diff --git a/docs/cookbook/autoregressive/Moonshotai/Kimi-K3.mdx b/docs/cookbook/autoregressive/Moonshotai/Kimi-K3.mdx index b255fe19a..6297569bf 100644 --- a/docs/cookbook/autoregressive/Moonshotai/Kimi-K3.mdx +++ b/docs/cookbook/autoregressive/Moonshotai/Kimi-K3.mdx @@ -10,28 +10,34 @@ tag: NEW -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. + + +```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. + + + ```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. -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). - 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.