DOCS . ULTRALYTICS . COM {}

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We are analyzing https://docs.ultralytics.com/integrations/openvino/.

Title:
Intel OpenVINO Export - Ultralytics YOLO Docs
Description:
Learn to export YOLO11 models to OpenVINO format for up to 3x CPU speedup and hardware acceleration on Intel GPU and NPU.
Website Age:
11 years and 4 months (reg. 2014-02-13).

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Technology & Computing
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Custom-built

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🌟 Strong Traffic: 100k - 200k visitors per month

Based on our best estimate, this website will receive around 127,142 visitors per month in the current month.

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Keywords {🔍}

openvino, intel, model, inference, yolo, export, models, core, run, ultralytics, results, benchmarks, gpu, arc, format, hardware, cpu, ultra, exported, benchmark, formats, performance, detailed, pytorch, npu, load, series, yolon, python, int, dataset, integrated, onnx, cli, benefits, deployment, import, accuracy, documentation, intels, refer, speed, runtime, exporting, toolkit, highperformance, api, computing, tasks, code,

Topics {✒️}

intel arc a770 intel arc b580 intel® arc™ aims intel core cpu integrated gpu integrated npu detailed benchmark results intel® core® series detailed yolo11 benchmarks integrated intel gpu delivering cutting-edge solutions series underscores intel' detailed performance comparisons intel openvino export ai-enhanced graphics professionals leveraging ai ai-driven operations leverage intel gpus supported intel hardware ai-enhanced devices intel devices compatible traditional cpu series benchmark yolo11 models device ai inference gpu-accelerated workloads leading gpu brands openvino yolo11 benchmarks intel cpus core i9 intel® processors intel gpus 3x cpu speedup intel® hardware intel hardware ai workloads intel® chip consulting intel' detailed steps detailed information biases binary data real-time object detection efficient detection post-processing content creation reproduce ultralytics benchmarks hardware-accelerated av1 encoding neural processing unit high-power variants marked cpu inference

Questions {❓}

Can I benchmark YOLO11 models on different formats such as PyTorch, ONNX, and OpenVINO?
How can I run inference using a YOLO11 model exported to OpenVINO?
How do I export YOLO11 models to OpenVINO format?
What are the benefits of using OpenVINO with YOLO11 models?
Why should I choose Ultralytics YOLO11 over other models for OpenVINO export?

Schema {🗺️}

["Article","FAQPage"]:
      context:https://schema.org
      headline:OpenVINO
      image:
         https://github.com/ultralytics/docs/releases/download/0/openvino-ecosystem.avif
      datePublished:2023-11-12 02:49:37 +0100
      dateModified:2025-06-10 23:14:58 +0200
      author:
            type:Organization
            name:Ultralytics
            url:https://ultralytics.com/
      abstract:Learn to export YOLO11 models to OpenVINO format for up to 3x CPU speedup and hardware acceleration on Intel GPU and NPU.
      mainEntity:
            type:Question
            name:How do I export YOLO11 models to OpenVINO format?
            acceptedAnswer:
               type:Answer
               text:Exporting YOLO11 models to the OpenVINO format can significantly enhance CPU speed and enable GPU and NPU accelerations on Intel hardware. To export, you can use either Python or CLI as shown below: For more information, refer to the export formats documentation.
            type:Question
            name:What are the benefits of using OpenVINO with YOLO11 models?
            acceptedAnswer:
               type:Answer
               text:Using Intel's OpenVINO toolkit with YOLO11 models offers several benefits: For detailed performance comparisons, visit our benchmarks section.
            type:Question
            name:How can I run inference using a YOLO11 model exported to OpenVINO?
            acceptedAnswer:
               type:Answer
               text:After exporting a YOLO11n model to OpenVINO format, you can run inference using Python or CLI: Refer to our predict mode documentation for more details.
            type:Question
            name:Why should I choose Ultralytics YOLO11 over other models for OpenVINO export?
            acceptedAnswer:
               type:Answer
               text:Ultralytics YOLO11 is optimized for real-time object detection with high accuracy and speed. Specifically, when combined with OpenVINO, YOLO11 provides: For in-depth performance analysis, check our detailed YOLO11 benchmarks on different hardware.
            type:Question
            name:Can I benchmark YOLO11 models on different formats such as PyTorch, ONNX, and OpenVINO?
            acceptedAnswer:
               type:Answer
               text:Yes, you can benchmark YOLO11 models in various formats including PyTorch, TorchScript, ONNX, and OpenVINO. Use the following code snippet to run benchmarks on your chosen dataset: For detailed benchmark results, refer to our benchmarks section and export formats documentation.
Organization:
      name:Ultralytics
      url:https://ultralytics.com/
Question:
      name:How do I export YOLO11 models to OpenVINO format?
      acceptedAnswer:
         type:Answer
         text:Exporting YOLO11 models to the OpenVINO format can significantly enhance CPU speed and enable GPU and NPU accelerations on Intel hardware. To export, you can use either Python or CLI as shown below: For more information, refer to the export formats documentation.
      name:What are the benefits of using OpenVINO with YOLO11 models?
      acceptedAnswer:
         type:Answer
         text:Using Intel's OpenVINO toolkit with YOLO11 models offers several benefits: For detailed performance comparisons, visit our benchmarks section.
      name:How can I run inference using a YOLO11 model exported to OpenVINO?
      acceptedAnswer:
         type:Answer
         text:After exporting a YOLO11n model to OpenVINO format, you can run inference using Python or CLI: Refer to our predict mode documentation for more details.
      name:Why should I choose Ultralytics YOLO11 over other models for OpenVINO export?
      acceptedAnswer:
         type:Answer
         text:Ultralytics YOLO11 is optimized for real-time object detection with high accuracy and speed. Specifically, when combined with OpenVINO, YOLO11 provides: For in-depth performance analysis, check our detailed YOLO11 benchmarks on different hardware.
      name:Can I benchmark YOLO11 models on different formats such as PyTorch, ONNX, and OpenVINO?
      acceptedAnswer:
         type:Answer
         text:Yes, you can benchmark YOLO11 models in various formats including PyTorch, TorchScript, ONNX, and OpenVINO. Use the following code snippet to run benchmarks on your chosen dataset: For detailed benchmark results, refer to our benchmarks section and export formats documentation.
Answer:
      text:Exporting YOLO11 models to the OpenVINO format can significantly enhance CPU speed and enable GPU and NPU accelerations on Intel hardware. To export, you can use either Python or CLI as shown below: For more information, refer to the export formats documentation.
      text:Using Intel's OpenVINO toolkit with YOLO11 models offers several benefits: For detailed performance comparisons, visit our benchmarks section.
      text:After exporting a YOLO11n model to OpenVINO format, you can run inference using Python or CLI: Refer to our predict mode documentation for more details.
      text:Ultralytics YOLO11 is optimized for real-time object detection with high accuracy and speed. Specifically, when combined with OpenVINO, YOLO11 provides: For in-depth performance analysis, check our detailed YOLO11 benchmarks on different hardware.
      text:Yes, you can benchmark YOLO11 models in various formats including PyTorch, TorchScript, ONNX, and OpenVINO. Use the following code snippet to run benchmarks on your chosen dataset: For detailed benchmark results, refer to our benchmarks section and export formats documentation.

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