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Update docs (#13391)

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Co-authored-by: sglang-bot <sglangbot@gmail.com>
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Lianmin Zheng
2025-11-16 19:36:33 -08:00
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co-authored by sglang-bot
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docs/get_started/install.md
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You can install SGLang using one of the methods below.
This page primarily applies to common NVIDIA GPU platforms.
For other or newer platforms, please refer to the dedicated pages for [AMD GPUs](../platforms/amd_gpu.md), [Intel Xeon CPUs](../platforms/cpu_server.md), [TPU](../platforms/tpu.md), [NVIDIA DGX Spark](https://lmsys.org/blog/2025-10-13-nvidia-dgx-spark/), [NVIDIA Jetson](../platforms/nvidia_jetson.md), [Ascend NPUs](../platforms/ascend_npu.md).
For other or newer platforms, please refer to the dedicated pages for [AMD GPUs](../platforms/amd_gpu.md), [Intel Xeon CPUs](../platforms/cpu_server.md), [TPU](../platforms/tpu.md), [NVIDIA DGX Spark](https://lmsys.org/blog/2025-11-03-gpt-oss-on-nvidia-dgx-spark/), [NVIDIA Jetson](../platforms/nvidia_jetson.md), [Ascend NPUs](../platforms/ascend_npu.md), and [Intel XPU](../platforms/xpu.md).
## Method 1: With pip or uv
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**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`.
- If you want to develop SGLang, you can try the dev docker image. 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