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[Doc] Update CUDA 13 install guide to install torch first (#18404)

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Co-authored-by: Baizhou Zhang <sobereddiezhang@gmail.com>
This commit is contained in:
Mohammad Miadh Angkad
2026-02-07 18:04:37 +08:00
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parent 52401bec1d
commit c47c2f9466
1 changed files with 25 additions and 13 deletions
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docs/get_started/install.md
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@@ -11,22 +11,34 @@ It is recommended to use uv for faster installation:
```bash
pip install --upgrade pip
pip install uv
uv pip install "sglang"
uv pip install sglang
```
**Quick fixes to common problems**
- For CUDA 13, Docker is recommended (see Method 3 note on B300/GB300/CUDA 13). If you do not have Docker access, an extra index url needs to be provided when installing wheels:
```
uv pip install "sglang" --extra-index-url https://download.pytorch.org/whl/cu130
```
- The `sgl_kernel` wheel for CUDA 13 can be downloaded from [the sgl-project whl releases](https://github.com/sgl-project/whl/blob/gh-pages/cu130/sgl-kernel/index.html). Replace `X.Y.Z` with the `sgl_kernel` version required by your SGLang install (you can find this by running `uv pip show sgl_kernel`). Examples:
```bash
# x86_64
uv pip install "https://github.com/sgl-project/whl/releases/download/vX.Y.Z/sgl_kernel-X.Y.Z+cu130-cp310-abi3-manylinux2014_x86_64.whl"
### For CUDA 13
# aarch64
uv pip install "https://github.com/sgl-project/whl/releases/download/vX.Y.Z/sgl_kernel-X.Y.Z+cu130-cp310-abi3-manylinux2014_aarch64.whl"
```
Docker is recommended (see Method 3 note on B300/GB300/CUDA 13). If you do not have Docker access, follow these steps:
1. Install PyTorch with CUDA 13 support first:
```bash
# Replace X.Y.Z with the version by your SGLang install
uv pip install torch==X.Y.Z torchvision torchaudio --index-url https://download.pytorch.org/whl/cu130
```
2. Install sglang:
```bash
uv pip install sglang
```
3. Install the `sgl_kernel` wheel for CUDA 13 from [the sgl-project whl releases](https://github.com/sgl-project/whl/blob/gh-pages/cu130/sgl-kernel/index.html). Replace `X.Y.Z` with the `sgl_kernel` version required by your SGLang install (you can find this by running `uv pip show sgl_kernel`). Examples:
```bash
# x86_64
uv pip install "https://github.com/sgl-project/whl/releases/download/vX.Y.Z/sgl_kernel-X.Y.Z+cu130-cp310-abi3-manylinux2014_x86_64.whl"
# aarch64
uv pip install "https://github.com/sgl-project/whl/releases/download/vX.Y.Z/sgl_kernel-X.Y.Z+cu130-cp310-abi3-manylinux2014_aarch64.whl"
```
### **Quick fixes to common problems**
- If you encounter `OSError: CUDA_HOME environment variable is not set`. Please set it to your CUDA install root with either of the following solutions:
1. Use `export CUDA_HOME=/usr/local/cuda-<your-cuda-version>` to set the `CUDA_HOME` environment variable.
2. Install FlashInfer first following [FlashInfer installation doc](https://docs.flashinfer.ai/installation.html), then install SGLang as described above.