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[Docs] Update Kimi-K3 installation options (#34886)

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Baizhou Zhang
2026-08-14 16:19:23 -07:00
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docs/cookbook/autoregressive/Moonshotai/Kimi-K3.mdx
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@@ -10,28 +10,34 @@ tag: NEW
<Accordion title="Install SGLang">
For all methods and hardware platforms, see the [official SGLang installation guide](../../../docs/get-started/install).
For all methods and hardware platforms, see the [official SGLang installation guide](../../../docs/get-started/install). The two paths below match the **Python / Docker** toggle in the command panel.
<Tabs>
<Tab title="Python (pip / uv)">
```bash Command
pip install --upgrade pip
pip install uv
uv pip install sglang
```
Then run the **Python** output of the command panel below in that environment.
</Tab>
<Tab title="Docker">
```bash Command
docker pull lmsysorg/sglang:kimi-k3 # CUDA13
docker pull lmsysorg/sglang:kimi-k3-cu12 # CUDA12
docker pull lmsysorg/sglang-rocm:v0.5.16-rocm720-mi35x-20260805 # ROCM daily image
docker pull lmsysorg/sglang:latest
```
These tags publish with the public K3 launch; until then, build from the Dockerfiles linked below.
For how to launch the image, see [Install → Method 3: Using Docker](../../../docs/get-started/install#method-3-using-docker). Substitute the inner `sglang serve ...` with what the command generator below produces.
</Tab>
</Tabs>
If you do not want to use a Docker image, reproduce the dependency installation steps from the [CUDA 13 Dockerfile](https://github.com/sgl-project/sglang/blob/kimi-k3/docker/kimi_k3/kimi_k3_cu13.Dockerfile) or [CUDA 12 Dockerfile](https://github.com/sgl-project/sglang/blob/kimi-k3/docker/kimi_k3/kimi_k3_cu12.Dockerfile).
</Accordion>
Pick your hardware, then the deployment shape and operating point. Node count follows the hardware recipe (B200 2×8, GB200 4×4, H100 4×8, B300 1×8, H200 2×8 — 4×8 on Unified High-Throughput, GB300 2×4, MI350X/MI355X 1×8), so it is not a separate choice.