onnxruntime

Run ONNX model inference and training with hardware acceleration.

4|Updated May 6, 2026

Search Tags:#deployment #cross-platform #training #inference #model optimization #onnx

Ready to run

npx skills add https://github.com/jstzwj/ai-infra-plugins --skill onnxruntime

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: onnxruntime
Source: https://github.com/jstzwj/ai-infra-plugins/tree/main/plugins/onnxruntime/skills/onnxruntime
Command: npx skills add https://github.com/jstzwj/ai-infra-plugins --skill onnxruntime

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires onnxruntime, protobuf, numpy, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill enables efficient inference and training of ONNX models across multiple hardware backends, simplifying deployment and accelerating AI workflows.

Core Features & Use Cases

Cross-platform Deployment: Supports inference on CPUs, GPUs, NPUs, and edge devices with hardware-specific acceleration.
Model Optimization: Applies graph transformations, quantization, and format conversions for faster startup and lower memory footprint.
Use Case: Deploy a transformer model on server and mobile environments to perform real-time language understanding with optimized latency.

Quick Start

Invoke the onnxruntime skill to run a pre-trained image classification model on your dataset.

Frequently Asked Questions about onnxruntime

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I run ONNX model inference across different hardware backends?▼

ONNX model inference can run across CPUs, GPUs, NPUs, and edge devices using hardware-specific acceleration. This Skill configures the onnxruntime execution provider to deploy models on diverse platforms while maintaining optimized latency.

What's the best way to optimize ONNX models for lower memory footprint and faster startup?▼

Optimizing ONNX models for faster startup and lower memory footprint involves applying graph transformations, quantization, and format conversions. This Skill executes these model optimization techniques to reduce inference latency without altering the original model architecture.

Does onnxruntime support training or is it strictly for inference?▼

ONNX Runtime supports both high-performance inference and training. This Skill provides hardware-accelerated execution for ONNX models, allowing you to train models and perform real-time inference using the same backend environment.

Can I deploy a transformer model for real-time language understanding on mobile environments?▼

Deploying a transformer model on mobile environments for real-time language understanding is supported. This Skill simplifies cross-platform deployment to run optimized inference on edge devices and servers with hardware acceleration.

What Python dependencies do I need to install for ONNX model deployment?▼

ONNX model deployment requires the onnxruntime, protobuf, and numpy Python packages. These dependencies must be installed to execute the inference scripts and configure the hardware acceleration backends properly.