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ONNX Runtime GenAI supports multiple hardware acceleration providers to optimize model inference across different platforms. Each execution provider targets specific hardware and offers unique performance characteristics.

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Available Execution Providers

ONNX Runtime GenAI supports the following execution providers:

CUDA

NVIDIA GPU acceleration with comprehensive memory management

DirectML

Cross-vendor GPU acceleration on Windows platforms

OpenVINO

Intel hardware optimization for CPU, GPU, and NPU

QNN

Qualcomm NPU acceleration for edge and mobile devices

WebGPU

Browser-based GPU acceleration using WebGPU API

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Platform Compatibility Matrix

ProviderWindowsLinuxmacOSAndroidBrowser
CUDA✅✅❌❌❌
DirectML✅❌❌❌❌
OpenVINO✅✅✅❌❌
QNN✅✅❌✅❌
WebGPU✅✅✅❌✅
CPU✅✅✅✅✅

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Hardware Type Support

ProviderCPUGPUNPUTarget Hardware
CUDA❌✅❌NVIDIA GPUs
DirectML❌✅❌All DirectX 12 GPUs
OpenVINO✅✅✅Intel CPUs, iGPUs, NPUs
QNN❌❌✅Qualcomm Hexagon NPUs
WebGPU❌✅❌Browser-supported GPUs

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Performance Considerations

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Memory Management

Each provider handles memory differently:
  • CUDA: Device memory with host-pinned allocations for efficient transfers
  • DirectML: D3D12 resource management with upload/readback heaps
  • OpenVINO: CPU-accessible memory with optional device acceleration
  • QNN: CPU-accessible NPU memory
  • WebGPU: GPU buffers with async CPU-GPU synchronization

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Precision Support

All providers support full precision (FP32) inference.

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Provider Selection Guide

Choose the right provider based on your deployment scenario:

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Server Deployment

import onnxruntime_genai as og

# NVIDIA GPU server
config = og.Config(model_path)
config.clear_providers()
config.append_provider("cuda")
model = og.Model(config)

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Windows Desktop

import onnxruntime_genai as og

# Cross-vendor GPU support
config = og.Config(model_path)
config.clear_providers()
config.append_provider("dml")
model = og.Model(config)

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Edge Devices

import onnxruntime_genai as og

# Intel edge hardware
config = og.Config(model_path)
config.clear_providers()
config.append_provider("openvino")
config.set_provider_option("openvino", "device_type", "CPU")
model = og.Model(config)

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Mobile Deployment

import onnxruntime_genai as og

# Qualcomm Snapdragon devices
config = og.Config(model_path)
config.clear_providers()
config.append_provider("qnn")
model = og.Model(config)

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Configuration in genai_config.json

Providers can be configured directly in your model’s genai_config.json: 
{
  "model": {
    "decoder": {
      "session_options": {
        "provider_options": [
          {
            "cuda": {}
          }
        ]
      }
    }
  }
}
The provider_options array specifies execution providers in priority order. ONNX Runtime will use the first available provider.

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Device Filtering

For multi-device systems, you can filter by hardware type: 
{
  "provider_options": [
    {
      "openvino": {
        "device_type": "GPU"
      },
      "device_filtering_options": {
        "hardware_device_type": "gpu",
        "hardware_device_id": 0
      }
    }
  ]
}
Provider availability depends on your installation. Install provider-specific packages:
  • CUDA: onnxruntime-genai-cuda
  • DirectML: onnxruntime-genai-directml
  • Other providers may require building from source.

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Next Steps

CUDA Setup

Configure NVIDIA GPU acceleration

DirectML Setup

Enable DirectML on Windows

OpenVINO Setup

Optimize for Intel hardware

QNN Setup

Deploy to Qualcomm devices

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