Running Local AI with LlamaSharp in .NET: A Developer's Guide

Why Local AI Matters

If you've been building AI-powered applications, you've probably relied on OpenAI, Azure OpenAI, or other cloud APIs. They work well, but there's a catch - every API call costs money, your data leaves your machine, and during peak hours, response times can slow down.

What if you could run the same powerful AI models directly on your own hardware? No internet required, no per-token billing, complete data privacy. That's exactly what LlamaSharp enables.

What is LlamaSharp?

LlamaSharp is a C#/.NET library that wraps llama.cpp - the popular C++ library for running LLaMA-family models. It gives .NET developers a clean API to run large language models locally on either CPU or GPU.

Think of it as bringing the power of local AI into your .NET applications without learning a new language or framework.

Key Features

Model inference - Run LLaMA, Llama 2, Llama 3, Phi, Mistral, and more
Quantization support - Smaller model files, faster inference
Embeddings - For RAG and semantic search
Multiple executors - Interactive, instruct, or stateless modes
Chat sessions - Maintain conversation history
GPU acceleration - CUDA 11/12 or Vulkan support

Getting Started

Install the NuGet packages:

dotnet add package LLamaSharp
dotnet add package LLamaSharp.Backend.Cpu

A simple console app to chat with a model:

using LLama;
using LLama.Common;

var modelPath = "path/to/your/model.gguf";
var parameters = new ModelParams(modelPath)
{
    ContextSize = 512,
    GpuLayerCount = 0 // Use CPU only
};

using var model = LLamaWeights.LoadFromFile(parameters);
using var executor = new InteractiveExecutor(model);

var session = new ChatSession(executor);
session.History.AddMessage(AuthorRole.System, "You are a helpful assistant.");

Console.WriteLine("Chat started. Type 'exit' to quit.\n");

while (true)
{
    Console.Write("You: ");
    var input = Console.ReadLine();
    if (input?.ToLower() == "exit") break;
    
    await foreach (var token in session.ChatAsync(input, new InferenceParams { Temperature = 0.6f }))
    {
        Console.Write(token);
    }
    Console.WriteLine();
}

Picking the Right Model

Models come in different formats and sizes. Here's what you need to know:

Format	Extension	Notes
GGUF	.gguf	Current standard format, works out of the box
GGML	.ggml	Older format, needs conversion

Quantization levels (bits per parameter):

Q4_K_S - Good balance of size and quality
Q5_K_M - Better quality, larger file
Q8_0 - closest to full precision

Popular models to try:

Phi-4 - Microsoft's latest, great for reasoning
Llama 3 - Meta's flagship model
Mistral - Efficient and capable
Gemma 3 - Google's lightweight option
GPT OSS - OpenAI Open Source Models

Grab quantized models from Hugging Face - they're ready to run.

CPU vs GPU

Running on CPU works fine for smaller models (up to 7B parameters) on modern hardware. For larger models or faster inference, add a GPU backend:

dotnet add package LLamaSharp.Backend.Cuda12

Set GpuLayerCount in your ModelParams to offload layers to the GPU.

Integration Options

LlamaSharp plays nicely with other AI libraries:

Semantic Kernel: Use LlamaSharp as a chat completion service
Kernel Memory: Build RAG applications
BotSharp: Bot development platform

This means if you're already using Microsoft's Semantic Kernel, swapping in a local model is just a few lines of code.

Performance Tips

A few things that help:

Use quantized models - Q4_K_S or Q5_K_M offers the best trade-off
Adjust context size - Smaller context = less memory and faster inference
Batch inference - If generating multiple responses, do it in one call
Enable GPU - For models 7B and above, GPU makes a big difference

When to Use Local AI

Local AI shines in these scenarios:

Development and testing - No API costs while you're building
Offline applications - IoT, edge devices, air-gapped environments
Privacy sensitive data - Your data never leaves the machine
High volume inference - When you need to process thousands of queries
Custom fine-tuned models - Run your own fine-tuned models

Wrapping Up

LlamaSharp makes local AI accessible to .NET developers. No Python required, no complex setups - just reference the NuGet package and start building.

The ecosystem is mature now. With version 0.26.0 out recently, support for the latest models like Gemma 3, and integrations with Semantic Kernel, there's never been a better time to try running AI locally.

Give it a shot with a small quantized model first. You might be surprised at what runs on your own machine.

Links:

LlamaSharp Repo: https://github.com/SciSharp/LLamaSharp

Example Repo: https://github.com/avikeid2007/KaiROS-AI

GGUF Model Library: https://huggingface.co/models?library=gguf&sort=trending