---
title: YOLO26 Deployment Options Compared
comments: true
description: Learn about YOLO26's diverse deployment options to maximize your model's performance. Explore PyTorch, TensorRT, OpenVINO, LiteRT, and more!
keywords: YOLO26, deployment options, export formats, PyTorch, TensorRT, OpenVINO, LiteRT, ONNX, CoreML, edge AI, NPU, model deployment
---

# Comparative Analysis of YOLO26 Deployment Options

YOLO26 supports more than 20 deployment options, each tuned for a different runtime, hardware target, or platform — from PyTorch and [ONNX](../integrations/onnx.md) to [TensorRT](../integrations/tensorrt.md), [OpenVINO](../integrations/openvino.md), [CoreML](../integrations/coreml.md), and dedicated edge-NPU formats. Picking the right one balances inference speed, hardware constraints, and ease of integration. This guide compares every option so you can choose the best fit for your application, then move on to [model deployment best practices](./model-deployment-practices.md) to deploy it reliably.

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Deployment is the stage in the [computer vision project workflow](./steps-of-a-cv-project.md) where a trained model starts doing real work, so the format you export to has a direct impact on speed, cost, and portability.

## How to Select the Right Deployment Option for Your YOLO26 Model

When it's time to deploy your YOLO26 model, selecting a suitable export format is very important. As outlined in the [Ultralytics YOLO26 export documentation](../modes/export.md#usage-examples), the `model.export()` function converts your trained model into a variety of formats tailored to diverse environments and performance requirements.

The ideal format depends on your model's intended operational context and hardware.

!!! tip "Skip the manual export"

    For managed deployment without manual export, [Ultralytics Platform](https://platform.ultralytics.com) provides ready-to-use [inference endpoints](../platform/deploy/endpoints.md) with auto-scaling across 42 global regions.

## YOLO26's Deployment Options

Here is a short description of each format and when to reach for it. For the full export walkthrough, see the [export documentation](../modes/export.md); for the side-by-side criteria, jump to the [comparison table](#deployment-options-compared).

- **PyTorch** (`.pt`): The native training and inference format, offering maximum flexibility and GPU acceleration through NVIDIA CUDA or [AMD ROCm](../integrations/amd.md) — ideal for research and prototyping with no export step required.
- **[TorchScript](../integrations/torchscript.md)** (`torchscript`): Serializes the model for a Python-free C++ runtime, suited to production systems where Python is unavailable.
- **[ONNX](../integrations/onnx.md)** (`onnx`): A framework-agnostic interchange format with broad cross-platform and hardware support through ONNX Runtime.
- **[OpenVINO](../integrations/openvino.md)** (`openvino`): Intel's toolkit for optimized inference on Intel CPUs, integrated GPUs, and NPUs, common in IoT and [edge computing](https://www.ultralytics.com/glossary/edge-computing).
- **[TensorRT](../integrations/tensorrt.md)** (`engine`): NVIDIA's high-performance runtime delivering top-tier GPU inference with FP16 and INT8 optimization.
- **[CoreML](../integrations/coreml.md)** (`coreml`): Apple's on-device format for iOS, macOS, watchOS, and tvOS, using the Apple Neural Engine.
- **[TF SavedModel](../integrations/tf-savedmodel.md)** (`saved_model`): TensorFlow's standard format for scalable server-side serving with TensorFlow Serving.
- **[TF GraphDef](../integrations/tf-graphdef.md)** (`pb`): A frozen static-graph TensorFlow format for environments that need a fixed computation graph.
- **[TF Edge TPU](../integrations/edge-tpu.md)** (`edgetpu`): Compiles `.tflite` models for Google Coral Edge TPU accelerators.
- **[LiteRT](../integrations/litert.md)** (`litert`): Google's on-device runtime (formerly TensorFlow Lite) for mobile, embedded, and browser inference from a single `.tflite` model, with FP32 and INT8 support and in-browser execution via LiteRT.js.
- **[PaddlePaddle](../integrations/paddlepaddle.md)** (`paddle`): Baidu's [deep learning](https://www.ultralytics.com/glossary/deep-learning-dl) framework, popular in China, with broad hardware support.
- **[MNN](../integrations/mnn.md)** (`mnn`): A lightweight, high-performance inference engine optimized for mobile and embedded ARM and x86-64 systems.
- **[NCNN](../integrations/ncnn.md)** (`ncnn`): A high-performance, lightweight inference framework tuned for mobile ARM devices.
- **[Sony IMX500](../integrations/sony-imx500.md)** (`imx`): Exports for Sony's IMX500 intelligent vision sensor with on-chip processing, such as the Raspberry Pi AI Camera.
- **[Rockchip RKNN](../integrations/rockchip-rknn.md)** (`rknn`): Targets Rockchip NPUs on embedded boards with FP16 and INT8 quantization.
- **[ExecuTorch](../integrations/executorch.md)** (`executorch`): PyTorch's native on-device runtime for mobile (iOS and Android) and embedded systems via XNNPACK.
- **[Axelera AI](../integrations/axelera.md)** (`axelera`): Compiles for Axelera's Metis AIPU (up to 856 TOPS) over PCIe or M.2 for high-throughput edge inference.
- **[DEEPX](../integrations/deepx.md)** (`deepx`): Targets DEEPX NPU hardware with INT8 quantization for embedded edge inference.
- **[Qualcomm QNN](../integrations/qnn.md)** (`qnn`): On-device inference on Snapdragon Hexagon NPU, Adreno GPU, and CPU through the Qualcomm AI stack.

The [Hailo integration](../integrations/hailo.md) exports YOLO detection, segmentation, semantic segmentation, depth estimation, classification, pose, and OBB models directly to Hailo HEF; see its compatibility table for validated models and targets. The [Ambarella integration](../integrations/ambarella.md) follows an ONNX-first workflow and compiles ONNX exports to the AmbaPB format with Ambarella's CVflow toolchain for CVflow® SoCs such as the CV72, optionally using SpongeTorch compression-aware training. The [Huawei Ascend integration](../integrations/ascend.md) compiles ONNX exports to the `.om` offline format with the CANN ATC compiler for FP16 inference on Ascend AI Processors.

## Deployment Options Compared

The following table summarizes the deployment options for YOLO26 models across the criteria that usually drive the choice. For an in-depth look at each format, see the [export formats documentation](../modes/export.md#export-formats).

| Deployment Option | Performance Benchmarks                          | Compatibility and Integration                  | Community Support and Ecosystem               | Case Studies                                | Maintenance and Updates                        | Security Considerations                           | Hardware Acceleration               |
| ----------------- | ----------------------------------------------- | ---------------------------------------------- | --------------------------------------------- | ------------------------------------------- | ---------------------------------------------- | ------------------------------------------------- | ----------------------------------- |
| PyTorch           | Good flexibility; may trade off raw performance | Excellent with Python libraries                | Extensive resources and community             | Research and prototypes                     | Regular, active development                    | Dependent on deployment environment               | CUDA support for GPU acceleration   |
| TorchScript       | Better for production than PyTorch              | Smooth transition from PyTorch to C++          | Specialized but narrower than PyTorch         | Industry where Python is a bottleneck       | Consistent updates with PyTorch                | Improved security without full Python             | Inherits CUDA support from PyTorch  |
| ONNX              | Variable depending on runtime                   | High across different frameworks               | Broad ecosystem, supported by many orgs       | Flexibility across ML frameworks            | Regular updates for new operations             | Ensure secure conversion and deployment practices | Various hardware optimizations      |
| OpenVINO          | Optimized for Intel hardware                    | Best within Intel ecosystem                    | Solid in computer vision domain               | IoT and edge with Intel hardware            | Regular updates for Intel hardware             | Robust features for sensitive applications        | Tailored for Intel hardware         |
| TensorRT          | Top-tier on NVIDIA GPUs                         | Best for NVIDIA hardware                       | Strong network through NVIDIA                 | Real-time video and image inference         | Frequent updates for new GPUs                  | Emphasis on security                              | Designed for NVIDIA GPUs            |
| CoreML            | Optimized for on-device Apple hardware          | Exclusive to Apple ecosystem                   | Strong Apple and developer support            | On-device ML on Apple products              | Regular Apple updates                          | Focus on privacy and security                     | Apple neural engine and GPU         |
| TF SavedModel     | Scalable in server environments                 | Wide compatibility in TensorFlow ecosystem     | Large support due to TensorFlow popularity    | Serving models at scale                     | Regular updates by Google and community        | Robust features for enterprise                    | Various hardware accelerations      |
| TF GraphDef       | Stable for static computation graphs            | Integrates well with TensorFlow infrastructure | Resources for optimizing static graphs        | Scenarios requiring static graphs           | Updates alongside TensorFlow core              | Established TensorFlow security practices         | TensorFlow acceleration options     |
| TF Edge TPU       | Optimized for Google's Edge TPU hardware        | Exclusive to Edge TPU devices                  | Growing with Google and third-party resources | IoT devices requiring real-time processing  | Improvements for new Edge TPU hardware         | Google's robust IoT security                      | Custom-designed for Google Coral    |
| LiteRT            | Speed and efficiency on mobile/embedded/web     | Mobile, embedded, edge, and browser support    | Robust community, Google backed               | On-device apps across Android, iOS, and web | Latest on-device runtime features              | Secure on-device and in-browser inference         | GPU, DSP, and WebGPU acceleration   |
| PaddlePaddle      | Competitive, easy to use and scalable           | Baidu ecosystem, wide application support      | Rapidly growing, especially in China          | Chinese market and language processing      | Focus on Chinese AI applications               | Emphasizes data privacy and security              | Including Baidu's Kunlun chips      |
| MNN               | High-performance for mobile devices             | Mobile and embedded ARM systems and X86-64 CPU | Mobile/embedded ML community                  | Mobile systems efficiency                   | High performance maintenance on mobile devices | On-device security advantages                     | ARM CPUs and GPUs optimizations     |
| NCNN              | Optimized for mobile ARM-based devices          | Mobile and embedded ARM systems                | Niche but active mobile/embedded ML community | Android and ARM systems efficiency          | High performance maintenance on ARM            | On-device security advantages                     | ARM CPUs and GPUs optimizations     |
| Sony IMX500       | On-sensor inference at very low power           | Sony IMX500 sensor, Raspberry Pi AI Camera     | Sony AITRIOS ecosystem                        | On-camera edge AI                           | Sony SDK and MCT toolchain updates             | Data stays on the sensor                          | Sony IMX500 on-chip accelerator     |
| Rockchip RKNN     | Optimized for Rockchip NPUs                     | Rockchip SoC boards (e.g. RK3588)              | Rockchip developer community                  | Embedded SBC and edge devices               | Rockchip RKNN-Toolkit updates                  | On-device local inference                         | Rockchip NPU                        |
| ExecuTorch        | Efficient on-device PyTorch runtime             | iOS, Android, embedded via XNNPACK             | Backed by the PyTorch project                 | Mobile and embedded apps                    | Maintained alongside PyTorch                   | On-device inference keeps data local              | XNNPACK and mobile CPU/GPU backends |
| Axelera AI        | Very high throughput (up to 856 TOPS)           | Metis AIPU over PCIe or M.2                    | Axelera Voyager SDK                           | High-throughput edge inference              | Axelera SDK updates                            | On-premises edge inference                        | Axelera Metis AIPU                  |
| DEEPX             | INT8-optimized NPU inference                    | DEEPX NPU hardware                             | DEEPX developer tools (dx_com, dx_engine)     | Embedded edge inference                     | DEEPX SDK and runtime updates                  | On-device local inference                         | DEEPX NPU                           |
| Qualcomm QNN      | Fast on-device Snapdragon inference             | Snapdragon Hexagon NPU, Adreno GPU, CPU        | Qualcomm AI Hub ecosystem                     | Mobile and edge Snapdragon devices          | Qualcomm AI stack (QAIRT) updates              | On-device inference keeps data local              | Snapdragon Hexagon NPU              |

This comparison gives you a high-level overview. For deployment, weigh the specific requirements and constraints of your project against each option, and consult the linked integration guide for the format you choose.

## Conclusion

YOLO26's wide range of export formats lets you tailor a model to almost any environment, from a cloud GPU server to an on-sensor edge camera. Once you have picked a format, follow [model deployment best practices](./model-deployment-practices.md) for optimization, troubleshooting, and security, and lean on the [Ultralytics community](https://github.com/orgs/ultralytics/discussions) when you hit a snag.

## FAQ

### What are the deployment options available for YOLO26 on different hardware platforms?

Ultralytics YOLO26 supports various deployment formats, each designed for specific environments and hardware platforms. Key formats include:

- **PyTorch** for research and prototyping, with excellent Python integration.
- **TorchScript** for production environments where Python is unavailable.
- **ONNX** for cross-platform compatibility and hardware acceleration.
- **OpenVINO** for optimized performance on Intel hardware.
- **TensorRT** for high-speed inference on NVIDIA GPUs.

Each format has unique advantages. For a detailed walkthrough, see our [export process documentation](../modes/export.md#usage-examples).

### How do I improve the inference speed of my YOLO26 model on an Intel CPU?

To enhance inference speed on Intel CPUs, you can deploy your YOLO26 model using Intel's OpenVINO toolkit. OpenVINO offers significant performance boosts by optimizing models to leverage Intel hardware efficiently.

1. Convert your YOLO26 model to the OpenVINO format using the `model.export()` function.
2. Follow the detailed setup guide in the [Intel OpenVINO Export documentation](../integrations/openvino.md).

For more insights, check out our [blog post](https://www.ultralytics.com/blog/achieve-faster-inference-speeds-ultralytics-yolov8-openvino).

### Can I deploy YOLO26 models on mobile devices?

Yes, YOLO26 models can be deployed on mobile devices using [LiteRT](../integrations/litert.md) (formerly TensorFlow Lite) and [NCNN](../integrations/ncnn.md) for Android, and [CoreML](../integrations/coreml.md) or LiteRT for iOS. LiteRT is Google's on-device runtime for mobile and embedded devices and runs the same model across Android, iOS, and the browser, providing efficient on-device inference.

!!! example

    === "Python"

        ```python
        # Export command for NCNN format
        model.export(format="ncnn")
        ```

    === "CLI"

        ```bash
        # CLI command for NCNN export
        yolo export model=yolo26n.pt format=ncnn
        ```

For more details on deploying models to mobile, refer to our [LiteRT integration guide](../integrations/litert.md).

### What factors should I consider when choosing a deployment format for my YOLO26 model?

When choosing a deployment format for YOLO26, consider the following factors:

- **Performance**: Some formats like TensorRT provide exceptional speeds on NVIDIA GPUs, while OpenVINO is optimized for Intel hardware.
- **Compatibility**: ONNX offers broad compatibility across different platforms.
- **Ease of Integration**: Formats like CoreML or LiteRT are tailored for specific ecosystems like iOS and Android, respectively.
- **Community Support**: Formats like [PyTorch](https://www.ultralytics.com/glossary/pytorch) and TensorFlow have extensive community resources and support.

For a comparative analysis, refer to our [export formats documentation](../modes/export.md#export-formats).

### How can I deploy YOLO26 models in a web application?

To deploy YOLO26 models in a web application, you can use [LiteRT.js](https://developers.google.com/edge/litert/web), LiteRT's web runtime, which allows for running [machine learning](https://www.ultralytics.com/glossary/machine-learning-ml) models directly in the browser and Node.js. This approach eliminates the need for backend infrastructure and provides real-time performance.

1. Export the YOLO26 model to the LiteRT format.
2. Integrate the exported model into your web application using LiteRT.js.

For step-by-step instructions, refer to our [LiteRT integration guide](../integrations/litert.md).
