu${CUDA_VERSION}-cp38-abi3-manylinux_2_31_x86_64.whl --extra-index-url https://download.pytorch.org/whl/cu${CUDA_VERSION}
 ```
 
 #### Install the latest code
 
-LLM inference is a fast-evolving field, and the latest code may contain bug fixes, performance improvements, and new features that are not released yet. To allow users to try the latest code without waiting for the next release, vLLM provides wheels for Linux running on an x86 platform with CUDA 12 for every commit since `v0.5.3`.
+LLM inference is a fast-evolving field, and the latest code may contain bug fixes, performance improvements, and new features that are not released yet. To allow users to try the latest code without waiting for the next release, vLLM provides wheels for every commit since `v0.5.3` on <https://wheels.vllm.ai/nightly>. There are multiple indices that could be used:
+
+* `https://wheels.vllm.ai/nightly`: the default variant (CUDA with version specified in `VLLM_MAIN_CUDA_VERSION`) built with the last commit on the `main` branch. Currently it is CUDA 12.9.
+* `https://wheels.vllm.ai/nightly/<variant>`: all other variants. Now this includes `cu130`, and `cpu`. The default variant (`cu129`) also has a subdirectory to keep consistency.
+
+To install from nightly index, run:
 
 ```bash
 uv pip install -U vllm \
     --torch-backend=auto \
-    --extra-index-url https://wheels.vllm.ai/nightly
+    --extra-index-url https://wheels.vllm.ai/nightly # add variant subdirectory here if needed
 ```
 
-??? console "pip"
+!!! warning "`pip` caveat"
+
+    Using `pip` to install from nightly indices is _not supported_, because `pip` combines packages from `--extra-index-url` and the default index, choosing only the latest version, which makes it difficult to install a development version prior to the released version. In contrast, `uv` gives the extra index [higher priority than the default index](https://docs.astral.sh/uv/pip/compatibility/#packages-that-exist-on-multiple-indexes).
+
+    If you insist on using `pip`, you have to specify the full URL of the wheel file (which can be obtained from the web page).
+
     ```bash
-    pip install -U vllm \
-        --pre \
-        --extra-index-url https://wheels.vllm.ai/nightly
+    pip install -U https://wheels.vllm.ai/nightly/vllm-0.11.2.dev399%2Bg3c7461c18-cp38-abi3-manylinux_2_31_x86_64.whl # current nightly build (the filename will change!)
+    pip install -U https://wheels.vllm.ai/${VLLM_COMMIT}/vllm-0.11.2.dev399%2Bg3c7461c18-cp38-abi3-manylinux_2_31_x86_64.whl # from specific commit
     ```
 
-    `--pre` is required for `pip` to consider pre-released versions.
-
 ##### Install specific revisions
 
 If you want to access the wheels for previous commits (e.g. to bisect the behavior change, performance regression), you can specify the commit hash in the URL:
@@ -71,29 +78,9 @@ If you want to access the wheels for previous commits (e.g. to bisect the behavi
 export VLLM_COMMIT=72d9c316d3f6ede485146fe5aabd4e61dbc59069 # use full commit hash from the main branch
 uv pip install vllm \
     --torch-backend=auto \
-    --extra-index-url https://wheels.vllm.ai/${VLLM_COMMIT}
+    --extra-index-url https://wheels.vllm.ai/${VLLM_COMMIT} # add variant subdirectory here if needed
 ```
 
-The `uv` approach works for vLLM `v0.6.6` and later and offers an easy-to-remember command. A unique feature of `uv` is that packages in `--extra-index-url` have [higher priority than the default index](https://docs.astral.sh/uv/pip/compatibility/#packages-that-exist-on-multiple-indexes). If the latest public release is `v0.6.6.post1`, `uv`'s behavior allows installing a commit before `v0.6.6.post1` by specifying the `--extra-index-url`. In contrast, `pip` combines packages from `--extra-index-url` and the default index, choosing only the latest version, which makes it difficult to install a development version prior to the released version.
-
-??? note "pip"
-    If you want to access the wheels for previous commits (e.g. to bisect the behavior change,
-    performance regression), due to the limitation of `pip`, you have to specify the full URL of the
-    wheel file by embedding the commit hash in the URL:
-
-    ```bash
-    export VLLM_COMMIT=33f460b17a54acb3b6cc0b03f4a17876cff5eafd # use full commit hash from the main branch
-    pip install https://wheels.vllm.ai/${VLLM_COMMIT}/vllm-1.0.0.dev-cp38-abi3-manylinux1_x86_64.whl
-    ```
-
-    Note that the wheels are built with Python 3.8 ABI (see [PEP
-    425](https://peps.python.org/pep-0425/) for more details about ABI), so **they are compatible
-    with Python 3.8 and later**. The version string in the wheel file name (`1.0.0.dev`) is just a
-    placeholder to have a unified URL for the wheels, the actual versions of wheels are contained in
-    the wheel metadata (the wheels listed in the extra index url have correct versions). Although we
-    don't support Python 3.8 any more (because PyTorch 2.5 dropped support for Python 3.8), the
-    wheels are still built with Python 3.8 ABI to keep the same wheel name as before.
-
 # --8<-- [end:pre-built-wheels]
 # --8<-- [start:build-wheel-from-source]