LLamaSharp: Running LLaMA Models Locally in .NET with llama.cpp Backends

The .NET gap that LLamaSharp fills

llama.cpp is a C++ inference engine. If your application is written in C#, calling into it means P/Invoke bindings, marshalling, and keeping the native library version in step with the managed API. LLamaSharp is that binding layer plus a higher-level API surface. The README describes it as a cross-platform library to run LLaMA models on your local device, based on llama.cpp, with inference efficient on both CPU and GPU.

The audience is narrow and specific: .NET developers who want a language model running inside their own process, on their own hardware, without a Python service sitting next to it. The repository topics include chatbot, semantic-kernel and llava, which signals the intended shape of the work: chat loops, embedding pipelines, and multi-modal input, wired into a .NET codebase rather than exposed over HTTP. If you are not in .NET, the value proposition mostly disappears, because llama.cpp itself already provides the engine.

Managed API over native backends

The architecture is a two-package split, and understanding it explains most deployment problems. The managed package, LLamaSharp, contains the C# API. The native code arrives separately through backend packages compiled from C++. The README states that you do not need to compile any C++ yourself, provided a published backend matches your device.

Those backends are LLamaSharp.Backend.Cpu for pure CPU on Windows, Linux and Mac (with Metal GPU support on Mac), LLamaSharp.Backend.Cuda11 and LLamaSharp.Backend.Cuda12 for Windows and Linux, and LLamaSharp.Backend.Vulkan for Windows and Linux. The repository also contains a llama.cpp submodule, so the native source is vendored rather than fetched from an arbitrary build.

Models are not part of the library. LLamaSharp reads GGUF files, which the README describes as convertible from PyTorch (.pth) or Hugging Face (.bin) formats. The practical consequence: the library version and the model file version are coupled. The README warns explicitly to note the publishing time of a GGUF file on Hugging Face, because some older ones may only work with older versions of LLamaSharp. That is a real compatibility surface, and it is the one most likely to bite on a first attempt.

Installing LLamaSharp and running a first chat

Installation goes through NuGet. The README gives the Package Manager console form for the managed package. Run this in Visual Studio's Package Manager console, and you should see LLamaSharp appear in the project's package references.

PM> Install-Package LLamaSharp

Then add exactly one backend that matches your hardware. On a machine without a supported GPU, the CPU package is the safe choice, and on macOS it also brings Metal support according to the README.

PM> Install-Package LLamaSharp.Backend.Cpu

For an NVIDIA card, pick the CUDA package whose major version matches your driver. The README lists CUDA 11 and CUDA 12 variants for Windows and Linux, and mixing them up produces native load failures rather than a managed exception with a helpful message.

PM> Install-Package LLamaSharp.Backend.Cuda12

Model preparation is a separate step. The README offers two routes: search for the model name plus 'gguf' on Hugging Face, or convert a PyTorch or Hugging Face checkpoint yourself using the conversion scripts described in the llama.cpp readme. The README recommends downloading quantized models rather than fp16, because quantization significantly reduces required memory while only slightly affecting generation quality.

The chat example in the README begins with a using block and a model path variable that you are told to replace. That is where a first program starts:

using LLama;
using LLama.Common;
using LLama.Sampling;

string modelPath = @"<Your Model Path>"; // change it

The README truncates the example at that point, so the full chat loop is not reproduced here. The complete version lives in the LLama.Examples project in the repository and in the quick start page on the project site. Treat those two as the authoritative source for the remaining calls, and check the LLamaSharp version they target against the one you installed.

Where LLamaSharp is the wrong tool

The library does not download models, does not manage a model cache, and does not expose an HTTP endpoint. The repository contains LLama.Web and LLama.WebAPI projects, and the README links an ASP.NET demo, but those are examples of what you can build, not a supported server product.

The second limitation is hardware coverage. The README says plainly that if no published backend matches your device, you should open an issue, or compile a backend yourself following the contributing guide. That is a real cliff: an unusual accelerator or an operating system outside the Windows, Linux and Mac set leaves you maintaining a C++ toolchain.

The third is version drift. Because LLamaSharp tracks llama.cpp and the vendored submodule moves with it, a GGUF file that worked last year may not load today. There is a version map in the README precisely because this alignment matters. If your deployment pins an old LLamaSharp, you are also pinning the era of model files you can use.

LLamaSharp compared with Ollama

Ollama is the comparison people reach for, and the difference is architectural rather than cosmetic. Ollama runs as a separate service that manages model downloads and exposes an API; your application talks to it over a local socket. LLamaSharp is a library you link into your process, with no daemon and no model registry.

That means LLamaSharp gives you in-process control: you own the model path, the sampling parameters, the lifetime of the context, and the memory footprint. It also means you own all the operational work that Ollama does for you, including fetching the model file and keeping it compatible. If your application is a .NET desktop tool or a game, in-process is the better fit. If you just want a local model answering HTTP requests, the daemon approach removes an entire category of work.

Within .NET itself, the README lists integrations with BotSharp, LangChain and MaIN.NET, plus a Semantic Kernel topic on the repository. Those sit on top of LLamaSharp rather than replacing it, and they are maintained in their own repositories, so their release cadence is not LLamaSharp's.

Licence, releases and the cost of upgrading

LLamaSharp is MIT licensed, which permits commercial use and modification with the usual requirement to retain the copyright notice. The native backends are compiled from llama.cpp, so their licensing is a separate question you should confirm for your own distribution model rather than assume from the MIT label on the managed package.

Release cadence is visible in the repository: v0.26.0 in February 2026, v0.27.0 in April 2026, and v0.29.0 in August 2026, with the last push to the default branch on 2026-09-01. That is a fast-moving project, and the version map in the README exists because LLamaSharp and llama.cpp versions are tied together.

The upgrade cost is therefore not just a package bump. A new LLamaSharp version can change which GGUF files load, and it can change the managed API. Budget for re-testing your model file and your chat loop on each upgrade, and read the version map before you move.

Editorial conclusion

Adopt LLamaSharp when you are already writing C# and want local inference inside an existing .NET process, with a backend package matching your GPU. Do not adopt it if you want a ready-made chat server or a model downloader; the library gives you neither, and you must supply a GGUF file yourself. Before committing, verify that a published backend matches your hardware, that your chosen GGUF file was converted recently enough for your LLamaSharp version, and how much memory your quantized model needs on your target machine.

Frequently asked questions

Official sources

1. License: MIT
2. Project website
3. README
4. Releases
5. SciSharp/LLamaSharp on GitHub