# Single XPU (Qwen3-VL-32B) ## Run vllm-kunlun on Single XPU Setup environment using container: ```bash # !/bin/bash # rundocker.sh XPU_NUM=8 DOCKER_DEVICE_CONFIG="" if [ $XPU_NUM -gt 0 ]; then for idx in $(seq 0 $((XPU_NUM-1))); do DOCKER_DEVICE_CONFIG="${DOCKER_DEVICE_CONFIG} --device=/dev/xpu${idx}:/dev/xpu${idx}" done DOCKER_DEVICE_CONFIG="${DOCKER_DEVICE_CONFIG} --device=/dev/xpuctrl:/dev/xpuctrl" fi export build_image="xxxxxxxxxxxxxxxxx" docker run -itd ${DOCKER_DEVICE_CONFIG} \ --net=host \ --cap-add=SYS_PTRACE --security-opt seccomp=unconfined \ --tmpfs /dev/shm:rw,nosuid,nodev,exec,size=32g \ --cap-add=SYS_PTRACE \ -v /home/users/vllm-kunlun:/home/vllm-kunlun \ -v /usr/local/bin/xpu-smi:/usr/local/bin/xpu-smi \ --name "$1" \ -w /workspace \ "$build_image" /bin/bash ``` ### Offline Inference on Single XPU Start the server in a container: ```bash from vllm import LLM, SamplingParams def main(): model_path = "/models/Qwen3-VL-32B" llm_params = { "model": model_path, "tensor_parallel_size": 1, "trust_remote_code": True, "dtype": "float16", "enable_chunked_prefill": False, "distributed_executor_backend": "mp", "max_model_len": 16384, "gpu_memory_utilization": 0.9, } llm = LLM(**llm_params) messages = [ { "role": "user", "content": [ { "type": "text", "text": "tell a joke" } ] } ] sampling_params = SamplingParams( max_tokens=200, temperature=1.0, top_k=50, top_p=1.0 ) outputs = llm.chat(messages, sampling_params=sampling_params) response = outputs[0].outputs[0].text print("=" * 50) print("Input content:", messages) print("Model response:\n", response) print("=" * 50) if __name__ == "__main__": main() ``` ::::: If you run this script successfully, you can see the info shown below: ```bash ================================================== Input content: [{'role': 'user', 'content': [{'type': 'text', 'text': 'tell a joke'}]}] Model response: Why don’t skeletons fight each other? Because they don’t have the guts! 🦴😄 ================================================== ``` ### Online Serving on Single XPU Start the vLLM server on a single XPU: ```bash python -m vllm.entrypoints.openai.api_server \ --host 0.0.0.0 \ --port 9988 \ --model /models/Qwen3-VL-32B \ --gpu-memory-utilization 0.9 \ --trust-remote-code \ --max-model-len 32768 \ --tensor-parallel-size 1 \ --dtype float16 \ --max_num_seqs 128 \ --max_num_batched_tokens 32768 \ --block-size 128 \ --no-enable-prefix-caching \ --no-enable-chunked-prefill \ --distributed-executor-backend mp \ --served-model-name Qwen3-VL-32B \ --compilation-config '{"splitting_ops": ["vllm.unified_attention", "vllm.unified_attention_with_output", "vllm.unified_attention_with_output_kunlun", "vllm.mamba_mixer2", "vllm.mamba_mixer", "vllm.short_conv", "vllm.linear_attention", "vllm.plamo2_mamba_mixer", "vllm.gdn_attention", "vllm.sparse_attn_indexer"]} #Version 0.11.0 ``` If your service start successfully, you can see the info shown below: ```bash (APIServer pid=109442) INFO: Started server process [109442] (APIServer pid=109442) INFO: Waiting for application startup. (APIServer pid=109442) INFO: Application startup complete. ``` Once your server is started, you can query the model with input prompts: ```bash curl http://localhost:9988/v1/completions \ -H "Content-Type: application/json" \ -d '{ "model": "Qwen3-VL-32B", "prompt": "你好！你是谁?", "max_tokens": 100, "temperature": 0 }' ``` If you query the server successfully, you can see the info shown below (client): ```bash {"id":"cmpl-4f61fe821ff34f23a91baade5de5103e","object":"text_completion","created":1768876583,"model":"Qwen3-VL-32B","choices":[{"index":0,"text":" 你好！我是通义千问，是阿里云研发的超大规模语言模型。我能够回答问题、创作文字、编程等，还能根据你的需求进行多轮对话。有什么我可以帮你的吗？😊\n\n（温馨提示：我是一个AI助手，虽然我尽力提供准确和有用的信息，但请记得在做重要决策时，最好结合专业意见或进一步核实信息哦！）","logprobs":null,"finish_reason":"stop","stop_reason":null,"token_ids":null,"prompt_logprobs":null,"prompt_token_ids":null}],"service_tier":null,"system_fingerprint":null,"usage":{"prompt_tokens":5,"total_tokens":90,"completion_tokens":85,"prompt_tokens_details":null},"kv_transfer_params":null} ``` Logs of the vllm server: ```bash (APIServer pid=109442) INFO: 127.0.0.1:19962 - "POST /v1/completions HTTP/1.1" 200 OK (APIServer pid=109442) INFO 01-20 10:36:28 [loggers.py:127] Engine 000: Avg prompt throughput: 0.5 tokens/s, Avg generation throughput: 8.5 tokens/s, Running: 0 reqs, Waiting: 0 reqs, GPU KV cache usage: 0.0%, Prefix cache hit rate: 0.0% (APIServer pid=109442) INFO 01-20 10:36:38 [loggers.py:127] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 0.0 tokens/s, Running: 0 reqs, Waiting: 0 reqs, GPU KV cache usage: 0.0%, Prefix cache hit rate: 0.0% (APIServer pid=109442) INFO 01-20 10:43:23 [chat_utils.py:560] Detected the chat template content format to be 'openai'. You can set `--chat-template-content-format` to override this. (APIServer pid=109442) INFO 01-20 10:43:28 [loggers.py:127] Engine 000: Avg prompt throughput: 9.0 tokens/s, Avg generation throughput: 6.9 tokens/s, Running: 1 reqs, Waiting: 0 reqs, GPU KV cache usage: 0.5%, Prefix cache hit rate: 0.0% ``` Input an image for testing.Here,a python script is used: ```python import requests import base64 API_URL = "http://localhost:9988/v1/chat/completions" MODEL_NAME = "Qwen3-VL-32B" IMAGE_PATH = "/images.jpeg" def encode_image(image_path): with open(image_path, "rb") as image_file: return base64.b64encode(image_file.read()).decode('utf-8') base64_image = encode_image(IMAGE_PATH) payload = { "model": MODEL_NAME, "messages": [ { "role": "user", "content": [ { "type": "text", "text": "你好！请描述一下这张图片。" }, { "type": "image_url", "image_url": { "url": f"data:image/jpeg;base64,{base64_image}" } } ] } ], "max_tokens": 300, "temperature": 0.1 } response = requests.post(API_URL, json=payload) print(response.json()) ``` If you query the server successfully, you can see the info shown below (client): ```bash {'id': 'chatcmpl-4b42fe46f2c84991b0af5d5e1ffad9ba', 'object': 'chat.completion', 'created': 1768877003, 'model': 'Qwen3-VL-32B', 'choices': [{'index': 0, 'message': {'role': 'assistant', 'content': '你好！这张图片展示的是“Hugging Face”的标志。\n\n图片左侧是一个黄色的圆形表情符号（emoji），它有着圆圆的眼睛、张开的嘴巴露出微笑，双手合拢在脸颊两侧，做出一个拥抱或欢迎的姿态，整体传达出友好、温暖和亲切的感觉。\n\n图片右侧是黑色的英文文字“Hugging Face”，字体简洁现代，与左侧的表情符号相呼应。\n\n整个标志设计简洁明了，背景为纯白色，突出了标志本身。这个标志属于Hugging Face公司，它是一家知名的开源人工智能公司，尤其在自然语言处理（NLP）领域以提供预训练模型（如Transformers库）和模型托管平台而闻名。\n\n整体来看，这个标志通过可爱的表情符号和直白的文字，成功传达了公司“拥抱”技术、开放共享、友好的品牌理念。', 'refusal': None, 'annotations': None, 'audio': None, 'function_call': None, 'tool_calls': [], 'reasoning_content': None}, 'logprobs': None, 'finish_reason': 'stop', 'stop_reason': None, 'token_ids': None}], 'service_tier': None, 'system_fingerprint': None, 'usage': {'prompt_tokens': 90, 'total_tokens': 266, 'completion_tokens': 176, 'prompt_tokens_details': None}, 'prompt_logprobs': None, 'prompt_token_ids': None, 'kv_transfer_params': None} ``` Logs of the vllm server: ```bash (APIServer pid=109442) INFO: 127.0.0.1:26854 - "POST /v1/chat/completions HTTP/1.1" 200 OK (APIServer pid=109442) INFO 01-20 10:43:38 [loggers.py:127] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 10.7 tokens/s, Running: 0 reqs, Waiting: 0 reqs, GPU KV cache usage: 0.0%, Prefix cache hit rate: 0.0% (APIServer pid=109442) INFO 01-20 10:43:48 [loggers.py:127] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 0.0 tokens/s, Running: 0 reqs, Waiting: 0 reqs, GPU KV cache usage: 0.0%, Prefix cache hit rate: 0.0% ```