@@ -7,7 +7,7 @@ Special thanks to Meituan's Search & Recommend Platform Team and Baseten's Model
## Hardware Recommendation
- 8 x NVIDIA H200 GPUs
If you do not have GPUs with large enough memory, please try multi-node tensor parallelism ([help 1](https://github.com/sgl-project/sglang/blob/637de9e8ce91fd3e92755eb2a842860925954ab1/docs/backend/backend.md?plain=1#L88-L95)[help 2](https://github.com/sgl-project/sglang/blob/637de9e8ce91fd3e92755eb2a842860925954ab1/docs/backend/backend.md?plain=1#L152-L168)).
If you do not have GPUs with large enough memory, please try multi-node tensor parallelism ([help 1](https://github.com/sgl-project/sglang/blob/637de9e8ce91fd3e92755eb2a842860925954ab1/docs/backend/backend.md?plain=1#L88-L95)[help 2](https://github.com/sgl-project/sglang/blob/637de9e8ce91fd3e92755eb2a842860925954ab1/docs/backend/backend.md?plain=1#L152-L168)). Here is an example serving with [2 H20 node](https://github.com/sgl-project/sglang/tree/main/benchmark/deepseek_v3#example-serving-with-2-h208)
## Installation & Launch
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@@ -15,6 +15,11 @@ If you encounter errors when starting the server, ensure the weights have finish
### Using Docker (Recommended)
```bash
# Pull latest image
# https://hub.docker.com/r/lmsysorg/sglang/tags
docker pull lmsysorg/sglang:latest
# Launch
docker run --gpus all --shm-size 32g -p 30000:30000 -v ~/.cache/huggingface:/root/.cache/huggingface --ipc=host lmsysorg/sglang:latest \
@@ -14,6 +14,8 @@ tar xf vscode_cli_alpine_x64_cli.tar.gz
## Setup Docker Container
The following startup command is an example for internal development by the SGLang team. You can **modify or add directory mappings as needed**, especially for model weight downloads, to prevent repeated downloads by different Docker containers.