单个NPU（Qwen3 8B）

单个NPU（Qwen3 8B）#

在单个 NPU 上运行 vllm-ascend#

在单个NPU上进行离线推理#

运行 docker 容器：

# Update the vllm-ascend image
export IMAGE=quay.io/ascend/vllm-ascend:v0.11.0rc0
docker run --rm \
--name vllm-ascend \
--device /dev/davinci0 \
--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

Set up environment variables:

# Load model from ModelScope to speed up download
export VLLM_USE_MODELSCOPE=True

# Set `max_split_size_mb` to reduce memory fragmentation and avoid out of memory
export PYTORCH_NPU_ALLOC_CONF=max_split_size_mb:256

备注

max_split_size_mb 防止本地分配器拆分超过此大小（以 MB 为单位）的内存块。这可以减少内存碎片，并且可能让一些边缘情况下的工作负载顺利完成而不会耗尽内存。你可以在这里找到更多详细信息。

运行以下脚本以在单个 NPU 上执行离线推理：

import os
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)
llm = LLM(
        model="Qwen/Qwen3-8B",
        max_model_len=26240
)

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}")

import os
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)
llm = LLM(
        model="Qwen/Qwen3-8B",
        max_model_len=26240,
        enforce_eager=True
)

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}")

如果你成功运行此脚本，你可以看到如下所示的信息：

Prompt: 'Hello, my name is', Generated text: ' Daniel and I am an 8th grade student at York Middle School. I'
Prompt: 'The future of AI is', Generated text: ' following you. As the technology advances, a new report from the Institute for the'

单个 NPU 上的在线服务#

运行 docker 容器，在单个 NPU 上启动 vLLM 服务器：

# Update the vllm-ascend image
export IMAGE=quay.io/ascend/vllm-ascend:v0.11.0rc0
docker run --rm \
--name vllm-ascend \
--device /dev/davinci0 \
--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 \
-e VLLM_USE_MODELSCOPE=True \
-e PYTORCH_NPU_ALLOC_CONF=max_split_size_mb:256 \
-it $IMAGE \
vllm serve Qwen/Qwen3-8B --max_model_len 26240

export IMAGE=quay.io/ascend/vllm-ascend:v0.11.0rc0
docker run --rm \
--name vllm-ascend \
--device /dev/davinci0 \
--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 \
-e VLLM_USE_MODELSCOPE=True \
-e PYTORCH_NPU_ALLOC_CONF=max_split_size_mb:256 \
-it $IMAGE \
vllm serve Qwen/Qwen3-8B --max_model_len 26240 --enforce-eager

备注

Add --max_model_len option to avoid ValueError that the Qwen2.5-7B model's max seq len (32768) is larger than the maximum number of tokens that can be stored in KV cache (26240). This will differ with different NPU series based on the HBM size. Please modify the value according to a suitable value for your NPU series.

如果你的服务启动成功，你会看到如下所示的信息：

INFO:     Started server process [6873]
INFO:     Waiting for application startup.
INFO:     Application startup complete.

一旦你的服务器启动，你可以通过输入提示词来查询模型：

curl http://localhost:8000/v1/completions \
    -H "Content-Type: application/json" \
    -d '{
        "model": "Qwen/Qwen3-8B",
        "prompt": "The future of AI is",
        "max_tokens": 7,
        "temperature": 0
    }'

如果你成功查询了服务器，你可以看到如下所示的信息（客户端）：

{"id":"cmpl-b25a59a2f985459781ce7098aeddfda7","object":"text_completion","created":1739523925,"model":"Qwen/Qwen3-8B","choices":[{"index":0,"text":" here. It’s not just a","logprobs":null,"finish_reason":"length","stop_reason":null,"prompt_logprobs":null}],"usage":{"prompt_tokens":5,"total_tokens":12,"completion_tokens":7,"prompt_tokens_details":null}}

vllm 服务器的日志：

INFO:     172.17.0.1:49518 - "POST /v1/completions HTTP/1.1" 200 OK
INFO 02-13 08:34:35 logger.py:39] Received request cmpl-574f00e342904692a73fb6c1c986c521-0: prompt: 'San Francisco is a', params: SamplingParams(n=1, presence_penalty=0.0, frequency_penalty=0.0, repetition_penalty=1.0, temperature=0.0, top_p=1.0, top_k=-1, min_p=0.0, seed=None, stop=[], stop_token_ids=[], bad_words=[], include_stop_str_in_output=False, ignore_eos=False, max_tokens=7, min_tokens=0, logprobs=None, prompt_logprobs=None, skip_special_tokens=True, spaces_between_special_tokens=True, truncate_prompt_tokens=None, guided_decoding=None), prompt_token_ids: [23729, 12879, 374, 264], lora_request: None, prompt_adapter_request: None.
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