{"id":97515,"date":"2026-08-29T18:40:40","date_gmt":"2026-08-29T10:40:40","guid":{"rendered":"https:\/\/www.wsisp.com\/helps\/97515.html"},"modified":"2026-08-29T18:40:40","modified_gmt":"2026-08-29T10:40:40","slug":"qwen3-8-flash-next-%e5%bc%80%e6%94%be%e6%9d%83%e9%87%8d%ef%bc%9afp8-%e4%b9%9f%e8%a6%81-172-78-gib%ef%bc%8c%e7%ae%97%e5%ae%b6%e4%ba%91-5090-%e5%88%b0%e5%ba%95%e6%80%8e%e4%b9%88%e9%83%a8%e7%bd%b2","status":"publish","type":"post","link":"https:\/\/www.wsisp.com\/helps\/97515.html","title":{"rendered":"Qwen3.8 Flash-Next \u5f00\u653e\u6743\u91cd\uff1aFP8 \u4e5f\u8981 172.78 GiB\uff0c\u7b97\u5bb6\u4e91 5090 \u5230\u5e95\u600e\u4e48\u90e8\u7f72\uff1f"},"content":{"rendered":"<\/p>\n

\u66f4\u65b0\u65e5\u671f：2026 \u5e74 8 \u6708 27 \u65e5 \u9002\u7528\u5bf9\u8c61：\u5927\u6a21\u578b\u90e8\u7f72\u3001vLLM\u3001\u4e91 GPU\u3001Agent \u5e94\u7528\u5f00\u53d1\u8005<\/p>\n

2026 \u5e74 8 \u6708 27 \u65e5，Qwen \u65b0\u589e\u5f00\u653e\u6743\u91cd\u7684\u662f Qwen3.8-Flash-Next \u548c\u5b98\u65b9 FP8 \u91cf\u5316\u7248\u3002\u5148\u8bf4\u7ed3\u8bba：\u8fd9\u662f\u540c\u4e00\u4e2a\u6a21\u578b\u7684\u4e24\u79cd\u6743\u91cd\u683c\u5f0f，FP8 \u6743\u91cd\u4ecd\u6709 172.78 GiB，\u4e0d\u80fd\u628a\u201c\u6bcf\u4e2a Token \u6fc0\u6d3b 6B \u53c2\u6570\u201d\u7406\u89e3\u6210\u201c\u4e00\u5f20\u6d88\u8d39\u7ea7\u663e\u5361\u5c31\u80fd\u8dd1\u201d\u3002<\/p>\n

\u4eca\u5929\u5230\u5e95\u5f00\u653e\u4e86\u54ea\u4e9b Qwen3.8？<\/h3>\n

\u622a\u81f3 2026 \u5e74 8 \u6708 27 \u65e5，Qwen \u5b98\u65b9\u6a21\u578b\u9875\u53ef\u4ee5\u786e\u8ba4：<\/p>\n\n\u6a21\u578b\u672c\u6b21\u65b0\u589e\u6743\u91cd\u89c4\u6a21\u8bb8\u53ef\u8bc1\u9002\u5408\u573a\u666f<\/tr>\n\n\n\n\n\n
Qwen\/Qwen3.8-Flash-Next<\/td>\n\u662f<\/td>\nBF16 \u7ea6 335.28 GiB<\/td>\nQwen Community 1.0<\/td>\n\u9ad8\u7aef\u591a\u5361\u3001\u67b6\u6784\u7814\u7a76<\/td>\n<\/tr>\n
Qwen\/Qwen3.8-Flash-Next-FP8<\/td>\n\u662f<\/td>\n172.78 GiB<\/td>\nQwen Community 1.0<\/td>\n\u591a\u5361\u63a8\u7406\u3001\u670d\u52a1\u90e8\u7f72<\/td>\n<\/tr>\n
Qwen\/Qwen3.8-27B<\/td>\n\u5426，8 \u6708 14 \u65e5\u5df2\u53d1\u5e03<\/td>\n27B Dense<\/td>\n\u4ee5\u6a21\u578b\u9875\u4e3a\u51c6<\/td>\n\u5355\u5361\u6216\u53cc\u5361\u90e8\u7f72<\/td>\n<\/tr>\n
Qwen\/Qwen3.8-2.4T-A95B<\/td>\n\u5426，8 \u6708 12 \u65e5\u5df2\u53d1\u5e03<\/td>\n2.4T\u3001\u6fc0\u6d3b 95B<\/td>\n\u4ee5\u6a21\u578b\u9875\u4e3a\u51c6<\/td>\n\u8d85\u5927\u89c4\u6a21\u63a8\u7406\u96c6\u7fa4<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

\u4e25\u683c\u6765\u8bf4，\u672c\u6587\u4f7f\u7528\u201c\u5f00\u653e\u6743\u91cd\u201d\u6bd4\u7b3c\u7edf\u79f0\u4e3a\u201c\u5f00\u6e90\u201d\u66f4\u51c6\u786e\u3002Flash-Next \u6a21\u578b\u6743\u91cd\u9875\u663e\u793a\u7684\u8bb8\u53ef\u8bc1\u4e3a qwen-community-1.0，\u4e0d\u8981\u4e0e Qwen GitHub \u4ed3\u5e93\u7684 Apache-2.0 \u8bb8\u53ef\u8bc1\u6df7\u4e3a\u4e00\u8c08\u3002<\/p>\n

\u5b98\u65b9\u6765\u6e90：Qwen3.8-Flash-Next \u6a21\u578b\u9875\u3001FP8 \u6a21\u578b\u9875\u3001Qwen3.8 \u5b98\u65b9\u4ed3\u5e93\u3002<\/p>\n

\u4e3a\u4ec0\u4e48\u6fc0\u6d3b\u53c2\u6570\u53ea\u6709 6B，\u663e\u5b58\u8981\u6c42\u4ecd\u7136\u5f88\u9ad8？<\/h3>\n

Flash-Next \u7684\u7ed3\u6784\u5305\u542b：<\/p>\n

    \n
  • 125B \u4e3b\u6a21\u578b\u53c2\u6570，\u6bcf\u4e2a Token \u6fc0\u6d3b\u7ea6 6B；<\/li>\n
  • \u989d\u5916\u7684 51B N-gram Embedding；<\/li>\n
  • \u7ea6 4B MTP \u53c2\u6570；<\/li>\n
  • 262,144 Token \u539f\u751f\u4e0a\u4e0b\u6587，\u53ef\u901a\u8fc7 YaRN \u6269\u5c55\u81f3 100 \u4e07 Token；<\/li>\n
  • \u89c6\u89c9\u7f16\u7801\u5668，\u652f\u6301\u6587\u672c\u548c\u56fe\u50cf\u8f93\u5165\u3002<\/li>\n<\/ul>\n

    MoE \u7684\u201c\u6fc0\u6d3b\u53c2\u6570\u5c11\u201d\u4e3b\u8981\u964d\u4f4e\u5355\u6b21\u8ba1\u7b97\u91cf，\u5e76\u4e0d\u4f1a\u8ba9\u672a\u6fc0\u6d3b\u6743\u91cd\u51ed\u7a7a\u6d88\u5931\u3002\u6a21\u578b\u52a0\u8f7d\u65f6\u4ecd\u8981\u8003\u8651\u5168\u90e8\u6743\u91cd\u3001KV Cache\u3001CUDA Kernel\u3001\u8fd0\u884c\u65f6\u7f13\u5b58\u548c\u5e76\u53d1\u7a7a\u95f4\u3002<\/p>\n

    \u6839\u636e vLLM \u5b98\u65b9\u90e8\u7f72\u914d\u65b9：<\/p>\n\n\u7248\u672c\u6743\u91cd\u5927\u5c0f\u5b98\u65b9\u9a8c\u8bc1\u914d\u7f6e<\/tr>\n\n\n\n\n
    FP8<\/td>\n172.78 GiB<\/td>\nGB300 \u6700\u4f4e TP2，\u63a8\u8350 TP4；H200 \u4f7f\u7528 TEP8<\/td>\n<\/tr>\n
    BF16<\/td>\n335.28 GiB<\/td>\n\u9a8c\u8bc1\u914d\u7f6e\u7ea6\u5360\u6bcf\u5f20 GB300 190 GiB<\/td>\n<\/tr>\n
    N-gram CPU Offload<\/td>\n\u989d\u5916\u8981\u6c42<\/td>\n\u81f3\u5c11\u51c6\u5907 51 GB \u4e3b\u673a\u5185\u5b58\u53ca\u8fd0\u884c\u4f59\u91cf<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

    \u6240\u4ee5，RTX 4090\u3001RTX 5090\u3001A100 80G \u90fd\u4e0d\u662f Flash-Next \u5b98\u65b9\u9996\u53d1\u914d\u65b9\u4e2d\u7684\u5355\u5361\u90e8\u7f72\u8bbe\u5907\u3002\u5c24\u5176\u4e0d\u8981\u770b\u5230\u201c\u6fc0\u6d3b 6B\u201d\u5c31\u76f4\u63a5\u6309 6B \u6a21\u578b\u4f30\u7b97\u663e\u5b58\u3002<\/p>\n

    \u8def\u7ebf\u4e00：\u6309\u5b98\u65b9\u914d\u7f6e\u90e8\u7f72 Flash-Next FP8<\/h3>\n

    \u76ee\u524d\u6bd4\u8f83\u7a33\u59a5\u7684\u662f\u4f7f\u7528 vLLM \u63d0\u4f9b\u7684\u4e13\u7528\u955c\u50cf，\u800c\u4e0d\u662f\u76f4\u63a5\u6267\u884c\u666e\u901a\u7684 pip install vllm：<\/p>\n

    docker pull vllm\/vllm-openai:qwen38-flash-next <\/p>\n

    \u4ee5\u5b98\u65b9\u9a8c\u8bc1\u7684 8\u00d7H200 \u914d\u7f6e\u4e3a\u4f8b：<\/p>\n

    vllm serve Qwen\/Qwen3.8-Flash-Next-FP8 \\\\
    \n –tensor-parallel-size 8 \\\\
    \n –enable-expert-parallel \\\\
    \n –moe-backend triton \\\\
    \n –gpu-memory-utilization 0.85 \\\\
    \n –max-num-seqs 256 \\\\
    \n –enable-prefix-caching \\\\
    \n –no-enable-flashinfer-autotune \\\\
    \n –enable-auto-tool-choice \\\\
    \n –tool-call-parser qwen3_xml \\\\
    \n –reasoning-parser qwen3 <\/p>\n

    \u8fd9\u91cc\u6709\u4e00\u4e2a\u5bb9\u6613\u8e29\u5751\u7684\u70b9：\u5b98\u65b9\u914d\u65b9\u660e\u786e\u6307\u51fa，H200 \u4e0a\u4e0d\u80fd\u76f4\u63a5\u4f7f\u7528\u666e\u901a TP8 \u52a0\u8f7d\u8be5 FP8 \u6743\u91cd，\u9700\u8981\u542f\u7528\u4e13\u5bb6\u5e76\u884c，\u5373 TEP8\u3002<\/p>\n

    \u5982\u679c\u52a0\u8f7d\u9636\u6bb5 OOM，\u53ef\u4ee5\u4f9d\u6b21\u68c0\u67e5：<\/p>\n

  • \u662f\u5426\u4f7f\u7528\u4e86 Flash-Next \u4e13\u7528 vLLM \u955c\u50cf；<\/li>\n
  • \u662f\u5426\u8bef\u628a\u539f\u751f\u4e0a\u4e0b\u6587\u76f4\u63a5\u6269\u5230 100 \u4e07 Token；<\/li>\n
  • \u662f\u5426\u9700\u8981\u542f\u7528 N-gram Embedding CPU Offload；<\/li>\n
  • \u662f\u5426\u7ed9\u4e3b\u673a\u5185\u5b58\u9884\u7559\u4e86\u81f3\u5c11 51 GB \u52a0\u8fd0\u884c\u4f59\u91cf；<\/li>\n
  • \u662f\u5426\u9700\u8981\u63d0\u9ad8 TP \u6570\u91cf\u6216\u964d\u4f4e –max-model-len\u3002<\/li>\n

    \u8def\u7ebf\u4e8c：\u60f3\u5728\u7b97\u5bb6\u4e91（suanjiayun.com) \u5c3d\u5feb\u8dd1\u8d77\u6765，\u5148\u90e8\u7f72 Qwen3.8-27B<\/h3>\n

    \u5982\u679c\u76ee\u6807\u662f\u4eca\u5929\u5b8c\u6210 Qwen3.8 API \u9a8c\u8bc1，\u800c\u4e0d\u662f\u7814\u7a76 Flash-Next \u67b6\u6784，\u66f4\u73b0\u5b9e\u7684\u9009\u62e9\u662f Qwen3.8-27B\u3002<\/p>\n

    vLLM \u5b98\u65b9\u914d\u65b9\u5df2\u7ecf\u9a8c\u8bc1：<\/p>\n

      \n
    • 2\u00d7RTX 5090 \u53ef\u4ee5\u8fd0\u884c Qwen3.8-27B \u7684 FP8 \u6216 NVFP4 \u7248\u672c；<\/li>\n
    • 1\u00d7RTX 5090 \u53ef\u4ee5\u8fd0\u884c NVFP4，\u4f46\u9700\u8981\u964d\u4f4e\u4e0a\u4e0b\u6587\u5e76\u589e\u52a0 –enforce-eager；<\/li>\n
    • \u53cc\u5361\u914d\u7f6e\u66f4\u9002\u5408\u4fdd\u7559\u957f\u4e0a\u4e0b\u6587\u548c KV Cache \u7a7a\u95f4\u3002<\/li>\n<\/ul>\n

      \u7b97\u5bb6\u4e91\u5f53\u524d\u5b98\u7f51\u5217\u51fa\u4e86 32GB \u663e\u5b58\u7684 RTX 5090\u3002\u5b9e\u9645\u5e93\u5b58\u3001\u533a\u57df\u548c\u53ef\u9009\u5361\u6570\u4f1a\u52a8\u6001\u53d8\u5316，\u5e94\u4ee5\u521b\u5efa\u5b9e\u4f8b\u9875\u9762\u4e3a\u51c6\u3002<\/p>\n

      \u5728\u7b97\u5bb6\u4e91\u521b\u5efa\u5b9e\u4f8b\u540e，\u5148\u68c0\u67e5\u8fd0\u884c\u73af\u5883：<\/p>\n

      nvidia-smi
      \npython -V
      \npython -c "import torch; print(torch.__version__, torch.cuda.is_available())" <\/p>\n

      \u5982\u679c\u521b\u5efa\u9875\u9762\u63d0\u4f9b\u53cc RTX 5090，\u53ef\u4ee5\u53c2\u8003\u4ee5\u4e0b FP8 \u542f\u52a8\u65b9\u5f0f：<\/p>\n

      vllm serve Qwen\/Qwen3.8-27B-FP8 \\\\
      \n –tensor-parallel-size 2 \\\\
      \n –max-model-len 262144 \\\\
      \n –kv-cache-dtype fp8 \\\\
      \n –reasoning-parser qwen3 \\\\
      \n –enable-auto-tool-choice \\\\
      \n –tool-call-parser qwen3_coder <\/p>\n

      \u542f\u52a8\u540e\u9a8c\u8bc1 OpenAI \u517c\u5bb9\u63a5\u53e3：<\/p>\n

      curl http:\/\/127.0.0.1:8000\/v1\/chat\/completions \\\\
      \n -H "Content-Type: application\/json" \\\\
      \n -d '{
      \n "model": "Qwen\/Qwen3.8-27B-FP8",
      \n "messages": [
      \n {"role": "user", "content": "\u7528 Python \u5b9e\u73b0\u4e00\u4e2a\u5e26\u91cd\u8bd5\u7684\u5f02\u6b65 HTTP \u5ba2\u6237\u7aef"}
      \n ],
      \n "temperature": 1.0,
      \n "top_p": 0.95,
      \n "max_tokens": 1024
      \n }' <\/p>\n

      \u5982\u679c\u670d\u52a1\u53ea\u8fd4\u56de <think> \u5185\u5bb9，\u6216\u8005\u7b54\u6848\u88ab\u63a8\u7406\u8fc7\u7a0b\u6324\u5360，\u9700\u8981\u786e\u8ba4\u542f\u52a8\u53c2\u6570\u4e2d\u5305\u542b：<\/p>\n

      –reasoning-parser qwen3 <\/p>\n

      \u7b97\u5bb6\u4e91\u9002\u5408\u653e\u5728\u54ea\u4e2a\u73af\u8282？<\/h3>\n

      \u7b97\u5bb6\u4e91\u66f4\u9002\u5408\u627f\u62c5\u201c\u6309\u9700\u51c6\u5907 GPU \u73af\u5883\u3001\u4e0b\u8f7d\u6743\u91cd\u3001\u542f\u52a8\u63a8\u7406\u670d\u52a1\u3001\u5b8c\u6210 API \u9a8c\u6536\u201d\u8fd9\u6bb5\u5de5\u7a0b\u6d41\u7a0b\u3002<\/p>\n

      \u5e73\u53f0\u652f\u6301 SSH\u3001JupyterLab\u3001VS Code \u7b49\u8fde\u63a5\u65b9\u5f0f；\u6309\u91cf\u5b9e\u4f8b\u5f00\u673a\u5f00\u59cb\u8ba1\u7b97\u7b97\u529b\u8d39\u7528，\u5173\u673a\u7ed3\u675f\u5b9e\u4f8b\u7b97\u529b\u8ba1\u8d39，\u4e0d\u8db3\u4e00\u5c0f\u65f6\u7684\u4f7f\u7528\u65f6\u6bb5\u6309\u79d2\u8ba1\u8d39\u3002\u6a21\u578b\u6743\u91cd\u3001\u6570\u636e\u76d8\u7b49\u5173\u8054\u8d44\u6e90\u662f\u5426\u7ee7\u7eed\u8ba1\u8d39，\u5219\u8981\u5206\u522b\u67e5\u770b\u5bf9\u5e94\u89c4\u5219\u3002<\/p>\n

      \u5bf9\u4e8e Qwen3.8，\u5efa\u8bae\u8fd9\u6837\u9009：<\/p>\n\n\u76ee\u6807\u5efa\u8bae\u8def\u7ebf<\/tr>\n\n\n\n\n\n\n
      \u5feb\u901f\u9a8c\u8bc1 Qwen3.8 \u5bf9\u8bdd\u3001\u4ee3\u7801\u548c\u5de5\u5177\u8c03\u7528<\/td>\n\u53cc RTX 5090 + Qwen3.8-27B-FP8<\/td>\n<\/tr>\n
      \u5355\u5361\u5b9e\u9a8c<\/td>\nRTX 5090 + 27B NVFP4，\u7f29\u77ed\u4e0a\u4e0b\u6587<\/td>\n<\/tr>\n
      \u7814\u7a76 Flash-Next \u65b0\u67b6\u6784<\/td>\n\u9009\u62e9\u5b98\u65b9\u9a8c\u8bc1\u7684 H200\u3001B200\u3001GB300 \u7b49\u591a\u5361\u914d\u7f6e<\/td>\n<\/tr>\n
      \u751f\u4ea7\u7ea7\u9ad8\u5e76\u53d1<\/td>\n\u5148\u538b\u6d4b\u541e\u5410\u3001\u9996 Token \u5ef6\u8fdf\u548c KV Cache，\u518d\u51b3\u5b9a TP\/TEP<\/td>\n<\/tr>\n
      \u53ea\u662f\u8c03\u7528\u6a21\u578b\u80fd\u529b<\/td>\n\u76f4\u63a5\u4f7f\u7528\u6258\u7ba1 API，\u907f\u514d\u7ef4\u62a4\u63a8\u7406\u57fa\u7840\u8bbe\u65bd<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

      FAQ<\/h3>\n

      Qwen3.8-Flash-Next \u662f 6B \u6a21\u578b\u5417？<\/h4>\n

      \u4e0d\u662f\u3002\u5b83\u7684\u4e3b\u6a21\u578b\u4e3a 125B \u53c2\u6570，\u6bcf\u4e2a Token \u6fc0\u6d3b\u7ea6 6B，\u6b64\u5916\u8fd8\u6709 51B N-gram Embedding \u548c MTP \u53c2\u6570\u3002<\/p>\n

      \u4e00\u5f20 RTX 5090 \u80fd\u8fd0\u884c Flash-Next FP8 \u5417？<\/h4>\n

      \u4e0d\u80fd\u6309\u5b98\u65b9 FP8 \u6743\u91cd\u76f4\u63a5\u52a0\u8f7d\u3002FP8 checkpoint \u4e3a 172.78 GiB，\u8fdc\u8d85\u5355\u5f20 RTX 5090 \u7684 32GB \u663e\u5b58\u3002<\/p>\n

      \u4e3a\u4ec0\u4e48\u63a8\u8350\u5728\u7b97\u5bb6\u4e91\u5148\u8dd1 Qwen3.8-27B？<\/h4>\n

      \u56e0\u4e3a 27B \u5df2\u6709\u53cc RTX 5090 \u7684 vLLM \u9a8c\u8bc1\u8bb0\u5f55，\u5de5\u7a0b\u95e8\u69db\u660e\u663e\u4f4e\u4e8e Flash-Next，\u66f4\u9002\u5408\u5feb\u901f\u5b8c\u6210 API \u548c\u4e1a\u52a1\u6548\u679c\u9a8c\u8bc1\u3002<\/p>\n

      Flash-Next \u80fd\u76f4\u63a5\u4f7f\u7528 100 \u4e07 Token \u4e0a\u4e0b\u6587\u5417？<\/h4>\n

      \u539f\u751f\u4e0a\u4e0b\u6587\u662f 262,144 Token\u3002\u6269\u5c55\u81f3 100 \u4e07 Token \u9700\u8981\u663e\u5f0f\u542f\u7528 YaRN，\u5e76\u91cd\u65b0\u8bc4\u4f30\u663e\u5b58\u5360\u7528\u548c\u77ed\u4e0a\u4e0b\u6587\u8d28\u91cf\u3002<\/p>\n

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