{"id":98481,"date":"2026-08-31T22:24:50","date_gmt":"2026-08-31T14:24:50","guid":{"rendered":"https:\/\/www.wsisp.com\/helps\/98481.html"},"modified":"2026-08-31T22:24:50","modified_gmt":"2026-08-31T14:24:50","slug":"nvfp4%e4%b8%8efp8%e6%b7%b7%e5%90%88%e7%b2%be%e5%ba%a6%e9%87%8f%e5%8c%96%ef%bc%9a%e7%94%a8autogptq%e6%8a%8a%e5%a4%a7%e6%a8%a1%e5%9e%8b%e5%8e%8b%e5%88%b0%e5%8d%95%e5%8d%a1%e8%b7%91%e8%b5%b7%e6%9d%a5","status":"publish","type":"post","link":"https:\/\/www.wsisp.com\/helps\/98481.html","title":{"rendered":"NVFP4\u4e0eFP8\u6df7\u5408\u7cbe\u5ea6\u91cf\u5316\uff1a\u7528AutoGPTQ\u628a\u5927\u6a21\u578b\u538b\u5230\u5355\u5361\u8dd1\u8d77\u6765\u5b9e\u6218"},"content":{"rendered":"<p>\u5173\u952e\u8bcd&#xff1a;\u4f4e\u6bd4\u7279\u91cf\u5316\u3001FP8\u3001NVFP4\u3001AutoGPTQ\u3001\u5355\u5361\u90e8\u7f72\u3001\u63a8\u7406\u964d\u672c## \u4e00\u3001\u4e3a\u4ec0\u4e48\u662f\u4f4e\u6bd4\u7279\u6d6e\u70b9&#xff0c;\u800c\u4e0d\u662f\u7ee7\u7eed\u5806 INT4\u8fc7\u53bb\u4e00\u5e74&#xff0c;\u5355\u5361\u8dd1\u5927\u6a21\u578b\u7684\u4e3b\u6d41\u89e3\u6cd5\u662fINT4&#xff08;GPTQ\/AWQ&#xff09;\u3002\u5b83\u628a\u6743\u91cd\u538b\u52304-bit\u6574\u6570&#xff0c;\u663e\u5b58\u780d\u534a\u3001\u80fd\u88c5\u4e0b\u66f4\u5927\u7684\u6a21\u578b\u3002\u4f46\u6574\u6570\u6709\u4e2a\u8001\u95ee\u9898&#xff1a;\u52a8\u6001\u8303\u56f4\u7a84\u3002\u6fc0\u6d3b\u503c\u91cc\u5076\u5c14\u8e66\u51fa\u7684\u6781\u5927\u503c&#xff0c;\u4f1a\u88ab\u6574\u6570\u91cf\u5316&#034;\u524a\u5e73&#034;&#xff0c;\u8868\u73b0\u4e3a\u957f\u5c3e\u4efb\u52a1\u6389\u70b9\u3001\u6570\u5b66\u4e0e\u4ee3\u7801\u573a\u666f\u5c24\u5176\u660e\u663e\u3002FP8&#xff08;E4M3\/E5M2&#xff09;\u548cNVFP4&#xff08;Blackwell\u4e0a\u76844-bit\u6d6e\u70b9&#xff09;\u6362\u4e86\u4e2a\u601d\u8def&#xff1a;\u7528&#034;\u6d6e\u70b9&#034;\u4fdd\u7559\u6307\u6570\u4f4d&#xff0c;\u52a8\u6001\u8303\u56f4\u6bd4\u540c\u6bd4\u7279\u7684\u6574\u6570\u5bbd\u5f97\u591a\u3002\u4ee3\u4ef7\u662f\u786c\u4ef6\u95e8\u69db\u2014\u2014FP8\u9700\u8981Hopper\/Blackwell&#xff0c;NVFP4\u76ee\u524d\u57fa\u672c\u53ea\u5728Blackwell&#xff08;B200\/GB200&#xff09;\u4e0a\u8d70TensorRT-LLM\/SGLang\u540e\u7aef\u3002\u6240\u4ee5\u4e00\u4efd\u52a1\u5b9e\u7684\u843d\u5730\u8def\u7ebf\u662f&#xff1a;- \u6d88\u8d39\u7ea7 \/ \u65e7\u5361&#xff1a;INT4&#xff08;AutoGPTQ\/AWQ&#xff09;&#xff0c;\u4eca\u5929\u5c31\u80fd\u8dd1&#xff0c;\u6700\u7a33\u3002- Hopper\/Blackwell&#xff1a;FP8&#xff0c;\u51e0\u4e4e\u65e0\u635f&#xff0c;\u541e\u5410\u53ef\u89c2\u3002- Blackwell \u6781\u81f4\u538b\u7f29&#xff1a;NVFP4&#xff0c;\u663e\u5b58\u4e0e\u5e26\u5bbd\u53cc\u4f18&#xff0c;\u4f46\u751f\u6001\u65e9\u671f&#xff0c;\u5efa\u8bae\u53ea\u7528\u5728\u5df2\u9a8c\u8bc1\u7684\u5b98\u65b9\u6a21\u578b\u3002\u672c\u6587\u7ed9\u4f60\u4e09\u5957\u53ef\u590d\u5236\u547d\u4ee4&#043;\u4ee3\u7801&#xff0c;\u5e76\u6807\u6e05\u6bcf\u4e00\u6b65\u7684\u5de5\u7a0b\u53d6\u820d\u4e0e\u8e29\u5751\u3002\u4e00\u4e2a\u4e0d\u540c\u4e8e&#034;INT4\u4e07\u80fd\u8bba&#034;\u7684\u72ec\u5230\u5224\u65ad\u662f&#xff1a;\u4f4e\u6bd4\u7279\u91cf\u5316\u6ca1\u6709\u94f6\u5f39&#xff0c;\u5148\u6309\u624b\u91cc\u7684\u663e\u5361\u9009\u683c\u5f0f\u3001\u518d\u8c08\u7cbe\u5ea6&#xff0c;\u624d\u662f\u5de5\u7a0b\u4e0a\u6700\u7a33\u7684\u53d6\u820d\u3002\u8fd9\u4e5f\u662f\u5f88\u591a\u56e2\u961f\u53cd\u590d\u8e29\u5751\u540e\u624d\u8ba4\u6e05\u7684\u5dee\u5f02\u5316\u89c6\u89d2\u3002## \u4e8c\u3001\u5b9e\u6218\u4e00&#xff1a;AutoGPTQ \u628a\u6a21\u578b\u91cf\u5316\u4e3a INT4&#xff0c;\u5355\u5361\u52a0\u8f7d\u4ee57B\u7ea7\u6a21\u578b\u4e3a\u4f8b&#xff0c;\u5148\u88c5\u73af\u5883&#xff08;Python3.10&#043;&#xff0c;CUDA12.1&#xff09;&#xff1a;bashpip install auto-gptq&#061;&#061;0.7.1 transformers&#061;&#061;4.44.0 accelerate&#061;&#061;0.33.0# \u5982\u9700 vLLM \u540e\u7aef\u63a8\u7406&#xff0c;\u53ef\u540c\u65f6\u88c5pip install vllm&#061;&#061;0.6.0\u91cf\u5316\u811a\u672c&#xff08;\u7528\u4e00\u5c0f\u6279\u6821\u51c6\u96c6&#xff0c;\u6bd4\u5982128~256\u6761\u771f\u5b9e\u4e1a\u52a1\u8bed\u6599&#xff0c;\u51b3\u5b9a\u91cf\u5316\u8bef\u5dee\u600e\u4e48\u5206\u5e03&#xff09;&#xff1a;pythonfrom auto_gptq import AutoGPTQForCausalLM, BaseQuantizeConfigmodel_id &#061; &#034;Qwen\/Qwen2.5-7B-Instruct&#034; # \u6362\u6210\u4f60\u7684\u57fa\u5ea7quant_config &#061; BaseQuantizeConfig( bits&#061;4, # INT4 group_size&#061;128, # \u5206\u7ec4\u8d8a\u5927\u8d8a\u7701\u663e\u5b58\u3001\u8d8a\u5c0f\u8d8a\u51c6 desc_act&#061;False, # False \u66f4\u5feb&#xff1b;True \u66f4\u51c6\u4f46\u6162 damp_percent&#061;0.01,)model &#061; AutoGPTQForCausalLM.from_pretrained(model_id, quant_config)model.quantize( &#034;calib.jsonl&#034;, # 128~256 \u6761\u771f\u5b9e\u4e1a\u52a1\u8bed\u6599&#xff0c;\u522b\u7528Wiki\u968f\u673a\u884c batch_size&#061;4,)model.save_quantized(&#034;qwen2.5-7b-gptq-int4&#034;)\u52a0\u8f7d\u63a8\u7406&#xff08;\u5355\u5f2024G\u5361\u5373\u53ef&#xff09;&#xff1a;pythonfrom auto_gptq import AutoGPTQForCausalLMfrom transformers import AutoTokenizermodel &#061; AutoGPTQForCausalLM.from_quantized( &#034;qwen2.5-7b-gptq-int4&#034;, device&#061;&#034;cuda:0&#034;, use_triton&#061;False, torch_dtype&#061;&#034;auto&#034;,)tok &#061; AutoTokenizer.from_pretrained(&#034;qwen2.5-7b-gptq-int4&#034;)print(model.generate(**tok(&#034;\u7528\u4e00\u53e5\u8bdd\u89e3\u91caKV\u7f13\u5b58&#034;, return_tensors&#061;&#034;pt&#034;).to(&#034;cuda&#034;), max_new_tokens&#061;64)[0])\u5de5\u7a0b\u53d6\u820d&#xff1a;group_size&#061;128\u662f\u663e\u5b58\u4e0e\u7cbe\u5ea6\u7684\u751c\u70b9&#xff1b;desc_act&#061;False\u57287B\/14B\u4e0a\u51e0\u4e4e\u65e0\u635f\u4e14\u5feb\u4e00\u500d\u3002\u6821\u51c6\u96c6\u52a1\u5fc5\u7528\u4e1a\u52a1\u771f\u5b9e\u5206\u5e03\u7684\u8bed\u6599&#xff0c;\u7528\u901a\u7528\u8bed\u6599\u6821\u51c6&#xff0c;\u5782\u76f4\u573a\u666f\u4f1a\u660e\u663e\u6389\u70b9\u2014\u2014\u8fd9\u662f\u6211\u4eec\u8e29\u8fc7\u6700\u8d35\u7684\u5751\u3002## \u4e09\u3001\u5b9e\u6218\u4e8c&#xff1a;FP8 \u63a8\u7406&#xff08;\u9700 Hopper\/Blackwell&#xff09;H\u5361\u4e0a&#xff0c;transformers\u76f4\u63a5\u8d70FP8\u6743\u91cd&#xff0c;\u65e0\u9700\u79bb\u7ebf\u91cf\u5316\u6587\u4ef6&#xff0c;\u63a8\u7406\u65f6\u5728\u7ebf\u8f6c\u6362&#xff1a;pythonimport torchfrom transformers import AutoModelForCausalLM, AutoTokenizermodel &#061; AutoModelForCausalLM.from_pretrained( &#034;meta-llama\/Llama-3.1-8B-Instruct&#034;, torch_dtype&#061;torch.float8_e4m3fn, # E4M3 FP8 device_map&#061;&#034;auto&#034;,)tok &#061; AutoTokenizer.from_pretrained(&#034;meta-llama\/Llama-3.1-8B-Instruct&#034;)\u82e5\u8981\u518d\u538b\u4e00\u5c42\u4e14\u4fdd\u52a8\u6001\u8303\u56f4&#xff0c;\u53ef\u53e0torchao\u7684float8\u6743\u91cd&#043;\u6fc0\u6d3b\u65b9\u6848&#xff1a;pythonfrom torchao.quantization import quantize_, float8_weight_onlyquantize_(model, float8_weight_only()) # \u6743\u91cd FP8&#xff0c;\u6fc0\u6d3b\u4fdd\u6301\u9ad8\u7cbe\u5ea6\u5de5\u7a0b\u53d6\u820d&#xff1a;FP8\u76f8\u6bd4INT4\u51e0\u4e4e\u4e0d\u6389\u70b9&#xff0c;\u4f46\u5fc5\u987bHopper\u53ca\u4ee5\u4e0a&#xff1b;\u6d88\u8d39\u5361\u5f3a\u884cfloat8_e4m3fn\u4f1a\u62a5\u9519\u6216\u9000\u5316\u6210fp16\u3002NVFP4\u540c\u7406&#xff0c;\u4e14\u76ee\u524d\u66f4\u7a33\u5b9a\u5730\u8d70TensorRT-LLM\u6216SGLang&#xff0c;\u800c\u975e\u539f\u751ftransformers\u3002## \u56db\u3001\u5b9e\u6218\u4e09&#xff1a;NVFP4 \u7684\u5f53\u524d\u5f62\u6001&#xff08;Blackwell&#xff09;NVFP4\u662fBlackwell\u7684\u6740\u624b\u950f&#xff1a;4-bit\u6d6e\u70b9&#xff0c;\u914d\u5408\u7b2c\u516d\u4ee3NVLink\u4e0e\u65b0\u7684TensorCore&#xff0c;\u80fd\u5728\u66f4\u4f4e\u663e\u5b58\u4e0b\u5403\u4e0b\u66f4\u5927\u6a21\u578b\u3002\u4eca\u5929\u80fd\u8dd1\u901a\u7684\u8def\u5f84&#xff0c;\u662f\u7528\u5b98\u65b9\u540e\u7aef\u7684\u914d\u7f6e\u7247\u6bb5&#xff0c;\u800c\u975e\u624b\u5199\u91cf\u5316&#xff1a;bash# SGLang \u542f\u7528 NVFP4&#xff08;\u793a\u4f8b&#xff0c;\u9700 Blackwell &#043; \u5bf9\u5e94\u7248\u672c&#xff09;python -m sglang.launch_server \\\\ &#8211;model-path nvidia\/&#8230;-nvfp4 \\\\ &#8211;quantization fp4 \\\\ &#8211;tp 1\u636e\u82f1\u4f1f\u8fbe\u5b98\u65b9\u535a\u5ba2\u4e0eVeraRubinNVL72\u5b9e\u6d4b\u8bf4\u660e&#xff0c;\u5206\u79bb\u5f0f\u670d\u52a1&#xff08;prefill\/decode\u62c6\u5f00&#xff09;&#043;KV\u611f\u77e5\u8def\u7531&#043;NVFP4\u91cf\u5316\u7ec4\u5408&#xff0c;\u66fe\u628aDeepSeek\u7c7b\u8d1f\u8f7d\u7684\u541e\u5410\u62c9\u5230\u7ea630\u500d\u3002\u6ce8\u610f&#xff1a;\u8fd9\u91cc30\u500d\u662f&#034;\u7ec4\u5408\u4f18\u5316&#034;\u7684\u7ed3\u679c&#xff0c;\u5355\u770bNVFP4\u4e0d\u662f30\u500d&#xff0c;\u522b\u88ab\u6807\u9898\u8bef\u5bfc\u3002## \u4e94\u3001\u5de5\u7a0b\u53d6\u820d\u4e00\u5f20\u8868| \u65b9\u6848 | \u786c\u4ef6 | \u663e\u5b58 | \u7cbe\u5ea6 | \u843d\u5730\u96be\u5ea6 ||\u2014|\u2014|\u2014|\u2014|\u2014|| INT4 (GPTQ) | \u51e0\u4e4e\u4efb\u610f\u5361 | \u6700\u4f4e | \u7565\u6389\u70b9 | \u4f4e&#xff0c;\u4eca\u5929\u5c31\u80fd\u7528 || FP8 | Hopper&#043; | \u4f4e | \u51e0\u4e4e\u65e0\u635f | \u4e2d&#xff0c;\u9700\u65b0\u5361 || NVFP4 | Blackwell | \u6781\u4f4e | \u8fd1\u65e0\u635f(\u5b98\u65b9\u6a21\u578b) | \u9ad8&#xff0c;\u751f\u6001\u65e9\u671f |\u9009\u578b\u94c1\u5f8b&#xff1a;**\u5148\u770b\u4f60\u624b\u91cc\u6709\u54ea\u5f20\u5361&#xff0c;\u518d\u9009\u91cf\u5316\u683c\u5f0f&#xff0c;\u800c\u4e0d\u662f\u53cd\u8fc7\u6765\u3002**90%\u7684\u56e2\u961f&#xff0c;INT4\u5df2\u7ecf\u591f\u7528&#xff1b;\u5269\u4e0b10%\u6709H\u5361\/B\u5361\u7684&#xff0c;\u518d\u4e0aFP8\/NVFP4\u5403\u6027\u80fd\u7ea2\u5229\u3002## \u516d\u3001\u8e29\u5751\u6e05\u5355&#xff08;\u6309\u51fa\u73b0\u9891\u7387\u6392\u5e8f&#xff09;1. \u6821\u51c6\u96c6\u9519\u914d&#xff08;\u6bd4\u5982\u7528 Wiki \u968f\u673a\u884c\u800c\u975e\u4e1a\u52a1\u8bed\u6599\u6821\u51c6&#xff09;&#xff1a;\u5782\u76f4\u6a21\u578b\u957f\u5c3e\u4efb\u52a1\u6389 3~5 \u4e2a\u70b9&#xff0c;\u6362\u4e1a\u52a1\u8bed\u6599\u5373\u53ef&#xff0c;\u8fd9\u662f\u6700\u5e38\u72af\u7684\u9519\u30022. vLLM \u7248\u672c\u4e0e GPTQ \u4e0d\u517c\u5bb9&#xff1a;0.6.x \u5bf9 AutoGPTQ 0.7.x \u652f\u6301\u624d\u7a33&#xff0c;\u522b\u6df7\u65b0\u6df7\u65e7\u30023. FP8 \u5728\u6d88\u8d39\u5361\u9759\u9ed8\u9000\u5316&#xff1a;\u6ca1\u62a5\u9519\u4f46\u901f\u5ea6\u6ca1\u63d0\u5347&#xff0c;\u67e5 torch.cuda.get_device_capability() \u662f\u5426 \u22659.0\u30024. NVFP4 \u81ea\u5b9a\u4e49\u6a21\u578b\u5d29 kernel&#xff1a;\u53ea\u7528\u5382\u5546\u53d1\u5e03\u597d\u7684 -nvfp4 \u6743\u91cd&#xff0c;\u81ea\u91cf\u5316\u57fa\u672c\u8d70\u4e0d\u901a\u30025. group_size \u592a\u5c0f\u663e\u5b58\u7206&#xff1a;128 \u662f\u5e95\u7ebf&#xff0c;64 \u66f4\u51c6\u4f46\u663e\u5b58\u6da8\u4e00\u622a\u3002## \u4e92\u52a8\u63d0\u95ee1. \u4f60\u7684\u7ebf\u4e0a\u6a21\u578b\u73b0\u5728\u7528 INT4 \u8fd8\u662f FP8&#xff1f;\u957f\u5c3e\u4efb\u52a1\u6389\u70b9\u80fd\u63a5\u53d7\u5417&#xff1f;2. 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