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:
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:
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
Register the service in ASP.NET Core.
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:
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:
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.