docker: add CUDA13 support in dockerfile and update GDRCopy/NVSHMEM for blackwell support (#11517)

Co-authored-by: Baizhou Zhang <sobereddiezhang@gmail.com>

docs/get_started/install.md

@@ -12,10 +12,11 @@ It is recommended to use uv for faster installation:
 ```bash
 pip install --upgrade pip
 pip install uv
-uv pip install sglang --prerelease=allow
+uv pip install "sglang" --prerelease=allow
 ```
 
 **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.
@@ -33,6 +34,7 @@ pip install -e "python"
 ```
 
 **Quick fixes to common problems**
+
 - If you want to develop SGLang, it is recommended to use docker. Please refer to [setup docker container](../developer_guide/development_guide_using_docker.md#setup-docker-container). The docker image is `lmsysorg/sglang:dev`.
 
 ## Method 3: Using docker