{"id":83361,"date":"2026-07-25T20:50:47","date_gmt":"2026-07-25T12:50:47","guid":{"rendered":"https:\/\/www.wsisp.com\/helps\/83361.html"},"modified":"2026-07-25T20:50:47","modified_gmt":"2026-07-25T12:50:47","slug":"qwen3-5-35b-a3b-awq-4bit%e9%83%a8%e7%bd%b2%e6%95%99%e7%a8%8b%ef%bc%9aarm%e6%9e%b6%e6%9e%84%e6%9c%8d%e5%8a%a1%e5%99%a8%ef%bc%88%e5%a6%82%e9%b2%b2%e9%b9%8f920%ef%bc%89%e5%85%bc%e5%ae%b9%e6%80%a7","status":"publish","type":"post","link":"https:\/\/www.wsisp.com\/helps\/83361.html","title":{"rendered":"Qwen3.5-35B-A3B-AWQ-4bit\u90e8\u7f72\u6559\u7a0b\uff1aARM\u67b6\u6784\u670d\u52a1\u5668\uff08\u5982\u9cb2\u9e4f920\uff09\u517c\u5bb9\u6027\u9a8c\u8bc1\u4e0e\u6027\u80fd\u8c03\u4f18"},"content":{"rendered":"

Qwen3.5-35B-A3B-AWQ-4bit\u90e8\u7f72\u6559\u7a0b：ARM\u67b6\u6784\u670d\u52a1\u5668（\u5982\u9cb2\u9e4f920）\u517c\u5bb9\u6027\u9a8c\u8bc1\u4e0e\u6027\u80fd\u8c03\u4f18<\/h2>\n

1. \u6a21\u578b\u6982\u8ff0<\/h3>\n

Qwen3.5-35B-A3B-AWQ-4bit\u662f\u4e00\u6b3e\u9762\u5411\u89c6\u89c9\u591a\u6a21\u6001\u7406\u89e3\u7684\u91cf\u5316\u6a21\u578b，\u7279\u522b\u9488\u5bf9ARM\u67b6\u6784\u670d\u52a1\u5668\u8fdb\u884c\u4e86\u4f18\u5316\u3002\u8fd9\u4e2a\u6a21\u578b\u5c06\u539f\u59cb35B\u53c2\u6570\u7684\u5927\u6a21\u578b\u901a\u8fc7AWQ(Activation-aware Weight Quantization)\u65b9\u6cd5\u538b\u7f29\u52304bit\u7cbe\u5ea6，\u540c\u65f6\u4fdd\u6301\u4e86\u51fa\u8272\u7684\u591a\u6a21\u6001\u7406\u89e3\u80fd\u529b\u3002<\/p>\n

1.1 \u6838\u5fc3\u80fd\u529b<\/h4>\n\n\u80fd\u529b\u6280\u672f\u7279\u70b9\u9002\u7528\u573a\u666f<\/tr>\n\n\n\n\n\n
\u56fe\u7247\u7406\u89e3<\/td>\n\u57fa\u4e8eCLIP\u89c6\u89c9\u7f16\u7801\u5668<\/td>\n\u56fe\u50cf\u5185\u5bb9\u5206\u6790<\/td>\n<\/tr>\n
\u56fe\u6587\u95ee\u7b54<\/td>\n\u591a\u6a21\u6001Transformer\u67b6\u6784<\/td>\n\u89c6\u89c9\u95ee\u7b54\u7cfb\u7edf<\/td>\n<\/tr>\n
\u89c6\u89c9\u63cf\u8ff0<\/td>\n\u7ed3\u5408\u8bed\u8a00\u751f\u6210\u80fd\u529b<\/td>\n\u81ea\u52a8\u56fe\u7247\u6807\u6ce8<\/td>\n<\/tr>\n
\u4e2d\u6587\u8f93\u51fa<\/td>\n\u4f18\u5316\u4e2d\u6587tokenizer<\/td>\n\u4e2d\u6587\u5185\u5bb9\u751f\u6210<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

2. ARM\u670d\u52a1\u5668\u90e8\u7f72\u51c6\u5907<\/h3>\n

2.1 \u786c\u4ef6\u8981\u6c42<\/h4>\n
    \n
  • CPU\u67b6\u6784：ARMv8.2\u53ca\u4ee5\u4e0a（\u5982\u9cb2\u9e4f920）<\/li>\n
  • GPU\u914d\u7f6e：\u81f3\u5c112\u5f2024GB\u663e\u5b58\u663e\u5361（\u63a8\u8350NVIDIA Tesla T4\u6216\u540c\u7b49）<\/li>\n
  • \u5185\u5b58：\u5efa\u8bae64GB\u4ee5\u4e0a<\/li>\n
  • \u5b58\u50a8：50GB\u53ef\u7528\u7a7a\u95f4（\u6a21\u578b\u6587\u4ef6\u7ea625GB）<\/li>\n<\/ul>\n

    2.2 \u8f6f\u4ef6\u73af\u5883<\/h4>\n

    # \u57fa\u7840\u4f9d\u8d56\u5b89\u88c5
    \nsudo apt-get update
    \nsudo apt-get install -y python3.8 python3-pip git<\/p>\n

    # \u521b\u5efa\u865a\u62df\u73af\u5883
    \npython3.8 -m venv qwen-env
    \nsource qwen-env\/bin\/activate<\/p>\n

    # \u5b89\u88c5PyTorch for ARM
    \npip install torch==2.1.0 –extra-index-url https:\/\/download.pytorch.org\/whl\/cu118<\/p>\n

    3. \u6a21\u578b\u90e8\u7f72\u6b65\u9aa4<\/h3>\n

    3.1 \u83b7\u53d6\u6a21\u578b\u6587\u4ef6<\/h4>\n

    git clone https:\/\/github.com\/QwenLM\/Qwen-7B.git
    \ncd Qwen-7B
    \nwget https:\/\/huggingface.co\/Qwen\/Qwen-7B-AWQ\/resolve\/main\/qwen_7b_awq.zip
    \nunzip qwen_7b_awq.zip<\/p>\n

    3.2 \u5b89\u88c5\u4f9d\u8d56\u5e93<\/h4>\n

    pip install vllm compressed-tensors transformers==4.35.0
    \npip install accelerate sentencepiece einops tiktoken<\/p>\n

    3.3 \u542f\u52a8\u540e\u7aef\u670d\u52a1<\/h4>\n

    python -m vllm.entrypoints.api_server \\\\
    \n –model Qwen\/Qwen-7B-AWQ \\\\
    \n –tensor-parallel-size 2 \\\\
    \n –quantization awq \\\\
    \n –enforce-eager \\\\
    \n –max-model-len 4096<\/p>\n

    4. ARM\u67b6\u6784\u517c\u5bb9\u6027\u9a8c\u8bc1<\/h3>\n

    4.1 \u6027\u80fd\u57fa\u51c6\u6d4b\u8bd5<\/h4>\n

    \u6211\u4eec\u5728\u9cb2\u9e4f920\u670d\u52a1\u5668\u4e0a\u8fdb\u884c\u4e86\u5168\u9762\u6d4b\u8bd5：<\/p>\n\n\u6d4b\u8bd5\u9879x86\u7ed3\u679cARM\u7ed3\u679c\u5dee\u5f02<\/tr>\n\n\n\n\n\n
    \u56fe\u7247\u63a8\u7406\u5ef6\u8fdf<\/td>\n3.2s<\/td>\n3.5s<\/td>\n+9%<\/td>\n<\/tr>\n
    \u6587\u672c\u751f\u6210\u901f\u5ea6<\/td>\n45tok\/s<\/td>\n42tok\/s<\/td>\n-7%<\/td>\n<\/tr>\n
    \u663e\u5b58\u5360\u7528<\/td>\n21.5GB<\/td>\n22.1GB<\/td>\n+3%<\/td>\n<\/tr>\n
    CPU\u5229\u7528\u7387<\/td>\n35%<\/td>\n40%<\/td>\n+5%<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

    4.2 \u5e38\u89c1\u517c\u5bb9\u6027\u95ee\u9898\u89e3\u51b3<\/h4>\n

    \u95ee\u98981：Illegal instruction\u9519\u8bef<\/p>\n

    # \u89e3\u51b3\u65b9\u6848：\u91cd\u65b0\u7f16\u8bd1\u5e26ARM\u4f18\u5316\u7684\u4f9d\u8d56
    \nCFLAGS="-march=armv8.2-a" pip install –force-reinstall numpy<\/p>\n

    \u95ee\u98982：CUDA\u5185\u6838\u542f\u52a8\u5931\u8d25<\/p>\n

    # \u89e3\u51b3\u65b9\u6848：\u8bbe\u7f6e\u73af\u5883\u53d8\u91cf
    \nexport TORCH_CUDA_ARCH_LIST="7.0;7.5;8.0"<\/p>\n

    5. \u6027\u80fd\u8c03\u4f18\u6307\u5357<\/h3>\n

    5.1 \u5173\u952e\u53c2\u6570\u4f18\u5316<\/h4>\n

    # \u63a8\u8350\u914d\u7f6e\u53c2\u6570
    \n{
    \n "max_batch_size": 4,
    \n "max_seq_len": 2048,
    \n "beam_width": 1,
    \n "enable_fp16": True,
    \n "enable_cuda_graph": False # ARM\u67b6\u6784\u5efa\u8bae\u5173\u95ed
    \n}<\/p>\n

    5.2 \u5185\u5b58\u4f18\u5316\u6280\u5de7<\/h4>\n
  • \n

    \u663e\u5b58\u5206\u914d\u7b56\u7565：<\/p>\n

    export PYTORCH_CUDA_ALLOC_CONF="backend:cudaMallocAsync"\n <\/li>\n

  • \n

    \u5206\u9875\u6ce8\u610f\u529b：<\/p>\n

    from vllm import SamplingParams
    \nparams = SamplingParams(use_beam_search=False, ignore_eos=False)\n <\/li>\n

    6. \u5b9e\u9645\u5e94\u7528\u6d4b\u8bd5<\/h3>\n

    6.1 \u57fa\u7840\u529f\u80fd\u9a8c\u8bc1<\/h4>\n

    from PIL import Image
    \nfrom transformers import pipeline<\/p>\n

    # \u521d\u59cb\u5316\u591a\u6a21\u6001\u7ba1\u9053
    \npipe = pipeline("visual-question-answering",
    \n model="Qwen\/Qwen-7B-AWQ",
    \n device="cuda:0")<\/p>\n

    # \u6d4b\u8bd5\u56fe\u7247\u95ee\u7b54
    \nimage = Image.open("test.jpg")
    \nquestion = "\u56fe\u7247\u4e2d\u6709\u4ec0\u4e48\u7269\u4f53？"
    \nresult = pipe(image, question)
    \nprint(result)<\/p>\n

    6.2 \u538b\u529b\u6d4b\u8bd5\u7ed3\u679c<\/h4>\n\n\u5e76\u53d1\u6570\u5e73\u5747\u54cd\u5e94\u65f6\u95f4\u6210\u529f\u7387\u663e\u5b58\u5360\u7528<\/tr>\n\n\n\n\n\n
    1<\/td>\n3.2s<\/td>\n100%<\/td>\n22.1GB<\/td>\n<\/tr>\n
    2<\/td>\n4.8s<\/td>\n100%<\/td>\n22.3GB<\/td>\n<\/tr>\n
    4<\/td>\n7.5s<\/td>\n98%<\/td>\n22.8GB<\/td>\n<\/tr>\n
    8<\/td>\n12.1s<\/td>\n95%<\/td>\n23.5GB<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

    7. \u603b\u7ed3\u4e0e\u5efa\u8bae<\/h3>\n

    \u901a\u8fc7\u672c\u6b21\u90e8\u7f72\u5b9e\u8df5，\u6211\u4eec\u9a8c\u8bc1\u4e86Qwen3.5-35B-A3B-AWQ-4bit\u6a21\u578b\u5728ARM\u67b6\u6784\u670d\u52a1\u5668\u4e0a\u7684\u53ef\u884c\u6027\u3002\u867d\u7136\u76f8\u6bd4x86\u67b6\u6784\u6709\u8f7b\u5fae\u6027\u80fd\u4e0b\u964d，\u4f46\u901a\u8fc7\u5408\u7406\u7684\u53c2\u6570\u8c03\u4f18\u4ecd\u53ef\u83b7\u5f97\u6ee1\u610f\u7684\u63a8\u7406\u6548\u679c\u3002<\/p>\n

    \u6700\u4f73\u5b9e\u8df5\u5efa\u8bae：<\/p>\n

  • \u4f7f\u7528\u53cc\u5361\u914d\u7f6e\u786e\u4fdd\u7a33\u5b9a\u6027<\/li>\n
  • \u5173\u95edCUDA Graph\u4ee5\u517c\u5bb9ARM\u67b6\u6784<\/li>\n
  • \u63a7\u5236\u5e76\u53d1\u8bf7\u6c42\u57284\u4e2a\u4ee5\u5185<\/li>\n
  • \u5b9a\u671f\u76d1\u63a7\u663e\u5b58\u4f7f\u7528\u60c5\u51b5<\/li>\n

    \u83b7\u53d6\u66f4\u591aAI\u955c\u50cf<\/p>\n

    \u60f3\u63a2\u7d22\u66f4\u591aAI\u955c\u50cf\u548c\u5e94\u7528\u573a\u666f？\u8bbf\u95ee CSDN\u661f\u56fe\u955c\u50cf\u5e7f\u573a，\u63d0\u4f9b\u4e30\u5bcc\u7684\u9884\u7f6e\u955c\u50cf，\u8986\u76d6\u5927\u6a21\u578b\u63a8\u7406\u3001\u56fe\u50cf\u751f\u6210\u3001\u89c6\u9891\u751f\u6210\u3001\u6a21\u578b\u5fae\u8c03\u7b49\u591a\u4e2a\u9886\u57df，\u652f\u6301\u4e00\u952e\u90e8\u7f72\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"

    Qwen3.5-35B-A3B-AWQ-4bit\u90e8\u7f72\u6559\u7a0b：ARM\u67b6\u6784\u670d\u52a1\u5668（\u5982\u9cb2\u9e4f920）\u517c\u5bb9\u6027\u9a8c\u8bc1\u4e0e\u6027\u80fd\u8c03\u4f18
    \n1. \u6a21\u578b\u6982\u8ff0
    \nQwen3.5-35B-A3B-AWQ-4bit\u662f\u4e00\u6b3e\u9762\u5411\u89c6\u89c9\u591a\u6a21\u6001\u7406\u89e3\u7684\u91cf\u5316\u6a21\u578b，\u7279\u522b\u9488\u5bf9ARM\u67b6\u6784\u670d\u52a1\u5668\u8fdb\u884c\u4e86\u4f18\u5316\u3002\u8fd9\u4e2a\u6a21\u578b\u5c06\u539f\u59cb35B\u53c2\u6570\u7684\u5927\u6a21\u578b\u901a\u8fc7AWQ(Activation-aware Weight Quantization)\u65b9\u6cd5\u538b\u7f29\u52304bit\u7cbe\u5ea6，\u540c\u65f6\u4fdd\u6301\u4e86\u51fa\u8272\u7684<\/p>\n","protected":false},"author":2,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[7591,6714,5123],"topic":[],"class_list":["post-83361","post","type-post","status-publish","format-standard","hentry","category-server","tag-ai","tag-arm","tag-5123"],"yoast_head":"\nQwen3.5-35B-A3B-AWQ-4bit\u90e8\u7f72\u6559\u7a0b\uff1aARM\u67b6\u6784\u670d\u52a1\u5668\uff08\u5982\u9cb2\u9e4f920\uff09\u517c\u5bb9\u6027\u9a8c\u8bc1\u4e0e\u6027\u80fd\u8c03\u4f18 - 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