From 088758c1c16bd52bb09669d6891496283ec2c963 Mon Sep 17 00:00:00 2001 From: Mohammad Miadh Angkad Date: Sun, 18 Jan 2026 14:57:01 +0800 Subject: [PATCH] [Tiny] Improve docs (#17264) --- docs/get_started/install.md | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/docs/get_started/install.md b/docs/get_started/install.md index 123b12904..e9686994f 100644 --- a/docs/get_started/install.md +++ b/docs/get_started/install.md @@ -21,14 +21,14 @@ For example, on GB200, you will need to do the following. Otherwise, it will ins uv pip install "sglang" --extra-index-url https://download.pytorch.org/whl/cu129 ``` -For CUDA 13, Docker is recommended (see Method 3 note on B300/CUDA 13). If you do not have Docker access, installing the matching `sgl_kernel` wheel from https://github.com/sgl-project/whl/releases after installing SGLang also works. Replace `X.Y.Z` with the `sgl_kernel` version required by your SGLang install. Examples: +For CUDA 13, Docker is recommended (see Method 3 note on B300/GB300/CUDA 13). If you do not have Docker access, installing the matching `sgl_kernel` wheel from [the sgl-project whl releases](https://github.com/sgl-project/whl/releases) after installing SGLang also works. 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 -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" # x86_64 -``` +# 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" -```bash -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" # aarch64 +# 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** @@ -84,7 +84,7 @@ docker run --gpus all \ You can also find the nightly docker images [here](https://hub.docker.com/r/lmsysorg/sglang/tags?name=nightly). -On B300 (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`. +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.