AI PORTAL
  • Models
  • Code
  • Tools

About the models on this Arm AI Portal

The Arm AI Portal (the “Portal”) provides details of AI models developed, optimized or tested for use on Arm-based platforms. Models listed on the Portal may have been developed by Arm or by third parties. Unless expressly stated otherwise, Arm did not develop or train the underlying third-party model and is not responsible for its original intended behavior or use.

Information accompanies each model listing and describes the model’s provenance, together with relevant information about the original third-party model, its developer and repository, where applicable. Inclusion on the Portal of a model developed by a third party or based on a third-party model does not constitute Arm’s endorsement or certification of that third-party model or any third-party work relating to it.

Except as expressly stated in the information accompanying a specific model, Arm makes no representation as to the accuracy, safety, security, non-infringement, legal compliance, suitability for production use or fitness for any particular purpose of any model, converted or optimized version, or related outputs.

Inclusion of a model on the Portal does not itself grant you any rights to use that model. Use of each model is subject to the applicable license terms, usage restrictions and documentation. You are responsible for reviewing that information and independently evaluating the model, its outputs and its suitability for your intended use, including compliance with applicable legal, regulatory, safety and security requirements. Any use of or reliance on a model, its outputs or related materials is at your own risk. Where the Portal links to repositories or other materials controlled by third parties, those repositories and materials are outside Arm’s control and may change without notice.

Use of the Portal is subject to the Arm Website Terms and Conditions of Use.

Models

Filters

1

Device Class

Edge LinuxMobile CPU

Evaluation Target

Raspberry Pi 5vivo X300

Arm Technology

SME2

Task

Image ClassificationText GenerationObject DetectionImage SegmentationAutomatic Speech RecognitionFeature ExtractionImage To ImageText To SpeechDepth EstimationImage Text To TextKeypoint DetectionText To ImageTranslationZero Shot Image Classification

Vendor

Runtime

Quantization

Format

Execution Backend

Model Size

≤40.3 MB

Average Memory

≤295 MB

Peak Memory

≤391 MB
    10 results

    YOLOv9-S INT8

    Raspberry Pi 5

    An INT8-quantized single-stage object detection family for efficient edge inference on ARM devices. It targets 80-class

    object-detection12.4 MBExecuTorch

    YOLO26n FP16

    vivo X300SME2

    Lightweight object detector for COCO classes, packaged with FP16 weights for efficient edge AI workloads.

    object-detection10.9 MBLiteRT

    YOLO26n INT8 weight-only

    vivo X300SME2

    Real-time object detection bundle for LiteRT with int8 weight-only quantization and COCO labels.

    object-detection3.5 MBLiteRT

    rtdetr l INT8

    Raspberry Pi 5

    This is an INT8-quantized version of RT-DETR-L, a real-time end-to-end object detector that pairs a convolutional backbo

    object-detection40.3 MBONNX

    YOLO11n INT8

    Raspberry Pi 5

    This is an INT8-quantized version of YOLO11n optimized for edge deployment on ARM devices using ExecuTorch with the XNNP

    object-detection5 MBExecuTorch

    YOLO11n INT8

    vivo X300SME2

    This is an INT8-quantized version of YOLO11n optimized for edge deployment on ARM devices using LiteRT with the XNNPACK

    object-detection4.6 MBLiteRT

    YOLOv8s INT8

    Raspberry Pi 5

    An INT8-quantized single-stage object detector for 80 COCO classes, using an anchor-free architecture with 640×640 RGB i

    object-detection13.9 MBExecuTorch

    YOLO12-L INT8

    Raspberry Pi 5

    This is an INT8-quantized version of YOLO12-L optimized for edge deployment on ARM devices using ExecuTorch with the XNN

    object-detection35.5 MBExecuTorch

    YOLOv5s INT8

    Raspberry Pi 5

    An INT8-quantized single-stage object detection model for 80 COCO classes, using an anchor-based architecture with 640×6

    object-detection7.5 MBExecuTorch

    YOLOX-s INT8

    Raspberry Pi 5

    This is an INT8-quantized version of YOLOX-s optimized for edge deployment on ARM devices using ExecuTorch with the XNNP

    object-detection15 MBExecuTorch

    Develop on Arm with your AI coding assistant

    The Arm AI Portal MCP Server makes it faster to find optimized models, integrate code examples and deploy to your target device from your agentic coding assistant.

    User guide

    Run the following command in your terminal:

    codex mcp add arm-ai --url https://mcp.api.devplatform.arm.com/ai-portal