[Doc] Fix instruction on Cuda 13 environments (#24516)

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-<your-cuda-version>` 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