---
title: "AMD GPUs"
---
This document describes how run SGLang on AMD GPUs. If you encounter issues or have questions, please [open an issue](https://github.com/sgl-project/sglang/issues).
## System Configuration
When using AMD GPUs (such as MI300X), certain system-level optimizations help ensure stable performance. Here we take MI300X as an example. AMD provides official documentation for MI300X optimization and system tuning:
* [AMD MI300X Tuning Guides](https://rocm.docs.amd.com/en/latest/how-to/tuning-guides/mi300x/index.html)
* [LLM inference performance validation on AMD Instinct MI300X](https://rocm.docs.amd.com/en/latest/how-to/rocm-for-ai/inference/vllm-benchmark.html)
* [AMD Instinct MI300X System Optimization](https://rocm.docs.amd.com/en/latest/how-to/system-optimization/mi300x.html)
* [AMD Instinct MI300X Workload Optimization](https://rocm.docs.amd.com/en/latest/how-to/rocm-for-ai/inference-optimization/workload.html)
* [Supercharge DeepSeek-R1 Inference on AMD Instinct MI300X](https://rocm.blogs.amd.com/artificial-intelligence/DeepSeekR1-Part2/README.html)
We strongly recommend reading these docs and guides entirely to fully utilize your system.
Below are a few key settings to confirm or enable for SGLang:
### Update GRUB Settings
In `/etc/default/grub`, append the following to `GRUB_CMDLINE_LINUX`:
```text GRUB Configuration
pci=realloc=off iommu=pt
```
Afterward, run `sudo update-grub` (or your distro's equivalent) and reboot.
### Disable NUMA Auto-Balancing
```bash Disable NUMA
sudo sh -c 'echo 0 > /proc/sys/kernel/numa_balancing'
```
You can automate or verify this change using [this helpful script](https://github.com/ROCm/triton/blob/rocm_env/scripts/amd/env_check.sh).
Again, please go through the entire documentation to confirm your system is using the recommended configuration.
## Install SGLang
The docker images are available on Docker Hub at [lmsysorg/sglang](https://hub.docker.com/r/lmsysorg/sglang/tags), built from [rocm.Dockerfile](https://github.com/sgl-project/sglang/tree/main/docker).
1. **Build the docker image**
If you use pre-built images, you can skip this step and replace `sglang_image` with the pre-built image names in the steps below.
```bash Build Image
docker build -t sglang_image -f rocm.Dockerfile .
```
2. **Create a convenient alias**
```bash Create Alias
alias drun='docker run -it --rm --network=host --privileged --device=/dev/kfd --device=/dev/dri \
--ipc=host --shm-size 16G --group-add video --cap-add=SYS_PTRACE \
--security-opt seccomp=unconfined \
-v $HOME/dockerx:/dockerx \
-v /data:/data'
```
If you are using RDMA, please note that:
* `--network host` and `--privileged` are required by RDMA. If you don't need RDMA, you can remove them.
* You may need to set `NCCL_IB_GID_INDEX` if you are using RoCE, for example: `export NCCL_IB_GID_INDEX=3`.
3. **Launch the server**
Replace `` below with your [huggingface hub token](https://huggingface.co/docs/hub/en/security-tokens).
```bash Launch Server
drun -p 30000:30000 \
-v ~/.cache/huggingface:/root/.cache/huggingface \
--env "HF_TOKEN=" \
sglang_image \
python3 -m sglang.launch_server \
--model-path NousResearch/Meta-Llama-3.1-8B \
--host 0.0.0.0 \
--port 30000
```
4. **Verify the installation**
You can run a benchmark in another terminal or refer to [other docs](../basic_usage/openai_api_completions) to send requests to the engine.
```bash Run Benchmark
drun sglang_image \
python3 -m sglang.bench_serving \
--backend sglang \
--dataset-name random \
--num-prompts 4000 \
--random-input 128 \
--random-output 128
```
With your AMD system properly configured and SGLang installed, you can now fully leverage AMD hardware to power SGLang's machine learning capabilities.
1. **Clone the repository**
Clone the SGLang repository.
```bash
git clone https://github.com/sgl-project/sglang.git
cd sglang
```
2. **Compile sgl-kernel**
Upgrade pip and compile the sgl-kernel for ROCm support.
```bash
pip install --upgrade pip
cd sgl-kernel
python setup_rocm.py install
```
3. **Install sglang package**
Install the SGLang Python package with HIP and diffusion support.
```bash
cd ..
rm -rf python/pyproject.toml && mv python/pyproject_other.toml python/pyproject.toml
pip install -e "python[all_hip]"
```
## Examples
### Running DeepSeek-V3
The only difference when running DeepSeek-V3 is in how you start the server.
```bash DeepSeek-V3
drun -p 30000:30000 \
-v ~/.cache/huggingface:/root/.cache/huggingface \
--ipc=host \
--env "HF_TOKEN=" \
sglang_image \
python3 -m sglang.launch_server \
--model-path deepseek-ai/DeepSeek-V3 \
--tp 8 \
--trust-remote-code \
--host 0.0.0.0 \
--port 30000
```
[Running DeepSeek-R1 on a single NDv5 MI300X VM](https://techcommunity.microsoft.com/blog/azurehighperformancecomputingblog/running-deepseek-r1-on-a-single-ndv5-mi300x-vm/4372726) could also be a good reference.
### Running Llama3.1
Running Llama3.1 is nearly identical to running DeepSeek-V3. The only difference is in the model specified when starting the server.
```bash Llama3.1
drun -p 30000:30000 \
-v ~/.cache/huggingface:/root/.cache/huggingface \
--ipc=host \
--env "HF_TOKEN=" \
sglang_image \
python3 -m sglang.launch_server \
--model-path meta-llama/Meta-Llama-3.1-8B-Instruct \
--tp 8 \
--trust-remote-code \
--host 0.0.0.0 \
--port 30000
```
### Warmup Step
When the server displays `The server is fired up and ready to roll!`, it means the startup is successful.