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  2. AI硬件
  3. 关于INTEL 的B70 PRO。

关于INTEL 的B70 PRO。

已定时 已固定 已锁定 已移动 AI硬件
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  • sirwangS 离线
    sirwangS 离线
    sirwang
    编写于 最后由 编辑
    #28

    请把俩代码合一块。就可以了。

        {
          "id": 26,
          "type": "SaveImage",
          "pos": [
            1145.1501729206636,
            195.07454751045992
          ],
          "size": [
            267.9266338657344,
            433.279270302052
          ],
          "flags": {},
          "order": 21,
          "mode": 0,
          "inputs": [
            {
              "label": "图像组",
              "name": "images",
              "type": "IMAGE",
              "link": 29
            },
            {
              "label": "文件名前缀",
              "name": "filename_prefix",
              "type": "STRING",
              "widget": {
                "name": "filename_prefix"
              },
              "link": null
            }
          ],
          "outputs": [],
          "properties": {
            "cnr_id": "comfy-core",
            "ver": "0.6.0",
            "Node name for S&R": "SaveImage",
            "ue_properties": {
              "widget_ue_connectable": {},
              "input_ue_unconnectable": {},
              "version": "7.5.2"
            },
            "ttNbgOverride": {
              "color": "#332922",
              "bgcolor": "#593930",
              "groupcolor": "#b06634"
            }
          },
          "widgets_values": [
            "ComfyUI"
          ],
          "color": "#332922",
          "bgcolor": "#593930"
        },
        {
          "id": 11,
          "type": "PreviewImage",
          "pos": [
            1487.4226159090695,
            -382.86645688700804
          ],
          "size": [
            861.0544444444449,
            1199.7606666666666
          ],
          "flags": {},
          "order": 20,
          "mode": 0,
          "inputs": [
            {
              "label": "图像组",
              "name": "images",
              "type": "IMAGE",
              "link": 17
            }
          ],
          "outputs": [],
          "properties": {
            "cnr_id": "comfy-core",
            "ver": "0.6.0",
            "Node name for S&R": "PreviewImage",
            "ue_properties": {
              "widget_ue_connectable": {},
              "version": "7.5.2",
              "input_ue_unconnectable": {}
            },
            "ttNbgOverride": {
              "color": "#332922",
              "bgcolor": "#593930",
              "groupcolor": "#b06634"
            }
          },
          "widgets_values": [],
          "color": "#332922",
          "bgcolor": "#593930"
        },
        {
          "id": 25,
          "type": "Note",
          "pos": [
            2890.282933565041,
            -663.9119545246833
          ],
          "size": [
            421.42547299979583,
            1472.8439775316037
          ],
          "flags": {},
          "order": 3,
          "mode": 0,
          "inputs": [],
          "outputs": [],
          "properties": {
            "ue_properties": {
              "widget_ue_connectable": {},
              "version": "7.5.2",
              "input_ue_unconnectable": {}
            }
          },
          "widgets_values": [
            "正面视图低角度特写:<sks> front view low-angle shot close-up\n\n右前侧视图低角度特写:<sks> front-right quarter view low-angle shot close-up\n\n右侧视图低角度特写:<sks> right side view low-angle shot close-up\n\n右后侧视图低角度特写:<sks> back-right quarter view low-angle shot close-up\n\n背面视图低角度特写:<sks> back view low-angle shot close-up\n\n左后侧视图低角度特写:<sks> back-left quarter view low-angle shot close-up\n\n左侧视图低角度特写:<sks> left side view low-angle shot close-up\n\n左前侧视图低角度特写:<sks> front-left quarter view low-angle shot close-up\n\n正面视图平视特写:<sks> front view eye-level shot close-up\n\n右前侧视图平视特写:<sks> front-right quarter view eye-level shot close-up\n\n右侧视图平视特写:<sks> right side view eye-level shot close-up\n\n右后侧视图平视特写:<sks> back-right quarter view eye-level shot close-up\n\n背面视图平视特写:<sks> back view eye-level shot close-up\n\n左后侧视图平视特写:<sks> back-left quarter view eye-level shot close-up\n\n左侧视图平视特写:<sks> left side view eye-level shot close-up\n\n左前侧视图平视特写:<sks> front-left quarter view eye-level shot close-up\n\n正面视图高位拍摄特写:<sks> front view elevated shot close-up\n\n右前侧视图高位拍摄特写:<sks> front-right quarter view elevated shot close-up\n\n右侧视图高位拍摄特写:<sks> right side view elevated shot close-up\n\n右后侧视图高位拍摄特写:<sks> back-right quarter view elevated shot close-up\n\n背面视图高位拍摄特写:<sks> back view elevated shot close-up\n\n左后侧视图高位拍摄特写:<sks> back-left quarter view elevated shot close-up\n\n左侧视图高位拍摄特写:<sks> left side view elevated shot close-up\n\n左前侧视图高位拍摄特写:<sks> front-left quarter view elevated shot close-up\n\n正面视图高角度特写:<sks> front view high-angle shot close-up\n\n右前侧视图高角度特写:<sks> front-right quarter view high-angle shot close-up\n\n右侧视图高角度特写:<sks> right side view high-angle shot close-up\n\n右后侧视图高角度特写:<sks> back-right quarter view high-angle shot close-up\n\n背面视图高角度特写:<sks> back view high-angle shot close-up\n\n左后侧视图高角度特写:<sks> back-left quarter view high-angle shot close-up\n\n左侧视图高角度特写:<sks> left side view high-angle shot close-up\n\n左前侧视图高角度特写:<sks> front-left quarter view high-angle shot close-up\n\n正面视图低角度中景:<sks> front view low-angle shot medium shot\n\n右前侧视图低角度中景:<sks> front-right quarter view low-angle shot medium shot\n\n右侧视图低角度中景:<sks> right side view low-angle shot medium shot\n\n右后侧视图低角度中景:<sks> back-right quarter view low-angle shot medium shot\n\n背面视图低角度中景:<sks> back view low-angle shot medium shot\n\n左后侧视图低角度中景:<sks> back-left quarter view low-angle shot medium shot\n\n左侧视图低角度中景:<sks> left side view low-angle shot medium shot\n\n左前侧视图低角度中景:<sks> front-left quarter view low-angle shot medium shot\n\n正面视图平视中景:<sks> front view eye-level shot medium shot\n\n右前侧视图平视中景:<sks> front-right quarter view eye-level shot medium shot\n\n右侧视图平视中景:<sks> right side view eye-level shot medium shot\n\n右后侧视图平视中景:<sks> back-right quarter view eye-level shot medium shot\n\n背面视图平视中景:<sks> back view eye-level shot medium shot\n\n左后侧视图平视中景:<sks> back-left quarter view eye-level shot medium shot\n\n左侧视图平视中景:<sks> left side view eye-level shot medium shot\n\n左前侧视图平视中景:<sks> front-left quarter view eye-level shot medium shot\n\n正面视图高位拍摄中景:<sks> front view elevated shot medium shot\n\n右前侧视图高位拍摄中景:<sks> front-right quarter view elevated shot medium shot\n\n右侧视图高位拍摄中景:<sks> right side view elevated shot medium shot\n\n右后侧视图高位拍摄中景:<sks> back-right quarter view elevated shot medium shot\n\n背面视图高位拍摄中景:<sks> back view elevated shot medium shot\n\n左后侧视图高位拍摄中景:<sks> back-left quarter view elevated shot medium shot\n\n左侧视图高位拍摄中景:<sks> left side view elevated shot medium shot\n\n左前侧视图高位拍摄中景:<sks> front-left quarter view elevated shot medium shot\n\n正面视图高角度中景:<sks> front view high-angle shot medium shot\n\n右前侧视图高角度中景:<sks> front-right quarter view high-angle shot medium shot\n\n右侧视图高角度中景:<sks> right side view high-angle shot medium shot\n\n右后侧视图高角度中景:<sks> back-right quarter view high-angle shot medium shot\n\n背面视图高角度中景:<sks> back view high-angle shot medium shot\n\n左后侧视图高角度中景:<sks> back-left quarter view high-angle shot medium shot\n\n左侧视图高角度中景:<sks> left side view high-angle shot medium shot\n\n左前侧视图高角度中景:<sks> front-left quarter view high-angle shot medium shot\n\n正面视图低角度广角:<sks> front view low-angle shot wide shot\n\n右前侧视图低角度广角:<sks> front-right quarter view low-angle shot wide shot\n\n右侧视图低角度广角:<sks> right side view low-angle shot wide shot\n\n右后侧视图低角度广角:<sks> back-right quarter view low-angle shot wide shot\n\n背面视图低角度广角:<sks> back view low-angle shot wide shot\n\n左后侧视图低角度广角:<sks> back-left quarter view low-angle shot wide shot\n\n左侧视图低角度广角:<sks> left side view low-angle shot wide shot\n\n左前侧视图低角度广角:<sks> front-left quarter view low-angle shot wide shot"
          ],
          "color": "#223",
          "bgcolor": "#335"
        },
        {
          "id": 27,
          "type": "Note",
          "pos": [
            -1009.7430949133999,
            -667.218084639018
          ],
          "size": [
            3344.867777777771,
            172.79096969696934
          ],
          "flags": {},
          "order": 4,
          "mode": 0,
          "inputs": [],
          "outputs": [],
          "properties": {
            "ue_properties": {
              "widget_ue_connectable": {},
              "version": "7.8",
              "input_ue_unconnectable": {}
            }
          },
          "widgets_values": [
            "可以使用comfuyi-lumi-batcher 来跑各个角度的,一下子跑出去90条。\n在这个流里,直接换位置,也就是把位置选成节点19,之后把参数选成左边复制的提示信息就行. william"
          ],
          "color": "#232",
          "bgcolor": "#353"
        },
        {
          "id": 20,
          "type": "Note",
          "pos": [
            2392.2425113410113,
            -661.9303860055464
          ],
          "size": [
            466.503515625,
            1468.5173727560402
          ],
          "flags": {},
          "order": 5,
          "mode": 0,
          "inputs": [],
          "outputs": [],
          "title": "All prompt possible for the Lora Qwen image edit multiple angles",
          "properties": {
            "ue_properties": {
              "widget_ue_connectable": {},
              "version": "7.5.2",
              "input_ue_unconnectable": {}
            }
          },
          "widgets_values": [
            "<sks> front view low-angle shot close-up\n<sks> front-right quarter view low-angle shot close-up\n<sks> right side view low-angle shot close-up\n<sks> back-right quarter view low-angle shot close-up\n<sks> back view low-angle shot close-up\n<sks> back-left quarter view low-angle shot close-up\n<sks> left side view low-angle shot close-up\n<sks> front-left quarter view low-angle shot close-up\n<sks> front view eye-level shot close-up\n<sks> front-right quarter view eye-level shot close-up\n<sks> right side view eye-level shot close-up\n<sks> back-right quarter view eye-level shot close-up\n<sks> back view eye-level shot close-up\n<sks> back-left quarter view eye-level shot close-up\n<sks> left side view eye-level shot close-up\n<sks> front-left quarter view eye-level shot close-up\n<sks> front view elevated shot close-up\n<sks> front-right quarter view elevated shot close-up\n<sks> right side view elevated shot close-up\n<sks> back-right quarter view elevated shot close-up\n<sks> back view elevated shot close-up\n<sks> back-left quarter view elevated shot close-up\n<sks> left side view elevated shot close-up\n<sks> front-left quarter view elevated shot close-up\n<sks> front view high-angle shot close-up\n<sks> front-right quarter view high-angle shot close-up\n<sks> right side view high-angle shot close-up\n<sks> back-right quarter view high-angle shot close-up\n<sks> back view high-angle shot close-up\n<sks> back-left quarter view high-angle shot close-up\n<sks> left side view high-angle shot close-up\n<sks> front-left quarter view high-angle shot close-up\n<sks> front view low-angle shot medium shot\n<sks> front-right quarter view low-angle shot medium shot\n<sks> right side view low-angle shot medium shot\n<sks> back-right quarter view low-angle shot medium shot\n<sks> back view low-angle shot medium shot\n<sks> back-left quarter view low-angle shot medium shot\n<sks> left side view low-angle shot medium shot\n<sks> front-left quarter view low-angle shot medium shot\n<sks> front view eye-level shot medium shot\n<sks> front-right quarter view eye-level shot medium shot\n<sks> right side view eye-level shot medium shot\n<sks> back-right quarter view eye-level shot medium shot\n<sks> back view eye-level shot medium shot\n<sks> back-left quarter view eye-level shot medium shot\n<sks> left side view eye-level shot medium shot\n<sks> front-left quarter view eye-level shot medium shot\n<sks> front view elevated shot medium shot\n<sks> front-right quarter view elevated shot medium shot\n<sks> right side view elevated shot medium shot\n<sks> back-right quarter view elevated shot medium shot\n<sks> back view elevated shot medium shot\n<sks> back-left quarter view elevated shot medium shot\n<sks> left side view elevated shot medium shot\n<sks> front-left quarter view elevated shot medium shot\n<sks> front view high-angle shot medium shot\n<sks> front-right quarter view high-angle shot medium shot\n<sks> right side view high-angle shot medium shot\n<sks> back-right quarter view high-angle shot medium shot\n<sks> back view high-angle shot medium shot\n<sks> back-left quarter view high-angle shot medium shot\n<sks> left side view high-angle shot medium shot\n<sks> front-left quarter view high-angle shot medium shot\n<sks> front view low-angle shot wide shot\n<sks> front-right quarter view low-angle shot wide shot\n<sks> right side view low-angle shot wide shot\n<sks> back-right quarter view low-angle shot wide shot\n<sks> back view low-angle shot wide shot\n<sks> back-left quarter view low-angle shot wide shot\n<sks> left side view low-angle shot wide shot\n<sks> front-left quarter view low-angle shot wide shot\n<sks> front view eye-level shot wide shot\n<sks> front-right quarter view eye-level shot wide shot\n<sks> right side view eye-level shot wide shot\n<sks> back-right quarter view eye-level shot wide shot\n<sks> back view eye-level shot wide shot\n<sks> back-left quarter view eye-level shot wide shot\n<sks> left side view eye-level shot wide shot\n<sks> front-left quarter view eye-level shot wide shot\n<sks> front view elevated shot wide shot\n<sks> front-right quarter view elevated shot wide shot\n<sks> right side view elevated shot wide shot\n<sks> back-right quarter view elevated shot wide shot\n<sks> back view elevated shot wide shot\n<sks> back-left quarter view elevated shot wide shot\n<sks> left side view elevated shot wide shot\n<sks> front-left quarter view elevated shot wide shot\n<sks> front view high-angle shot wide shot\n<sks> front-right quarter view high-angle shot wide shot\n<sks> right side view high-angle shot wide shot\n<sks> back-right quarter view high-angle shot wide shot\n<sks> back view high-angle shot wide shot\n<sks> back-left quarter view high-angle shot wide shot\n<sks> left side view high-angle shot wide shot\n<sks> front-left quarter view high-angle shot wide shot"
          ],
          "color": "#232",
          "bgcolor": "#353"
        },
        {
          "id": 13,
          "type": "TextEncodeQwenImageEditPlus",
          "pos": [
            346.3903358490907,
            -56.08507473664949
          ],
          "size": [
            400.4109260819468,
            258.660770021565
          ],
          "flags": {},
          "order": 13,
          "mode": 0,
          "inputs": [
            {
              "label": "CLIP",
              "name": "clip",
              "type": "CLIP",
              "link": 18
            },
            {
              "label": "VAE",
              "name": "vae",
              "shape": 7,
              "type": "VAE",
              "link": 19
            },
            {
              "label": "图像1",
              "name": "image1",
              "shape": 7,
              "type": "IMAGE",
              "link": 20
            },
            {
              "label": "图像2",
              "name": "image2",
              "shape": 7,
              "type": "IMAGE",
              "link": null
            },
            {
              "label": "图像3",
              "name": "image3",
              "shape": 7,
              "type": "IMAGE",
              "link": null
            },
            {
              "label": "提示词",
              "name": "prompt",
              "type": "STRING",
              "widget": {
                "name": "prompt"
              },
              "link": 30
            }
          ],
          "outputs": [
            {
              "label": "条件",
              "name": "CONDITIONING",
              "type": "CONDITIONING",
              "links": [
                3
              ]
            }
          ],
          "title": "TextEncodeQwenImageEditPlus (Positive)",
          "properties": {
            "cnr_id": "comfy-core",
            "ver": "0.5.1",
            "Node name for S&R": "TextEncodeQwenImageEditPlus",
            "ue_properties": {
              "widget_ue_connectable": {
                "prompt": true
              },
              "version": "7.5.2",
              "input_ue_unconnectable": {}
            },
            "enableTabs": false,
            "tabWidth": 65,
            "tabXOffset": 10,
            "hasSecondTab": false,
            "secondTabText": "Send Back",
            "secondTabOffset": 80,
            "secondTabWidth": 65
          },
          "widgets_values": [
            "<sks> front view low-angle shot close-up"
          ],
          "color": "#232",
          "bgcolor": "#353"
        },
        {
          "id": 19,
          "type": "VNCCS_VisualPositionControl",
          "pos": [
            -135.64643889289317,
            113.93573315518134
          ],
          "size": [
            377.25527938354924,
            400.69737743530504
          ],
          "flags": {},
          "order": 6,
          "mode": 0,
          "inputs": [],
          "outputs": [
            {
              "name": "prompt",
              "type": "STRING",
              "links": [
                30
              ]
            }
          ],
          "properties": {
            "cnr_id": "vnccs-utils",
            "ver": "e8899e8fda5e72744198efecdc6f74f7d88a3b6a",
            "Node name for S&R": "VNCCS_VisualPositionControl",
            "ue_properties": {
              "widget_ue_connectable": {
                "camera_data": true
              },
              "version": "7.5.2",
              "input_ue_unconnectable": {}
            },
            "ttNbgOverride": {
              "color": "#332922",
              "bgcolor": "#593930",
              "groupcolor": "#b06634"
            }
          },
          "widgets_values": [
            "{\"azimuth\":225,\"elevation\":-30,\"distance\":\"close-up\",\"include_trigger\":true}",
            ""
          ],
          "color": "#332922",
          "bgcolor": "#593930"
        },
        {
          "id": 12,
          "type": "LoadImage",
          "pos": [
            -1045.7126666666695,
            -374.68955555555544
          ],
          "size": [
            850,
            1220
          ],
          "flags": {},
          "order": 7,
          "mode": 0,
          "inputs": [
            {
              "label": "图像",
              "name": "image",
              "type": "COMBO",
              "widget": {
                "name": "image"
              },
              "link": null
            },
            {
              "label": "上传",
              "name": "upload",
              "type": "IMAGEUPLOAD",
              "widget": {
                "name": "upload"
              },
              "link": null
            }
          ],
          "outputs": [
            {
              "label": "图像",
              "name": "IMAGE",
              "type": "IMAGE",
              "links": [
                10
              ]
            },
            {
              "label": "遮罩",
              "name": "MASK",
              "type": "MASK",
              "links": null
            }
          ],
          "properties": {
            "cnr_id": "comfy-core",
            "ver": "0.5.1",
            "Node name for S&R": "LoadImage",
            "ue_properties": {
              "widget_ue_connectable": {
                "image": true,
                "upload": true
              },
              "version": "7.5.2",
              "input_ue_unconnectable": {}
            },
            "enableTabs": false,
            "tabWidth": 65,
            "tabXOffset": 10,
            "hasSecondTab": false,
            "secondTabText": "Send Back",
            "secondTabOffset": 80,
            "secondTabWidth": 65,
            "ttNbgOverride": {
              "color": "#332922",
              "bgcolor": "#593930",
              "groupcolor": "#b06634"
            },
            "#sdppp_variant": "default",
            "#sdppp_simple_content": "canvas",
            "#sdppp_simple_mask": "canvas",
            "#sdppp_simple_boundary": "canvas",
            "#sdppp_label": ""
          },
          "widgets_values": [
            "微信图片_20260515114607_5418_3.png",
            "image"
          ],
          "color": "#332922",
          "bgcolor": "#593930"
        }
      ],
      "links": [
        [
          1,
          15,
          0,
          1,
          0,
          "MODEL"
        ],
        [
          2,
          5,
          0,
          2,
          0,
          "CONDITIONING"
        ],
        [
          3,
          13,
          0,
          3,
          0,
          "CONDITIONING"
        ],
        [
          4,
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    1 条回复 最后回复
    2
    • sirwangS 离线
      sirwangS 离线
      sirwang
      编写于 最后由 编辑
      #29

      把大美女也发出来。这是baidu图片找的。版权不是我的。

      zhuyin.jpeg

      1 条回复 最后回复
      0
      • sirwangS 离线
        sirwangS 离线
        sirwang
        编写于 最后由 编辑
        #30

        182a112a-320e-44e6-8184-ae9ab1a3c700-image.jpeg

        模型用的官方原版的模型,没有量化。 下载地址:https://huggingface.co/Qwen/Qwen3.6-27B/tree/main 一共 55.6G

        1 条回复 最后回复
        1
        • 墙内人墙 离线
          墙内人墙 离线
          墙内人
          编写于 最后由 墙内人 编辑
          #31

          厉害了兄弟,全网最宝贵资料,简直就是外星人捕捉现场,稀有

          1 条回复 最后回复
          1
          • terryT terry 固定了该主题
          • terryT 离线
            terryT 离线
            terry
            编写于 最后由 编辑
            #32

            不错,非常好的参考数据,我也不知道论坛有上传文件有什么要求,我后台开放了的,zip,gz后缀文件,应该都可以传。

            油管:https://www.youtube.com/@抡锤者

            sirwangS 1 条回复 最后回复
            0
            • terryT terry

              不错,非常好的参考数据,我也不知道论坛有上传文件有什么要求,我后台开放了的,zip,gz后缀文件,应该都可以传。

              sirwangS 离线
              sirwangS 离线
              sirwang
              编写于 最后由 编辑
              #33

              @terry 应该是我上传工作流的 .json 有安全风险,不让上传吧。没事的。

              1 条回复 最后回复
              0
              • terryT 离线
                terryT 离线
                terry
                编写于 最后由 编辑
                #34

                是我不让上传json文件,压缩成zip即可

                油管:https://www.youtube.com/@抡锤者

                sirwangS 1 条回复 最后回复
                0
                • terryT terry

                  是我不让上传json文件,压缩成zip即可

                  sirwangS 离线
                  sirwangS 离线
                  sirwang
                  编写于 最后由 编辑
                  #35

                  @terry 没问题的, 代码已经上传了, 编辑器的插入代码一样。 以后压缩再说吧。谢谢。

                  1 条回复 最后回复
                  0
                  • 李明李 离线
                    李明李 离线
                    李明
                    编写于 最后由 编辑
                    #36

                    弱弱问一句,Intel B70 32G vs 4080s 32G,哪个算力高?好像价格差30%

                    sirwangS 1 条回复 最后回复
                    0
                    • A 离线
                      A 离线
                      applejuice
                      编写于 最后由 applejuice 编辑
                      #37

                      4080s 带宽比较大 算例更好 应该比较好
                      4080 快过3090
                      B70 不如3090
                      4080还支持FP8
                      缺点没有保家

                      以上资料来源都是AI

                      1 条回复 最后回复
                      0
                      • sirwangS sirwang

                        手里有INTEL 的 B70PRO 显卡,新发布的 32G显存。
                        可以用comfyui,用 z-image 生图,会强过4090, 但LTX/WAN上边,没办法720视频,适配的一塌糊涂。我都快没有信心去测试了。 comfyui也没办法更新。我正在调试。调试完之后第一时间来发报告。

                        V 离线
                        V 离线
                        vosrock
                        编写于 最后由 编辑
                        #38

                        @sirwang z-image不是几秒就能出一张图嘛,几秒和十来秒差异不大的,所以别用ZIMAGE来测试,因为都很快

                        sirwangS 1 条回复 最后回复
                        0
                        • V vosrock

                          @sirwang z-image不是几秒就能出一张图嘛,几秒和十来秒差异不大的,所以别用ZIMAGE来测试,因为都很快

                          sirwangS 离线
                          sirwangS 离线
                          sirwang
                          编写于 最后由 编辑
                          #39

                          @vosrock 需要参考图然后用文生图的。不止是文生图。

                          1 条回复 最后回复
                          0
                          • 李明李 李明

                            弱弱问一句,Intel B70 32G vs 4080s 32G,哪个算力高?好像价格差30%

                            sirwangS 离线
                            sirwangS 离线
                            sirwang
                            编写于 最后由 编辑
                            #40

                            @李明 4080S的CUDA强,目前看来。如果不介意钱,就买4080S。

                            V 1 条回复 最后回复
                            0
                            • sirwangS 离线
                              sirwangS 离线
                              sirwang
                              编写于 最后由 编辑
                              #41

                              3f9c8453-1dad-4e62-9799-67586010c458-image.jpeg
                              e3721e7a-9ba0-4372-a6d4-28b76fb519b3-image.jpeg

                              8并发。 40轮。 压下来了,相当帅。差不多180 token/s,我个人觉得已经超过我的期望值了。

                              1 条回复 最后回复
                              0
                              • 墙内人墙 离线
                                墙内人墙 离线
                                墙内人
                                编写于 最后由 编辑
                                #42

                                外星人观摩现场,很少看到如此详细的英特尔显卡实测数据

                                1 条回复 最后回复
                                0
                                • Gerry WangG 离线
                                  Gerry WangG 离线
                                  Gerry Wang
                                  编写于 最后由 编辑
                                  #43

                                  你的这个信息很有价值,如果仅仅生图就能强过4090的话,已经可以有很多本地的事情能做了。LTX/WAN这边,不知道480P的测试数据如何?如果480P已经可行的话,对于一些手机短视频,我觉得已经满足了。

                                  1 条回复 最后回复
                                  0
                                  • M 离线
                                    M 离线
                                    mraksugar
                                    编写于 最后由 编辑
                                    #44

                                    @sirwang 请继续发布inter显卡生态测试到的边界信息,谢谢。

                                    1 条回复 最后回复
                                    0
                                    • sirwangS 离线
                                      sirwangS 离线
                                      sirwang
                                      编写于 最后由 编辑
                                      #45

                                      还在测啥? 以后就是comfyui 吧,我得换版块发帖了吧

                                      terryT 1 条回复 最后回复
                                      0
                                      • K 离线
                                        K 离线
                                        kaifan
                                        编写于 最后由 编辑
                                        #46

                                        分享一下单卡跑llmscaler数据
                                        周末把 Qwen3.6-27B 调到了一个对于 Agentic Loop 来说还算能接受的状态。比较系统的跑了一下单请求和并行 5 rep的benchmark。pp速度还可以,但 tg还是有点慢。不过配合 vLLM 的 continuous batching,并行 token 生成整体还比较稳定。目前专门用来给Hermes agent的delegate task去收集代码库context打下手

                                        目前唯一比较大的问题是:KV Cache 必须使用 BF16,才能达到可用的 token generation 速度,但ctx就只有43000了。另外还需要骗 vLLM,让它识别 layer architecture。希望未来能有优化过的 FP8 dequant kernel去支持fp8的kvcache。fp8的dequant比Q8_0慢很多,可惜官方docker的vllm版本还不支持除了fp8和bf16以外的kvcache dtype。可惜它和7900xtx都没有fp8的硬件支持,好像r9700有。另外autoround质量还是稍微比不过Q4的gguf

                                        硬件比较旧 64g的ddr4 虽然比较慢,但总比 pcie4x16 快。proxmox 9.1

                                        vLLM 单请求 qwen/qwen3.6-27b(int4 AutoRound):

                                        PP TTFT:1,685 ms

                                        PP2048 TPS:1,686 ± 66 tok/s

                                        TG512:13.7 ± 1.4 tok/s

                                        并行测试 pp2048 tg512
                                        Conc: 1
                                        • TTFT(ms): 1,261
                                        • Prefill(tok/s): 1,400
                                        • Decode(tok/s): 13.3
                                        • Output(tok/s): 12.9

                                        • Conc: 2
                                        • TTFT(ms): 1,907
                                        • Prefill(tok/s): 925
                                        • Decode(tok/s): 12.9
                                        • Output(tok/s): 24.7

                                        • Conc: 4
                                        • TTFT(ms): 3,319
                                        • Prefill(tok/s): 532
                                        • Decode(tok/s): 12.7
                                        • Output(tok/s): 46.7

                                        • Conc: 8
                                        • TTFT(ms): 6,231
                                        • Prefill(tok/s): 283
                                        • Decode(tok/s): 11.9
                                        • Output(tok/s): 82.7

                                        docker run 命令:

                                        docker run -it --rm --name vllmb70 --ipc=host --shm-size=32g
                                        --device=/dev/dri:/dev/dri --privileged -p 1234:8000
                                        -v ~/.cache/huggingface:/root/.cache/huggingface
                                        -e VLLM_TARGET_DEVICE=xpu
                                        --entrypoint /bin/bash intel/llm-scaler-vllm:0.14.0-b8.2.1 -c "
                                        source /opt/intel/oneapi/setvars.sh --force &&
                                        sed -i 's/image_processor.max_pixels/getattr(image_processor, "max_pixels", 12845056)/g'
                                        /usr/local/lib/python3.12/dist-packages/vllm/model_executor/models/qwen2_vl.py &&
                                        python3 -m vllm.entrypoints.openai.api_server
                                        --model Intel/Qwen3.6-27B-int4-AutoRound
                                        --tokenizer Qwen/Qwen3.6-27B
                                        --served-model-name qwen/qwen3.6-27b
                                        --kv-cache-dtype auto
                                        --max-model-len 65536
                                        --gpu-memory-utilization 0.9
                                        --enable-auto-tool-choice
                                        --tool-call-parser qwen3_xml
                                        --allow-deprecated-quantization
                                        --trust-remote-code
                                        --port 8000
                                        --tensor-parallel-size 1
                                        --pipeline-parallel-size 1
                                        --enforce-eager
                                        "

                                        也跑了一下ltx2.3 full gpu offload比4070需要dynamic loading快10%左右 custom node很多不支持 暂时不值得折腾

                                        sirwangS 1 条回复 最后回复
                                        0
                                        • K kaifan

                                          分享一下单卡跑llmscaler数据
                                          周末把 Qwen3.6-27B 调到了一个对于 Agentic Loop 来说还算能接受的状态。比较系统的跑了一下单请求和并行 5 rep的benchmark。pp速度还可以,但 tg还是有点慢。不过配合 vLLM 的 continuous batching,并行 token 生成整体还比较稳定。目前专门用来给Hermes agent的delegate task去收集代码库context打下手

                                          目前唯一比较大的问题是:KV Cache 必须使用 BF16,才能达到可用的 token generation 速度,但ctx就只有43000了。另外还需要骗 vLLM,让它识别 layer architecture。希望未来能有优化过的 FP8 dequant kernel去支持fp8的kvcache。fp8的dequant比Q8_0慢很多,可惜官方docker的vllm版本还不支持除了fp8和bf16以外的kvcache dtype。可惜它和7900xtx都没有fp8的硬件支持,好像r9700有。另外autoround质量还是稍微比不过Q4的gguf

                                          硬件比较旧 64g的ddr4 虽然比较慢,但总比 pcie4x16 快。proxmox 9.1

                                          vLLM 单请求 qwen/qwen3.6-27b(int4 AutoRound):

                                          PP TTFT:1,685 ms

                                          PP2048 TPS:1,686 ± 66 tok/s

                                          TG512:13.7 ± 1.4 tok/s

                                          并行测试 pp2048 tg512
                                          Conc: 1
                                          • TTFT(ms): 1,261
                                          • Prefill(tok/s): 1,400
                                          • Decode(tok/s): 13.3
                                          • Output(tok/s): 12.9

                                          • Conc: 2
                                          • TTFT(ms): 1,907
                                          • Prefill(tok/s): 925
                                          • Decode(tok/s): 12.9
                                          • Output(tok/s): 24.7

                                          • Conc: 4
                                          • TTFT(ms): 3,319
                                          • Prefill(tok/s): 532
                                          • Decode(tok/s): 12.7
                                          • Output(tok/s): 46.7

                                          • Conc: 8
                                          • TTFT(ms): 6,231
                                          • Prefill(tok/s): 283
                                          • Decode(tok/s): 11.9
                                          • Output(tok/s): 82.7

                                          docker run 命令:

                                          docker run -it --rm --name vllmb70 --ipc=host --shm-size=32g
                                          --device=/dev/dri:/dev/dri --privileged -p 1234:8000
                                          -v ~/.cache/huggingface:/root/.cache/huggingface
                                          -e VLLM_TARGET_DEVICE=xpu
                                          --entrypoint /bin/bash intel/llm-scaler-vllm:0.14.0-b8.2.1 -c "
                                          source /opt/intel/oneapi/setvars.sh --force &&
                                          sed -i 's/image_processor.max_pixels/getattr(image_processor, "max_pixels", 12845056)/g'
                                          /usr/local/lib/python3.12/dist-packages/vllm/model_executor/models/qwen2_vl.py &&
                                          python3 -m vllm.entrypoints.openai.api_server
                                          --model Intel/Qwen3.6-27B-int4-AutoRound
                                          --tokenizer Qwen/Qwen3.6-27B
                                          --served-model-name qwen/qwen3.6-27b
                                          --kv-cache-dtype auto
                                          --max-model-len 65536
                                          --gpu-memory-utilization 0.9
                                          --enable-auto-tool-choice
                                          --tool-call-parser qwen3_xml
                                          --allow-deprecated-quantization
                                          --trust-remote-code
                                          --port 8000
                                          --tensor-parallel-size 1
                                          --pipeline-parallel-size 1
                                          --enforce-eager
                                          "

                                          也跑了一下ltx2.3 full gpu offload比4070需要dynamic loading快10%左右 custom node很多不支持 暂时不值得折腾

                                          sirwangS 离线
                                          sirwangS 离线
                                          sirwang
                                          编写于 最后由 编辑
                                          #47

                                          @kaifan 请问这是啥卡的数据?!

                                          K 1 条回复 最后回复
                                          0

                                          你好!看起来您对这段对话很感兴趣,但您还没有一个账号。

                                          厌倦了每次访问都刷到同样的帖子?您注册账号后,您每次返回时都能精准定位到您上次浏览的位置,并可选择接收新回复通知(通过邮件或推送通知)。您还能收藏书签、为帖子顶,向社区成员表达您的欣赏。

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