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How to deploy multimodal models on edge devices with ONNX Runtime and CoreML?

Computer Vision & Multimodal AI · 2 saved versions

Version 1 (Edit)

Edited by Aravind Patel · Aug 23, 2026 5:18 PM

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Title snapshot

How to deploy multimodal models on edge devices with ONNX Runtime and CoreML?

Summary snapshot

Model quantization (INT4/INT8), memory pruning, and hardware acceleration on mobile GPUs.

Content snapshot

### Deployment Recipe Convert PyTorch model to ONNX, apply INT8 quantization, and execute using CoreML Execution Provider on Apple Silicon or TensorRT on Nvidia Jetson.

Source snapshot

https://onnxruntime.ai/

Version 1 (Original Post)

Published by Aravind Patel · Aug 9, 2026 5:37 AM

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Post originally created and published to the Global Hub.

Original Title

How to deploy multimodal models on edge devices with ONNX Runtime and CoreML?

Original Summary

Model quantization (INT4/INT8), memory pruning, and hardware acceleration on mobile GPUs.

Original Content

### Deployment Recipe Convert PyTorch model to ONNX, apply INT8 quantization, and execute using CoreML Execution Provider on Apple Silicon or TensorRT on Nvidia Jetson.

Original Sources

https://onnxruntime.ai/