Deploy Phi-4-mini model with ONNX Runtime on Azure Cobalt 100
Introduction
Demo
Build ONNX Runtime and set up the Phi-4-mini Model
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Overview

In this Learning Path, you’ll run quantized Phi models with ONNX Runtime on Microsoft Azure Cobalt 100 servers.

Specifically, you’ll deploy the Phi-4-mini model on Arm-based servers running Ubuntu 24.04 LTS.

Note

These instructions have been tested on a 32-core Azure Dpls_v6 instance with 32 cores, 64GB of RAM, and 32GB of disk space.

You will learn how to build and configure ONNX Runtime to enable efficient LLM inference on Arm CPUs.

This Learning Path walks you through the following tasks:

  • Build ONNX Runtime.
  • Quantize and convert the Phi-4-mini model to ONNX format.
  • Run the model using a Python script with ONNX Runtime for CPU-based LLM inference.
  • Analyze performance on Arm CPUs.

Install dependencies

On your Arm-based server, install the following packages:

    

        
        
    sudo apt update
    sudo apt install python3-pip python3-venv cmake -y

Create a requirements file

Use a file editor of your choice and create a requirements.txt file with the Python packages shown below:

    

        
        
    requests
    torch
    transformers
    accelerate
    huggingface-hub
    pandas
    numpy
    psutil
    packaging
    setuptools
    requests

Install Python dependencies

Create a virtual environment:

    

        
        
    python3 -m venv onnx-env

Activate the virtual environment:

    

        
        
    source onnx-env/bin/activate

Install the required libraries using pip:

    

        
        
    pip install -r requirements.txt

Clone and build ONNX Runtime

Clone, build, and install the onnxruntime repository using the following commands:

    

        
        
git clone --recursive https://github.com/microsoft/onnxruntime.git
cd onnxruntime
./build.sh --config Release --build_shared_lib --parallel --build_wheel --skip_tests --update --build
cd build/Linux/Release
cmake --install . --prefix install
cd dist
pip install onnxruntime-*.whl

This process builds and installs ONNX Runtime with optimizations for efficient inference on Arm CPUs.

Clone and build ONNX Runtime genai

Clone and build the onnxruntime-genai repository:

    

        
        
cd ~
cp ./onnxruntime/build/Linux/Release/install/include/onnxruntime/onnxruntime_float16.h ./onnxruntime/build/Linux/Release/install/include/onnxruntime_float16.h
cp ./onnxruntime/build/Linux/Release/install/include/onnxruntime/onnxruntime_c_api.h ./onnxruntime/build/Linux/Release/install/include/onnxruntime_c_api.h
cp ./onnxruntime/build/Linux/Release/install/include/onnxruntime/onnxruntime_ep_c_api.h ./onnxruntime/build/Linux/Release/install/include/onnxruntime_ep_c_api.h
git clone https://github.com/microsoft/onnxruntime-genai.git
cd onnxruntime-genai
python3 build.py --config Release --update --ort_home ../onnxruntime/build/Linux/Release/install
python3 build.py --config Release --build --skip_tests --ort_home ../onnxruntime/build/Linux/Release/install
cd build/Linux/Release/wheel
pip install onnxruntime_genai-*.whl
Note

Ensure you’re using Python 3.12 to match the cp312 wheel format.

This build includes optimizations from KleidiAI for efficient inference on Arm CPUs.

Download and quantize the model

Navigate to your home directory. Now download the quantized model using huggingface-cli:

    

        
        
    cd ~
    huggingface-cli download microsoft/Phi-4-mini-instruct-onnx --include cpu_and_mobile/cpu-int4-rtn-block-32-acc-level-4/* --local-dir .

The Phi-4-mini model is now downloaded in ONNX format with INT4 quantization and is ready to run with ONNX Runtime.

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