Implementing Local LLM Inference in .NET Applications Using ONNX Runtime

Introduction

Large Language Models (LLMs) have become a core component of modern applications, powering chatbots, code assistants, document analysis tools, and intelligent search systems. While many organizations use cloud-hosted AI services, there is growing interest in running AI models locally.

Local inference offers several advantages, including reduced latency, improved privacy, lower operational costs, and the ability to operate without an internet connection. For .NET developers, ONNX Runtime provides a powerful and efficient way to run machine learning and AI models directly within applications.

By combining ONNX Runtime with locally deployed language models, developers can build AI-powered solutions that maintain full control over data and infrastructure.

In this article, you'll learn how ONNX Runtime works, how to integrate it into .NET applications, and best practices for implementing local LLM inference.

What Is ONNX Runtime?

ONNX Runtime is a high-performance inference engine for machine learning models in the Open Neural Network Exchange (ONNX) format.

It enables developers to:

Run AI models locally
Optimize inference performance
Support multiple hardware platforms
Leverage CPU and GPU acceleration
Deploy AI applications across different environments

Instead of calling an external AI API, applications can execute models directly on local hardware.

Typical workflow:

User Input
      |
      v
.NET Application
      |
      v
ONNX Runtime
      |
      v
Local LLM
      |
      v
Generated Output

This architecture eliminates dependency on external AI services.

Why Run LLMs Locally?

Many organizations are adopting local inference for practical reasons.

Improved Privacy

Sensitive information remains within organizational infrastructure.

Examples include:

Customer data
Financial records
Legal documents
Internal communications

Reduced Latency

Local processing removes network round trips.

Benefits include:

Faster responses
Improved user experience
Reduced dependency on internet connectivity

Cost Control

Cloud AI services often charge based on usage.

Local inference can reduce recurring costs for:

High-volume applications
Internal tools
Knowledge assistants

Offline Operation

Applications continue functioning even when external services are unavailable.

This is valuable for edge computing and disconnected environments.

Understanding ONNX Models

ONNX is a standard format for representing machine learning models.

Many AI frameworks support exporting models to ONNX, including:

PyTorch
TensorFlow
Hugging Face Transformers
Scikit-learn

An exported ONNX model can then be executed using ONNX Runtime.

Example:

Model.onnx

This file contains the trained model used during inference.

Setting Up ONNX Runtime in .NET

Install the ONNX Runtime package:

dotnet add package Microsoft.ML.OnnxRuntime

This package provides the APIs required to load and execute ONNX models.

Loading an ONNX Model

Create an inference session.

using Microsoft.ML.OnnxRuntime;

var session =
    new InferenceSession(
        "Model.onnx");

The session manages model execution and resource allocation.

Typically, applications create the session once and reuse it throughout the application lifecycle.

Creating Input Data

Before running inference, input data must be prepared in a format expected by the model.

Example:

var inputTensor =
    new DenseTensor<float>(
        new[] { 1.0f, 2.0f, 3.0f },
        new[] { 1, 3 });

The exact tensor structure depends on the model architecture.

For language models, tokenized text is typically converted into tensors before inference.

Running Inference

Execute the model using ONNX Runtime.

var inputs =
    new List<NamedOnnxValue>
{
    NamedOnnxValue.CreateFromTensor(
        "input",
        inputTensor)
};

using var results =
    session.Run(inputs);

The output contains model predictions that can be processed by the application.

Building a Local AI Service

Encapsulate inference logic within a dedicated service.

public interface ILocalAiService
{
    Task<string>
        GenerateResponseAsync(
            string prompt);
}

This abstraction makes it easier to swap models or inference engines later.

Implementation example:

public class LocalAiService
    : ILocalAiService
{
    public async Task<string>
        GenerateResponseAsync(
            string prompt)
    {
        // Tokenization

        // ONNX inference

        // Response generation

        return "Generated response";
    }
}

This service becomes the foundation for AI-powered features.

Practical Example: Internal Knowledge Assistant

Imagine an organization that maintains internal documentation.

A user asks:

How do I deploy the billing service?

Workflow:

Question
   |
   v
Tokenization
   |
   v
ONNX Runtime
   |
   v
Local LLM
   |
   v
Answer

Generated response:

The billing service is deployed through
the standard deployment pipeline and
requires database migration validation
before release.

All processing occurs locally without sending data to external providers.

Using Dependency Injection

builder.Services.AddSingleton<
    ILocalAiService,
    LocalAiService>();

This makes the AI service available throughout the application.

Example API endpoint:

app.MapPost("/chat",
    async (
        string prompt,
        ILocalAiService service) =>
{
    return await service
        .GenerateResponseAsync(prompt);
});

This exposes local AI capabilities through a standard API.

Optimizing Inference Performance

Performance is critical when running LLMs locally.

Common optimization strategies include:

Reuse Sessions

Avoid creating inference sessions for every request.

Good:

Singleton InferenceSession

Bad:

New InferenceSession
per request

Session reuse significantly improves performance.

Use Hardware Acceleration

ONNX Runtime supports:

CPU execution
NVIDIA GPUs
DirectML
Other hardware accelerators

Hardware acceleration can dramatically reduce inference time.

Optimize Models

Smaller models often provide better performance for production workloads.

Examples include:

Quantized models
Distilled models
Task-specific models

These models require fewer resources while maintaining acceptable accuracy.

Monitor Resource Consumption

Track:

CPU utilization
GPU utilization
Memory usage
Response times

Monitoring helps identify bottlenecks and optimize deployments.

Security Considerations

Although local inference improves privacy, security remains important.

Protect:

Model files
Prompt data
Application endpoints
Generated outputs

Implement:

Authentication
Authorization
Logging
Rate limiting

These controls help secure AI-powered applications.

Common Challenges

Organizations implementing local LLM inference may encounter:

Large model sizes
Hardware limitations
Memory constraints
Tokenization complexity
Performance tuning requirements

Choosing the appropriate model size and hardware configuration is critical for success.

Best Practices

Start with Smaller Models

Evaluate lightweight models before deploying large LLMs.

Use Service Abstractions

Separate inference logic from application logic.

Benchmark Performance

Measure:

Response latency
Throughput
Resource consumption

Before moving to production.

Implement Caching

Frequently requested responses can be cached to reduce inference workload.

Plan for Model Updates

Establish a strategy for evaluating and deploying new model versions safely.

Conclusion

Local LLM inference enables organizations to build AI-powered applications while maintaining control over data, infrastructure, and operational costs. Using ONNX Runtime, .NET developers can efficiently execute AI models locally and integrate intelligent capabilities into web applications, desktop software, and enterprise systems.

By combining ONNX Runtime with proper architecture, performance optimization, and security practices, developers can create scalable AI solutions that deliver fast responses without relying entirely on cloud-based services. As privacy, cost efficiency, and edge computing continue to gain importance, local AI inference will become an increasingly valuable capability for modern .NET applications.