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 Linux

Evaluation Target

vivo X300Raspberry Pi 5AWS Graviton G4Alif DK-E8

Arm Technology

No Arm technologies available

Task

Image ClassificationObject DetectionImage SegmentationText GenerationKeypoint DetectionZero Shot Image Classification

Vendor

Runtime

Quantization

Format

Execution Backend

Model Size

≤434.4 MB

Average Memory

≤319 MB

Peak Memory

≤947 MB
    26 results

    DDRNet-23-Slim INT8

    Raspberry Pi 5

    DDRNet-23-Slim semantic segmentation optimized using static INT8 post-training quantization (PTQ) with symmetric per-cha

    image-segmentation5.9 MBExecuTorch

    YOLO11n-Pose INT8

    Raspberry Pi 5

    This is an INT8-quantized version of yolo11n-pose (Ultralytics) optimized for edge deployment on ARM devices using Execu

    keypoint-detection5.4 MBExecuTorch

    ERNIE-4.5-0.3B-PT Q4_K_M

    Raspberry Pi 5

    ERNIE-4.5-0.3B-PT optimized for text generation in GGUF format with the llama.cpp runtime, targeting Arm-based Edge Linu

    text-generation240.6 MBllama.cpp

    MobileSAM INT8

    Raspberry Pi 5

    An INT8-quantized, lightweight segment-anything image segmentation family with a compact TinyViT backbone, designed for

    image-segmentation22.8 MBExecuTorch

    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

    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

    Qwen3-0.6B-Base Q4_K_M

    Raspberry Pi 5

    This is a Q4_K_M k-quantized GGUF build of Qwen/Qwen3-0.6B-Base — the base pretrained checkpoint, not the instruct varia

    text-generation434.4 MBllama.cpp

    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

    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

    PIDNet-S INT8

    Raspberry Pi 5

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

    image-segmentation7.9 MBExecuTorch

    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

    CLIP ViT-B/32 INT8

    Raspberry Pi 5

    A quantized vision-language model for zero-shot image classification, pairing an INT8 vision encoder with an FP32 text e

    zero-shot-image-classification343.3 MBExecuTorch

    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

    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

    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

    YOLOv7-seg INT8 (V2)

    Raspberry Pi 5

    This is an INT8-quantized version of YOLOv7-seg optimized for edge deployment on ARM devices using ExecuTorch with the X

    image-segmentation42.8 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