Quick Start

Environment Setup

All images are stored here. The docker startup command below uses the dev image as an example.

NPU

Below are our pre-built dev image.

# A2 x86
docker pull quay.io/jd_xllm/xllm-ai:xllm-dev-a2-x86-20260306
# A2 arm
docker pull quay.io/jd_xllm/xllm-ai:xllm-dev-a2-arm-20260306
# A3 arm
docker pull quay.io/jd_xllm/xllm-ai:xllm-dev-a3-arm-20260306

Container startup command:

docker run -it \
--ipc=host \
-u 0 \
--name xllm-npu \
--privileged \
--network=host \
--device=/dev/davinci0 \
--device=/dev/davinci_manager \
--device=/dev/devmm_svm \
--device=/dev/hisi_hdc \
-v /usr/local/Ascend/driver:/usr/local/Ascend/driver \
-v /usr/local/Ascend/add-ons/:/usr/local/Ascend/add-ons/ \
-v /usr/local/sbin/npu-smi:/usr/local/sbin/npu-smi \
-v /usr/local/sbin/:/usr/local/sbin/ \
-v /var/log/npu/conf/slog/slog.conf:/var/log/npu/conf/slog/slog.conf \
-v /var/log/npu/slog/:/var/log/npu/slog \
-v /var/log/npu/profiling/:/var/log/npu/profiling \
-v /var/log/npu/dump/:/var/log/npu/dump \
-v $HOME:$HOME \
-w $HOME \
<docker_image_name> \
/bin/bash

NVIDIA GPU

We provide a Dockerfile for NVIDIA GPU usage, which can be used to build custom image. Of course, you can also use dev image we built based on the default Dockerfile:

docker pull quay.io/jd_xllm/xllm-ai:xllm-dev-cuda-x86

Container startup command:

sudo docker run -it \
--privileged \
--shm-size '128gb' \
--ipc=host \
--net=host \
--pid=host \
--name=xllm-cuda \
-v $HOME:$HOME \
-w $HOME \
<docker_image_name> \
/bin/bash

MLU

We cannot provide MLU image. If you already have the dev image, you can start the container with the following command:

sudo docker run -it \
--privileged \
--shm-size '128gb' \
--ipc=host \
--net=host \
--pid=host \
--name xllm-mlu \
-v $HOME:$HOME \
-w $HOME \
<docker_image_name> \
/bin/bash

Build xllm

If you download a release image, i.e., an image with a version number in the tag, you can skip this step because the release image comes with a pre-compiled xllm binary, located at /usr/local/bin/xllm.

Download xllm and dependencies:

git clone https://github.com/jd-opensource/xllm
cd xllm

# Install pre-commit for the first time
pip install pre-commit
pre-commit install

git submodule update --init --recursive

The compiled binary file is located at /path/to/xllm/build/xllm/core/server/xllm. In a new image, the first compilation of xllm takes a long time because all dependencies in vcpkg need to be compiled, but subsequent compilations will be much faster.

python setup.py build

Launch xllm

Please refer to How to Launch xllm.