From 7ec18f7e4e5c015a03267597d12c84397869c121 Mon Sep 17 00:00:00 2001 From: Baizhou Zhang Date: Wed, 6 May 2026 02:37:23 -0700 Subject: [PATCH] [Doc] Fix instruction on Cuda 13 environments (#24516) --- docs_new/docs/get-started/install.mdx | 31 +-------------------------- 1 file changed, 1 insertion(+), 30 deletions(-) diff --git a/docs_new/docs/get-started/install.mdx b/docs_new/docs/get-started/install.mdx index ad2d801af..a7231d153 100644 --- a/docs_new/docs/get-started/install.mdx +++ b/docs_new/docs/get-started/install.mdx @@ -25,35 +25,6 @@ pip install uv uv pip install sglang ``` -### For CUDA 13 - -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 Command -# 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 Command -uv pip install sglang -``` - -3. Install the `sglang-kernel` wheel for CUDA 13 from [the sgl-project whl releases](https://github.com/sgl-project/whl/blob/gh-pages/cu130/sglang-kernel/index.html). Replace `X.Y.Z` with the `sglang-kernel` version required by your SGLang install (you can find this by running `uv pip show sglang-kernel`). Examples: -```bash Command -# x86_64 -uv pip install "https://github.com/sgl-project/whl/releases/download/vX.Y.Z/sglang_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/sglang_kernel-X.Y.Z+cu130-cp310-abi3-manylinux2014_aarch64.whl" -``` - -4. If you encounter `ptxas fatal : Value 'sm_103a' is not defined for option 'gpu-name'` on B300/GB300, fix it with: -```bash Command -export TRITON_PTXAS_PATH=/usr/local/cuda/bin/ptxas -``` - ### 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-` to set the `CUDA_HOME` environment variable. @@ -107,7 +78,7 @@ docker run --gpus all \ You can also find the nightly docker images [here](https://hub.docker.com/r/lmsysorg/sglang/tags?name=nightly). Notes: -- On B300/GB300 (SM103) or CUDA 13 environment, we recommend using the nightly image at `lmsysorg/sglang:dev-cu13` or stable image at `lmsysorg/sglang:latest-cu130-runtime`. Please, do not re-install the project as editable inside the docker image, since it will override the version of libraries specified by the cu13 docker image. +- SGLang is shipped with CUDA 13 environment by default. To run SGLang on CUDA 12 environment, please use images with `-cu12` or `-cu129` suffix, such as `lmsysorg/sglang:latest-cu129` or `lmsysorg/sglang:dev-cu12`. ## Method 4: Using Kubernetes