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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 LinuxEthos-U NPUMobile CPUCloud CPU

Evaluation Target

Raspberry Pi 5Alif DK-E8vivo X300AWS Graviton G4

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

≤88.6 MB

Average Memory

≤190 MB

Peak Memory

≤190 MB
    22 results

    ResNet-50 INT8 (per-tensor)

    Alif DK-E8

    A 50-layer residual convolutional neural network for 1000-class image classification, using INT8 per-tensor quantization

    image-classification15.1 MBExecuTorch

    GoogLeNet INT8

    Raspberry Pi 5

    An INT8-quantized Inception v1 image-classification model with per-channel weights and per-tensor activations. It accept

    image-classification6.8 MBExecuTorch

    Swin Tiny INT8

    Raspberry Pi 5

    An INT8-quantized hierarchical Vision Transformer for 1000-class image classification, using shifted-window attention fo

    image-classification29.9 MBExecuTorch

    Inception V3 INT8

    Alif DK-E8

    INT8-quantized image classification models based on a deep convolutional architecture with factorized and asymmetric con

    image-classification18.4 MBExecuTorch

    ResNet-18 INT8

    Alif DK-E8

    An INT8-quantized 18-layer residual convolutional network for 1000-class image classification, designed for efficient in

    image-classification7.7 MBExecuTorch

    DeiT-Tiny INT8

    vivo X300SME2

    This is an INT8-quantized version of facebook/deit-tiny-patch16-224 optimized for edge deployment on ARM devices using L

    image-classification7.2 MBLiteRT

    Swin-Tiny INT8

    vivo X300SME2

    An INT8-quantized hierarchical vision transformer for ImageNet-style image classification, using shifted-window self-att

    image-classification31.7 MBLiteRT

    MobileNetV3-Small INT8

    Raspberry Pi 5

    A compact INT8 image-classification CNN using inverted residual blocks, Squeeze-and-Excitation modules, and hard-swish a

    image-classification2.8 MBExecuTorch

    SqueezeNet 1.1 INT8

    Raspberry Pi 5

    An INT8-quantized, fire-module CNN for efficient 1000-class image classification, using per-channel weights and per-tens

    image-classification1.3 MBExecuTorch

    ViT-Base INT8

    Raspberry Pi 5

    INT8-quantized Vision Transformer for 1000-class image classification, using 16×16 image patches at 224×224 resolution.

    image-classification88 MBExecuTorch

    MobileNetV3-Large INT8

    Alif DK-E8

    An INT8-quantized image classification model based on a depthwise-separable convolutional architecture with squeeze-and-

    image-classification5.4 MBExecuTorch

    DeiT-Tiny INT8

    Alif DK-E8

    An INT8-quantized tiny Vision Transformer for 1,000-class image classification, using SmoothQuant to reduce model size w

    image-classification4.9 MBExecuTorch

    MobileNetV3-Small INT8

    vivo X300SME2

    Lightweight INT8 image classifier for 224×224 RGB inputs, producing logits for 1,000 ImageNet-style classes. Built for m

    image-classification3 MBLiteRT

    DeiT-Tiny INT8

    Raspberry Pi 5

    INT8-quantized compact vision transformer for 1000-class image classification, designed for efficient inference on ARM e

    image-classification6.9 MBExecuTorch

    Inception V3 INT8

    AWS Graviton G4

    INT8-quantized image-classification family for 1,000-class ImageNet-style recognition, using static quantization with pe

    image-classification24.2 MBExecuTorch

    ResNet-50 INT8 (per-channel)

    Alif DK-E8

    An INT8 per-channel quantized 50-layer residual network for 1000-class image classification, using residual connections

    image-classification22.8 MBExecuTorch

    ShuffleNetV2 x1.0 INT8

    Raspberry Pi 5

    A lightweight INT8-quantized convolutional image-classification model using channel split, depthwise convolution, and ch

    image-classification2.5 MBExecuTorch

    GoogLeNet INT8

    AWS Graviton G4

    INT8-quantized image classification models based on an Inception-style convolutional architecture, designed to reduce mo

    image-classification6.8 MBExecuTorch

    ViT-Base/16 (timm augreg) INT8

    vivo X300SME2

    An INT8-quantized Vision Transformer for 1000-class image classification, using a Base architecture with 16×16 image pat

    image-classification88.6 MBLiteRT

    Inception V3 INT8

    Raspberry Pi 5

    This is an INT8-quantized version of Inception V3 optimized for edge deployment on ARM devices using ONNX Runtime, profi

    image-classification24.9 MBONNX

    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