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[Docs] Use --torch-backend=auto for editable install docs (#39511)

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Signed-off-by: Michael Goin <mgoin64@gmail.com>
This commit is contained in:
Michael Goin
2026-04-10 15:27:02 -07:00
committed by GitHub
parent 11e2375fe2
commit 7f0b8f2020
1 changed files with 6 additions and 6 deletions
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docs/getting_started/installation/gpu.cuda.inc.md
+6 -6
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@@ -1,12 +1,12 @@
<!-- markdownlint-disable MD041 MD051 -->
--8<-- [start:installation]
vLLM contains pre-compiled C++ and CUDA (12.8) binaries.
vLLM contains pre-compiled C++ and CUDA (12.9) binaries.
--8<-- [end:installation]
--8<-- [start:requirements]
- GPU: compute capability 7.0 or higher (e.g., V100, T4, RTX20xx, A100, L4, H100, etc.)
- GPU: compute capability 7.5 or higher (e.g., T4, RTX20xx, A100, L4, H100, B200, etc.)
--8<-- [end:requirements]
--8<-- [start:set-up-using-python]
@@ -31,7 +31,7 @@ uv pip install vllm --torch-backend=auto
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., `cu128`), set `--torch-backend=cu128` (or `UV_TORCH_BACKEND=cu128`). 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., `cu130`), set `--torch-backend=cu130` (or `UV_TORCH_BACKEND=cu130`). 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.
@@ -93,7 +93,7 @@ If you only need to change Python code, you can build and install vLLM without c
```bash
git clone https://github.com/vllm-project/vllm.git
cd vllm
VLLM_USE_PRECOMPILED=1 uv pip install --editable .
VLLM_USE_PRECOMPILED=1 uv pip install --editable . --torch-backend=auto
```
This command will do the following:
@@ -134,7 +134,7 @@ If you want to modify C++ or CUDA code, you'll need to build vLLM from source. T
```bash
git clone https://github.com/vllm-project/vllm.git
cd vllm
uv pip install -e .
uv pip install -e . --torch-backend=auto
```
!!! tip
@@ -185,7 +185,7 @@ To achieve this, you can set the environment variable VLLM_CUTLASS_SRC_DIR to po
```bash
git clone https://github.com/vllm-project/vllm.git
cd vllm
VLLM_CUTLASS_SRC_DIR=/path/to/cutlass uv pip install -e .
VLLM_CUTLASS_SRC_DIR=/path/to/cutlass uv pip install -e . --torch-backend=auto
```
##### Troubleshooting