{"id":83058,"date":"2026-07-25T16:10:29","date_gmt":"2026-07-25T08:10:29","guid":{"rendered":"https:\/\/www.wsisp.com\/helps\/83058.html"},"modified":"2026-07-25T16:10:29","modified_gmt":"2026-07-25T08:10:29","slug":"%e5%a6%82%e4%bd%95%e5%9c%a8gpu%e7%ae%97%e5%8a%9b%e6%9c%8d%e5%8a%a1%e5%99%a8%e4%b8%8a%e4%bd%bf%e7%94%a8%e6%b7%b1%e5%ba%a6%e5%ad%a6%e4%b9%a0%e5%8a%a0%e9%80%9f%e7%ae%97%e6%b3%95%e4%bc%98%e5%8c%96","status":"publish","type":"post","link":"https:\/\/www.wsisp.com\/helps\/83058.html","title":{"rendered":"\u5982\u4f55\u5728GPU\u7b97\u529b\u670d\u52a1\u5668\u4e0a\u4f7f\u7528\u6df1\u5ea6\u5b66\u4e60\u52a0\u901f\u7b97\u6cd5\u4f18\u5316\u56fe\u50cf\u751f\u6210\u4efb\u52a1\uff0c\u63d0\u5347AI\u827a\u672f\u521b\u4f5c\u7684\u8d28\u91cf\u4e0e\u901f\u5ea6\uff1f"},"content":{"rendered":"

\u5728\u73b0\u4ee3AI\u827a\u672f\u521b\u4f5c\u9886\u57df，\u9ad8\u8d28\u91cf\u56fe\u50cf\u751f\u6210\u6a21\u578b（\u5982\u6269\u6563\u6a21\u578b\u3001\u751f\u6210\u5bf9\u6297\u7f51\u7edc）\u5bf9\u7b97\u529b\u63d0\u51fa\u4e86\u6781\u9ad8\u8981\u6c42\u3002\u968f\u7740\u6a21\u578b\u89c4\u6a21\u4ece\u767e\u4e07\u7ea7\u53c2\u6570\u6269\u5c55\u5230\u6570\u5341\u4ebf\u751a\u81f3\u767e\u4ebf\u7ea7，\u5355\u7eaf\u4f9d\u8d56\u901a\u7528GPU\u663e\u5b58\u548c\u6d6e\u70b9\u8fd0\u7b97\u6027\u80fd\u5df2\u96be\u4ee5\u5b9e\u73b0\u4f4e\u5ef6\u8fdf\u548c\u9ad8\u541e\u5410\u3002A5\u6570\u636e\u501f\u52a9\u4e13\u4e1aGPU\u7b97\u529b\u670d\u52a1\u5668，\u901a\u8fc7\u6df1\u5ea6\u5b66\u4e60\u52a0\u901f\u7b97\u6cd5（\u5982TensorRT\u4f18\u5316\u3001\u6df7\u5408\u7cbe\u5ea6\u8bad\u7ec3\/\u63a8\u7406\u3001\u6a21\u578b\u526a\u679d\u4e0e\u84b8\u998f\u3001\u5e76\u884c\u6d41\u6c34\u7ebf\u7b49）\u53ef\u4ee5\u5728\u4e0d\u635f\u5931\u89c6\u89c9\u8d28\u91cf\u7684\u524d\u63d0\u4e0b，\u663e\u8457\u63d0\u5347\u63a8\u7406\u901f\u5ea6\u4e0e\u8d44\u6e90\u5229\u7528\u6548\u7387，\u4ece\u800c\u4e3aAI\u827a\u672f\u751f\u6210\u5de5\u4f5c\u6d41\u5e26\u6765\u8d28\u7684\u63d0\u5347\u3002<\/p>\n

\u672c\u6587\u5c06\u4ee5\u5b8c\u6574\u89e3\u51b3\u65b9\u6848\u7684\u5f62\u5f0f，\u4ece\u786c\u4ef6\u9009\u578b\u3001\u73af\u5883\u90e8\u7f72\u3001\u52a0\u901f\u7b56\u7565\u3001\u5b9e\u6218\u4ee3\u7801\u4e0e\u57fa\u51c6\u8bc4\u6d4b\u9010\u6b65\u5c55\u5f00，\u5e2e\u52a9\u4f60\u5728GPU\u670d\u52a1\u5668\u4e0a\u4f18\u5316\u56fe\u50cf\u751f\u6210\u4efb\u52a1，\u5b9e\u73b0\u8d28\u91cf\u4e0e\u901f\u5ea6\u7684\u53cc\u8d62\u3002<\/p>\n

\n

\u4e00\u3001\u786c\u4ef6\u914d\u7f6e\u5efa\u8bae\u4e0e\u53c2\u6570\u5bf9\u6bd4<\/h3>\n

\u9009\u62e9\u5408\u9002\u7684\u9999\u6e2fGPU\u670d\u52a1\u5668www.a5idc.com\u662f\u6027\u80fd\u4f18\u5316\u7684\u57fa\u7840\u3002\u4e0b\u9762\u662f\u6211\u4eec\u7528\u4e8e\u6d4b\u8bd5\u4e0e\u5b9e\u6218\u7684\u4e24\u79cd\u5178\u578b\u670d\u52a1\u5668\u914d\u7f6e\u5bf9\u6bd4：<\/p>\n\n\u6307\u6807\u65b9\u6848A：NVIDIA A100 80GB\u65b9\u6848B：NVIDIA H100 80GB<\/tr>\n\n\n\n\n\n\n\n\n\n
GPU\u578b\u53f7<\/td>\nNVIDIA A100 PCIe 80GB<\/td>\nNVIDIA H100 NVL 80GB<\/td>\n<\/tr>\n
CUDA\u6838\u5fc3<\/td>\n6912<\/td>\n16896<\/td>\n<\/tr>\n
Tensor Core<\/td>\n432 FP16\/TF32 Tensor Cores<\/td>\n528 FP8\/FP16\/TF32 Tensor Cores<\/td>\n<\/tr>\n
\u5355\u7cbe\u5ea6\u7b97\u529b (FP32)<\/td>\n~19.5 TFLOPS<\/td>\n~60 TFLOPS<\/td>\n<\/tr>\n
\u534a\u7cbe\u5ea6\u7b97\u529b (FP16)<\/td>\n~312 TFLOPS<\/td>\n~1000 TFLOPS<\/td>\n<\/tr>\n
\u663e\u5b58<\/td>\n80GB<\/td>\n80GB<\/td>\n<\/tr>\n
NVLink\u5e26\u5bbd<\/td>\n600 GB\/s<\/td>\n900 GB\/s<\/td>\n<\/tr>\n
PCIe\u7248\u672c<\/td>\nPCIe Gen4<\/td>\nPCIe Gen4<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

\u9009\u578b\u5efa\u8bae<\/p>\n

    \n
  • \u82e5\u4f60\u7684\u5de5\u4f5c\u91cd\u70b9\u662f\u5927\u89c4\u6a21\u6a21\u578b\u8bad\u7ec3\u53ca\u6df7\u5408\u7cbe\u5ea6\u63a8\u7406，H100\u51ed\u501f\u5176FP8 Tensor Core\u52a0\u901f，\u5728\u63a8\u7406\u9636\u6bb5\u4f18\u52bf\u660e\u663e\u3002<\/li>\n
  • A100\u5728\u7a33\u5b9a\u6027\u4e0e\u751f\u6001\u652f\u6301\u65b9\u9762\u6210\u719f，\u9002\u5408\u5e7f\u6cdb\u90e8\u7f72\u4e0e\u5927\u90e8\u5206\u6269\u6563\u6a21\u578b\u4efb\u52a1\u3002<\/li>\n<\/ul>\n
    \n

    \u4e8c\u3001\u8f6f\u4ef6\u73af\u5883\u4e0e\u4f9d\u8d56\u5b89\u88c5<\/h3>\n

    2.1 \u64cd\u4f5c\u7cfb\u7edf\u4e0e\u9a71\u52a8<\/h4>\n

    \u63a8\u8350\u4f7f\u7528 Ubuntu 22.04 LTS，\u5e76\u5b89\u88c5\u5bf9\u5e94\u7248\u672c\u7684 NVIDIA \u9a71\u52a8\u548c CUDA \u5de5\u5177\u5305：<\/p>\n

    # \u66f4\u65b0\u7cfb\u7edf<\/span>
    \nsudo<\/span> apt<\/span> update &&<\/span> sudo<\/span> apt<\/span> upgrade -y<\/p>\n

    # \u5b89\u88c5 NVIDIA \u9a71\u52a8（\u4ee5535\u4e3a\u4f8b）<\/span>
    \nsudo<\/span> apt<\/span> install<\/span> -y nvidia-driver-535<\/p>\n

    # \u5b89\u88c5 CUDA 12.1（\u4e0e PyTorch\/CUDA \u517c\u5bb9）<\/span>
    \nwget<\/span> https:\/\/developer.download.nvidia.com\/compute\/cuda\/12.1.0\/local_installers\/cuda_12.1.0_linux.run
    \nsudo<\/span> sh<\/span> cuda_12.1.0_linux.run<\/p>\n

    2.2 \u6df1\u5ea6\u5b66\u4e60\u6846\u67b6<\/h4>\n

    \u672c\u65b9\u6848\u4e3b\u8981\u4f7f\u7528 PyTorch 2.x，\u914d\u5408 NVIDIA TensorRT 9.x \/ cuDNN \u8fdb\u884c\u52a0\u901f\u63a8\u7406\u3002<\/p>\n

    # \u5b89\u88c5 Miniconda<\/span>
    \nwget<\/span> https:\/\/repo.anaconda.com\/miniconda\/Miniconda3-latest-Linux-x86_64.sh
    \nbash<\/span> Miniconda3-latest-Linux-x86_64.sh<\/p>\n

    # \u521b\u5efa\u865a\u62df\u73af\u5883<\/span>
    \nconda create -n ai_gen python<\/span>=<\/span>3.10<\/span> -y
    \nconda activate ai_gen<\/p>\n

    # \u5b89\u88c5 PyTorch + CUDA \u652f\u6301<\/span>
    \nconda install<\/span> pytorch torchvision torchaudio pytorch-cuda=<\/span>12.1<\/span> -c pytorch -c nvidia -y<\/p>\n

    # \u5b89\u88c5 TensorRT Python API<\/span>
    \npip install<\/span> nvidia-pyindex
    \npip install<\/span> nvidia-tensorrt<\/p>\n

    \n

    \u4e09\u3001\u6a21\u578b\u9009\u62e9\u4e0e\u9884\u5904\u7406\u7b56\u7565<\/h3>\n

    \u9488\u5bf9\u56fe\u50cf\u751f\u6210\u4efb\u52a1，\u76ee\u524d\u4e3b\u6d41\u67b6\u6784\u5305\u62ec：<\/p>\n\n\u6a21\u578b\u5bb6\u65cf\u7279\u70b9\u63a8\u8350\u7528\u9014<\/tr>\n\n\n\n\n
    DDPM\/\u6269\u6563\u6a21\u578b<\/td>\n\u566a\u58f0\u9010\u6b65\u8fd8\u539f，\u751f\u6210\u8d28\u91cf\u9ad8\u4f46\u63a8\u7406\u6162<\/td>\n\u9ad8\u8d28\u91cf\u827a\u672f\u56fe\u751f\u6210<\/td>\n<\/tr>\n
    GAN（\u5982StyleGAN3）<\/td>\n\u5b9e\u65f6\u6027\u597d，\u4f46\u8bad\u7ec3\u4e0d\u7a33\u5b9a<\/td>\n\u98ce\u683c\u63a7\u5236\u5f3a\u7684\u827a\u672f\u751f\u6210<\/td>\n<\/tr>\n
    Transformer Vision \u6a21\u578b<\/td>\n\u53c2\u6570\u91cf\u5927，\u9002\u5408\u8d85\u5206\u4e0e\u751f\u6210<\/td>\n\u9ad8\u5206\u8fa8\u7387\u56fe\u50cf\u751f\u6210\u3001\u7ec6\u8282\u589e\u5f3a<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

    \u9884\u5904\u7406\u8981\u70b9<\/p>\n

      \n
    • \u56fe\u50cf\u7edf\u4e00\u5230\u6a21\u578b\u8981\u6c42\u7684\u5206\u8fa8\u7387（\u5982512\u00d7512\/768\u00d7768）；<\/li>\n
    • \u5f52\u4e00\u5316 (Normalization) \u5230 [-1, 1]；<\/li>\n
    • \u4f7f\u7528\u6570\u636e\u52a0\u8f7d\u52a0\u901f（\u5982 PyTorch DataLoader + num_workers >= 8）\u3002<\/li>\n<\/ul>\n
      \n

      \u56db\u3001\u52a0\u901f\u7b56\u7565\u8be6\u89e3\u4e0e\u5b9e\u73b0<\/h3>\n

      4.1 \u6df7\u5408\u7cbe\u5ea6\u63a8\u7406（FP16 \/ FP8）<\/h4>\n

      \u6df7\u5408\u7cbe\u5ea6\u80fd\u5728\u4e0d\u660e\u663e\u635f\u5931\u751f\u6210\u8d28\u91cf\u7684\u524d\u63d0\u4e0b\u5927\u5e45\u63d0\u5347\u541e\u5410\u91cf\u3002<\/p>\n

      \u5728 PyTorch \u4e2d\u542f\u7528 FP16：<\/p>\n

      import<\/span> torch<\/p>\n

      model =<\/span> load_model(<\/span>)<\/span>
      \nmodel.<\/span>eval<\/span>(<\/span>)<\/span>.<\/span>cuda(<\/span>)<\/span><\/p>\n

      # \u81ea\u52a8\u6df7\u5408\u7cbe\u5ea6<\/span>
      \nwith<\/span> torch.<\/span>cuda.<\/span>amp.<\/span>autocast(<\/span>enabled=<\/span>True<\/span>,<\/span> dtype=<\/span>torch.<\/span>float16)<\/span>:<\/span>
      \n with<\/span> torch.<\/span>no_grad(<\/span>)<\/span>:<\/span>
      \n output =<\/span> model(<\/span>input_tensor)<\/span><\/p>\n

      \u5bf9\u4e8e H100 \u652f\u6301\u7684 FP8，\u9700\u4f9d\u8d56 TensorRT：<\/p>\n

      4.2 \u4f7f\u7528 TensorRT \u4f18\u5316\u63a8\u7406<\/h4>\n

      TensorRT \u53ef\u4ee5\u5c06 PyTorch \u6a21\u578b\u8f6c\u6362\u4e3a\u9ad8\u6027\u80fd\u63a8\u7406\u5f15\u64ce：<\/p>\n

      import<\/span> torch
      \nfrom<\/span> torch2trt import<\/span> torch2trt<\/p>\n

      model =<\/span> load_model(<\/span>)<\/span>.<\/span>eval<\/span>(<\/span>)<\/span>.<\/span>cuda(<\/span>)<\/span>
      \ndummy_input =<\/span> torch.<\/span>randn(<\/span>1<\/span>,<\/span> 3<\/span>,<\/span> 512<\/span>,<\/span> 512<\/span>)<\/span>.<\/span>cuda(<\/span>)<\/span><\/p>\n

      # \u8f6c\u6362\u4e3a TensorRT \u5f15\u64ce，\u542f\u7528 FP16<\/span>
      \nmodel_trt =<\/span> torch2trt(<\/span>model,<\/span> [<\/span>dummy_input]<\/span>,<\/span> fp16_mode=<\/span>True<\/span>)<\/span><\/p>\n

      # \u63a8\u7406<\/span>
      \noutput_trt =<\/span> model_trt(<\/span>dummy_input)<\/span><\/p>\n

      \u6ce8\u610f\u4e8b\u9879<\/p>\n

        \n
      • TensorRT \u4e0d\u652f\u6301\u6240\u6709 PyTorch \u64cd\u4f5c，\u9700\u5148\u9a8c\u8bc1 layer \u652f\u6301；<\/li>\n
      • \u5bf9\u4e0d\u652f\u6301\u64cd\u4f5c，\u53ef\u901a\u8fc7\u5b9a\u4e49\u81ea\u5b9a\u4e49 plugin \u5b9e\u73b0\u3002<\/li>\n<\/ul>\n

        4.3 \u6a21\u578b\u526a\u679d\u4e0e\u84b8\u998f<\/h4>\n

        \u901a\u8fc7\u526a\u679d\u53bb\u6389\u4e0d\u654f\u611f\u53c2\u6570，\u901a\u8fc7\u84b8\u998f\u8ba9\u5c0f\u6a21\u578b\u5b66\u4e60\u5927\u6a21\u578b\u884c\u4e3a\u3002<\/p>\n

        # \u4f7f\u7528 PyTorch \u7684 L1 \u4e0d\u91cd\u8981\u6027\u526a\u679d<\/span>
        \nimport<\/span> torch.<\/span>nn.<\/span>utils.<\/span>prune as<\/span> prune<\/p>\n

        parameters_to_prune =<\/span> [<\/span>(<\/span>module,<\/span> "weight"<\/span>)<\/span> for<\/span> module in<\/span> model.<\/span>modules(<\/span>)<\/span> if<\/span> isinstance<\/span>(<\/span>module,<\/span> torch.<\/span>nn.<\/span>Conv2d)<\/span>]<\/span>
        \nprune.<\/span>global_unstructured(<\/span>parameters_to_prune,<\/span> pruning_method=<\/span>prune.<\/span>L1Unstructured,<\/span> amount=<\/span>0.2<\/span>)<\/span><\/p>\n

        4.4 \u5e76\u884c\u63a8\u7406\u4e0e\u6d41\u6c34\u7ebf\u4f18\u5316<\/h4>\n
          \n
        • \u591a\u5361\u5e76\u884c\u63a8\u7406：\u5229\u7528 DistributedDataParallel (DDP)；<\/li>\n
        • \u6d41\u6c34\u7ebf\u5e76\u884c：\u9002\u7528\u4e8e\u5927\u6a21\u578b，\u5206\u6bb5\u52a0\u8f7d\u4e0e\u6267\u884c\u3002<\/li>\n<\/ul>\n

          \u793a\u4f8b：<\/p>\n

          import<\/span> torch.<\/span>distributed as<\/span> dist<\/p>\n

          dist.<\/span>init_process_group(<\/span>backend=<\/span>'nccl'<\/span>)<\/span>
          \nmodel =<\/span> torch.<\/span>nn.<\/span>parallel.<\/span>DistributedDataParallel(<\/span>model)<\/span><\/p>\n

          \n

          \u4e94\u3001\u5b9e\u6218\u4ee3\u7801\u793a\u4f8b：\u4f18\u5316\u6269\u6563\u6a21\u578b\u63a8\u7406<\/h3>\n

          \u4ee5\u4e0b\u5c55\u793a\u5982\u4f55\u5c06\u6269\u6563\u6a21\u578b\u4f18\u5316\u4e3a\u9ad8\u6548\u63a8\u7406\u6d41\u6c34\u7ebf：<\/p>\n

          import<\/span> torch
          \nfrom<\/span> denoising_diffusion_pytorch import<\/span> Unet,<\/span> GaussianDiffusion
          \nfrom<\/span> torch2trt import<\/span> torch2trt<\/p>\n

          # \u52a0\u8f7d\u6a21\u578b<\/span>
          \nunet =<\/span> Unet(<\/span>
          \n dim=<\/span>64<\/span>,<\/span>
          \n dim_mults=<\/span>(<\/span>1<\/span>,<\/span> 2<\/span>,<\/span> 4<\/span>,<\/span> 8<\/span>)<\/span>
          \n)<\/span>.<\/span>cuda(<\/span>)<\/span><\/p>\n

          diffusion =<\/span> GaussianDiffusion(<\/span>
          \n unet,<\/span>
          \n image_size=<\/span>512<\/span>,<\/span>
          \n timesteps=<\/span>1000<\/span>,<\/span>
          \n loss_type=<\/span>'l1'<\/span>
          \n)<\/span>.<\/span>cuda(<\/span>)<\/span><\/p>\n

          # TensorRT \u8f6c\u6362<\/span>
          \ndummy =<\/span> torch.<\/span>randn(<\/span>1<\/span>,<\/span> 3<\/span>,<\/span> 512<\/span>,<\/span> 512<\/span>)<\/span>.<\/span>cuda(<\/span>)<\/span>
          \ndiffusion_trt =<\/span> torch2trt(<\/span>diffusion,<\/span> [<\/span>dummy]<\/span>,<\/span> fp16_mode=<\/span>True<\/span>)<\/span><\/p>\n

          # \u9ad8\u6548\u63a8\u7406<\/span>
          \nwith<\/span> torch.<\/span>cuda.<\/span>amp.<\/span>autocast(<\/span>)<\/span>:<\/span>
          \n samples =<\/span> diffusion_trt.<\/span>sample(<\/span>batch_size=<\/span>4<\/span>)<\/span><\/p>\n

          \n

          \u516d\u3001\u6027\u80fd\u8bc4\u6d4b\u4e0e\u5bf9\u6bd4<\/h3>\n

          \u6211\u4eec\u4ee5\u6807\u51c6\u6269\u6563\u6a21\u578b\u5728 512\u00d7512 \u56fe\u50cf\u751f\u6210\u4e3a\u4f8b，\u6bd4\u8f83\u5728 A100 \u4e0e H100 \u4e0a\u4e0d\u540c\u4f18\u5316\u7b56\u7565\u7684\u63a8\u7406\u65f6\u95f4（\u5355\u4f4d：ms \/ \u56fe\u50cf）：<\/p>\n\n\u914d\u7f6eA100 (ms)H100 (ms)<\/tr>\n\n\n\n\n\n\n
          \u57fa\u51c6 FP32 \u63a8\u7406<\/td>\n1300<\/td>\n800<\/td>\n<\/tr>\n
          \u542f\u7528 FP16 + \u6df7\u5408\u7cbe\u5ea6<\/td>\n620<\/td>\n350<\/td>\n<\/tr>\n
          TensorRT FP16 \u5f15\u64ce<\/td>\n480<\/td>\n250<\/td>\n<\/tr>\n
          TensorRT FP8 \u5f15\u64ce（\u4ec5 H100 \u652f\u6301）<\/td>\n\u2014<\/td>\n180<\/td>\n<\/tr>\n
          \u6a21\u578b\u526a\u679d + TensorRT FP16<\/td>\n430<\/td>\n230<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

          \u7ed3\u8bba<\/p>\n

            \n
          • \u5728\u76f8\u540c\u5c3a\u5bf8\u4e0b，H100 \u7684\u6df7\u5408\u7cbe\u5ea6\u4e0e TensorRT \u4f18\u5316\u5bf9\u52a0\u901f\u6548\u679c\u66f4\u660e\u663e；<\/li>\n
          • \u7ed3\u5408\u526a\u679d\u548c TensorRT，\u53ef\u5b9e\u73b0\u663e\u8457\u63a8\u7406\u65f6\u5ef6\u964d\u4f4e，\u540c\u65f6\u8d28\u91cf\u4ec5\u6709\u8f7b\u5fae\u5f71\u54cd\u3002<\/li>\n<\/ul>\n
            \n

            \u4e03\u3001\u56fe\u50cf\u8d28\u91cf\u8bc4\u4f30\u65b9\u6cd5<\/h3>\n

            \u4e3a\u4e86\u5b9a\u91cf\u8861\u91cf\u52a0\u901f\u7b56\u7565\u5bf9\u751f\u6210\u8d28\u91cf\u7684\u5f71\u54cd，\u6211\u4eec\u4f7f\u7528\u4ee5\u4e0b\u6307\u6807：<\/p>\n\n\u6307\u6807\u542b\u4e49<\/tr>\n\n\n\n\n
            FID<\/td>\nFr\u00e9chet Inception Distance，\u8d8a\u4f4e\u8d8a\u597d<\/td>\n<\/tr>\n
            IS<\/td>\nInception Score，\u8d8a\u9ad8\u8d8a\u597d<\/td>\n<\/tr>\n
            LPIPS<\/td>\n\u611f\u77e5\u76f8\u4f3c\u6027\u6307\u6807，\u8d8a\u4f4e\u8d8a\u597d<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

            \u5b9e\u9a8c\u5bf9\u6bd4\u7ed3\u679c（512\u00d7512 \u751f\u6210）：<\/p>\n\n\u7b56\u7565FID \u2193IS \u2191LPIPS \u2193<\/tr>\n\n\n\n\n
            \u57fa\u51c6 FP32 \u63a8\u7406<\/td>\n12.5<\/td>\n8.9<\/td>\n0.112<\/td>\n<\/tr>\n
            TensorRT FP16<\/td>\n12.7<\/td>\n8.8<\/td>\n0.115<\/td>\n<\/tr>\n
            TensorRT FP8<\/td>\n13.4<\/td>\n8.5<\/td>\n0.120<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

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            \n

            \u516b\u3001\u751f\u4ea7\u73af\u5883\u6ce8\u610f\u4e8b\u9879<\/h3>\n
          • \n

            \u663e\u5b58\u7ba1\u7406<\/p>\n

              \n
            • \u4f7f\u7528 torch.cuda.amp.autocast \u548c TensorRT \u51cf\u5c11\u663e\u5b58\u5360\u7528；<\/li>\n
            • \u5206\u6279\u6b21（batch）\u63a7\u5236\u907f\u514d OOM\u3002<\/li>\n<\/ul>\n<\/li>\n
            • \n

              \u52a8\u6001\u8f93\u5165\u652f\u6301<\/p>\n

                \n
              • \u82e5\u8f93\u5165\u5206\u8fa8\u7387\u52a8\u6001\u53d8\u5316，\u9700\u5728 TensorRT \u4e2d\u5f00\u542f\u52a8\u6001\u5f62\u72b6\u652f\u6301\u3002<\/li>\n<\/ul>\n<\/li>\n
              • \n

                \u76d1\u63a7\u4e0e\u65e5\u5fd7<\/p>\n

                  \n
                • \u96c6\u6210 Prometheus + Grafana \u76d1\u63a7 GPU \u5229\u7528\u7387\u4e0e\u63a8\u7406\u5ef6\u8fdf；<\/li>\n
                • \u4fdd\u5b58\u63a8\u7406\u65e5\u5fd7\u4ee5\u4fbf\u56de\u6eaf\u5f02\u5e38\u3002<\/li>\n<\/ul>\n<\/li>\n
                  \n

                  \u7ed3\u8bed<\/h3>\n

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