# Quickstart ## Introduction This section guides you through container-based environment setup and large model inference, using the Qwen3-0.6B offline single-GPU inference script as an example. - For details on using different models, see the corresponding model tutorial in the "Model Tutorials" directory, for example, [Qwen3-30B-A3B](../../docs/source/tutorials/models/Qwen3-30B-A3B.md). - For details on using different functions, see the corresponding function tutorial in the "Function Tutorials" directory, for example, [Prefill-Decode Disaggregation (Deepseek)](../../docs/source/tutorials/features/pd_disaggregation_mooncake_multi_node.md). ## Prerequisites ### Supported Devices - Atlas A2 training series (Atlas 800T A2, Atlas 900 A2 PoD, Atlas 200T A2 Box16, Atlas 300T A2) - Atlas 800I A2 inference series (Atlas 800I A2) - Atlas A3 training series (Atlas 800T A3, Atlas 900 A3 SuperPoD, Atlas 9000 A3 SuperPoD) - Atlas 800I A3 inference series (Atlas 800I A3) - [Experimental] Atlas 300I inference series (Atlas 300I Duo) ## Requirements - OS: Linux - Python: >= 3.10, < 3.13 - Hardware with Ascend NPUs. It's usually the Atlas 800 A2 series. - Software: | Software | Supported version | Note | |---------------|----------------------------------|-------------------------------------------| | Ascend HDK | Refer to the documentation [CANN 9.0.0](https://www.hiascend.com/document/detail/zh/canncommercial/900/releasenote/releasenote_0000.html) | Required for CANN | | CANN | == 9.0.0 | Required for vllm-ascend and torch-npu | | torch-npu | == 2.10.0 | Required for vllm-ascend, No need to install manually, it will be auto installed in below steps | | torch | == 2.10.0 | Required for torch-npu and vllm, No need to install manually, it will be auto installed in below steps | | NNAL | == 9.0.0 | Required for libatb.so, enables advanced tensor operations | ## Setup environment using container Before using containers, make sure Docker is installed on your system. If Docker is not installed, please refer to the [Docker installation guide](https://docs.docker.com/get-docker/) for installation instructions. :::::{tab-set} ::::{tab-item} Ubuntu ```{code-block} bash :substitutions: # Update DEVICE according to your device (/dev/davinci[0-7]) export DEVICE=/dev/davinci0 # Update the vllm-ascend image # Atlas A2: # export IMAGE=quay.io/ascend/vllm-ascend:|vllm_ascend_version| # Atlas A3: # export IMAGE=quay.io/ascend/vllm-ascend:|vllm_ascend_version|-a3 export IMAGE=quay.io/ascend/vllm-ascend:|vllm_ascend_version| docker run --rm \ --name vllm-ascend \ --shm-size=1g \ --device $DEVICE \ --device /dev/davinci_manager \ --device /dev/devmm_svm \ --device /dev/hisi_hdc \ -v /usr/local/dcmi:/usr/local/dcmi \ -v /usr/local/bin/npu-smi:/usr/local/bin/npu-smi \ -v /usr/local/Ascend/driver/lib64/:/usr/local/Ascend/driver/lib64/ \ -v /usr/local/Ascend/driver/version.info:/usr/local/Ascend/driver/version.info \ -v /etc/ascend_install.info:/etc/ascend_install.info \ -v /root/.cache:/root/.cache \ -p 8000:8000 \ -it $IMAGE bash # Install curl apt-get update -y && apt-get install -y curl ``` :::: ::::{tab-item} openEuler ```{code-block} bash :substitutions: # Update DEVICE according to your device (/dev/davinci[0-7]) export DEVICE=/dev/davinci0 # Update the vllm-ascend image # Atlas A2: # export IMAGE=quay.io/ascend/vllm-ascend:|vllm_ascend_version|-openeuler # Atlas A3: # export IMAGE=quay.io/ascend/vllm-ascend:|vllm_ascend_version|-a3-openeuler export IMAGE=quay.io/ascend/vllm-ascend:|vllm_ascend_version|-openeuler docker run --rm \ --name vllm-ascend \ --shm-size=1g \ --device $DEVICE \ --device /dev/davinci_manager \ --device /dev/devmm_svm \ --device /dev/hisi_hdc \ -v /usr/local/dcmi:/usr/local/dcmi \ -v /usr/local/bin/npu-smi:/usr/local/bin/npu-smi \ -v /usr/local/Ascend/driver/lib64/:/usr/local/Ascend/driver/lib64/ \ -v /usr/local/Ascend/driver/version.info:/usr/local/Ascend/driver/version.info \ -v /etc/ascend_install.info:/etc/ascend_install.info \ -v /root/.cache:/root/.cache \ -p 8000:8000 \ -it $IMAGE bash # Install curl yum update -y && yum install -y curl ``` :::: ::::: The default workdir is `/workspace`, vLLM and vLLM Ascend code are placed in `/vllm-workspace` and installed in [development mode](https://setuptools.pypa.io/en/latest/userguide/development_mode.html) (`pip install -e`) to help developers make changes effective immediately without requiring a new installation. ## Usage You can use ModelScope mirror to speed up download: ```bash export VLLM_USE_MODELSCOPE=True ``` There are two ways to start vLLM on Ascend NPU: :::::{tab-set} ::::{tab-item} Offline Batched Inference With vLLM installed, you can start generating texts for list of input prompts (i.e. offline batch inference). Create and run a simple inference test. The `example.py` can be like: ```python from vllm import LLM, SamplingParams prompts = [ "Hello, my name is", "The future of AI is", ] sampling_params = SamplingParams(temperature=0.8, top_p=0.95) # The first run will take about 3-5 mins (10 MB/s) to download models llm = LLM(model="Qwen/Qwen3-0.6B") outputs = llm.generate(prompts, sampling_params) for output in outputs: prompt = output.prompt generated_text = output.outputs[0].text print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}") ``` Then run: ```bash python example.py ``` If you encounter a connection error with Hugging Face (e.g., `We couldn't connect to 'https://huggingface.co' to load the files, and couldn't find them in the cached files.`), run the following commands to use ModelScope as an alternative: ```bash export VLLM_USE_MODELSCOPE=True pip install modelscope python example.py ``` This section shows ascend platform is successfully detected in vllm: ```bash INFO 05-27 11:40:38 [__init__.py:44] Available plugins for group vllm.platform_plugins: INFO 05-27 11:40:38 [__init__.py:46] - ascend -> vllm_ascend:register INFO 05-27 11:40:38 [__init__.py:49] All plugins in this group will be loaded. Set `VLLM_PLUGINS` to control which plugins to load. INFO 05-27 11:40:38 [__init__.py:238] Platform plugin ascend is activated ``` This section shows the final output: ```bash Prompt: 'Hello, my name is', Generated text: ' Lucy and I am an 8 year old who loves to draw and write stories' Prompt: 'The president of the United States is', Generated text: " a key leader in the federal government, and the president's role in the executive" Prompt: 'The capital of France is', Generated text: ' a city. What is the capital of France? The capital of France is Paris' Prompt: 'The future of AI is', Generated text: ' a topic that is being discussed in various contexts. In the business world, AI' ``` This section shows process exits after offline inference, and is does not affect actual inference: ```bash (EngineCore pid=970) INFO 05-12 11:36:00 [core.py:1201] Shutdown initiated (timeout=0) (EngineCore pid=970) INFO 05-12 11:36:00 [core.py:1224] Shutdown complete ERROR 05-12 11:36:01 [core_client.py:704] Engine core proc EngineCore died unexpectedly, shutting down client. sys:1: DeprecationWarning: builtin type swigvarlink has no __module__ attribute ``` :::: ::::{tab-item} OpenAI Completions API vLLM can also be deployed as a server that implements the OpenAI API protocol. Run the following command to start the vLLM server with the [Qwen/Qwen3-0.6B](https://huggingface.co/Qwen/Qwen3-0.6B) model: ```bash # Deploy vLLM server (The first run will take about 3-5 mins (10 MB/s) to download models) vllm serve Qwen/Qwen3-0.6B & ``` If you see a log as below: ```shell INFO: Started server process [3594] INFO: Waiting for application startup. INFO: Application startup complete. INFO: Uvicorn running on http://0.0.0.0:8000 (Press CTRL+C to quit) ``` Congratulations, you have successfully started the vLLM server! You can query the list of models: ```bash curl http://localhost:8000/v1/models | python3 -m json.tool ``` You can also query the model with input prompts: ```bash curl http://localhost:8000/v1/completions \ -H "Content-Type: application/json" \ -d '{ "model": "Qwen/Qwen3-0.6B", "prompt": "Beijing is a", "max_completion_tokens": 5, "temperature": 0 }' | python3 -m json.tool ``` vLLM is serving as a background process, you can use `kill -2 $VLLM_PID` to stop the background process gracefully, which is similar to `Ctrl-C` for stopping the foreground vLLM process: ```bash VLLM_PID=$(pgrep -f "vllm serve") kill -2 "$VLLM_PID" ``` The output is as below: ```shell INFO: Shutting down FastAPI HTTP server. INFO: Shutting down INFO: Waiting for application shutdown. INFO: Application shutdown complete. ``` Finally, you can exit the container by using `ctrl-D`. :::: :::::