Llamasharp
A C#/.NET library to run LLM (🦙LLaMA/LLaVA) on your local device efficiently.
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What is Llamasharp?
LLamaSharp is a C#/.NET library that provides bindings for llama.cpp, enabling developers to run LLaMA, LLaVA, and other compatible LLMs directly on their local machines. It is designed to bring efficient, cross‑platform LLM inference into .NET applications, allowing both CPU and GPU execution while integrating cleanly with existing C# projects. The library exposes model inference, quantization, embeddings generation, and interactive chat‑style execution patterns so that developers can build offline‑first, privacy‑aware AI features without relying on cloud‑hosted LLM APIs.
Key capabilities include multiple executor modes (interactive, instruct, stateless), built‑in chat‑session APIs to manage conversation history, and support for saving and loading model states so that applications can maintain context across sessions. For model ecosystem, LLamaSharp uses GGUF‑formatted weights that can be downloaded from other sources such as Hugging Face, and it supports a wide range of model families including LLaMA, Alpaca, Chinese‑LLaMA‑Alpaca, Vigogne, Vicuna, and others. The library also offers RAG‑ready patterns and higher‑level APIs that make it easier to plug local LLMs into frameworks like semantic‑kernel, BotSharp, Unity, WPF, and web apps.
LLamaSharp targets .NET developers who want to run open‑source LLMs locally inside desktop, console, or web applications, especially those who care about data privacy, latency, and avoiding recurring cloud‑API costs. It suits scenarios such as on‑device chatbots, code‑assistance tools, document Q&A, and agent‑style workflows where the model stays on the user’s hardware. While the library is not a full‑stack product with a UI, it is positioned as a lightweight, embeddable engine that lets C# teams build their own AI experiences around pre‑trained, open‑source models without leaving the .NET ecosystem.
The tool is oriented toward developers comfortable with C# and Native/interop concepts, as it relies on a native backend (CPU, CUDA‑11, CUDA‑12, etc.) and requires careful matching between backend version, llama.cpp commit, and LLamaSharp version. It is also suitable for teams experimenting with model quantization, context‑length tuning, and anti‑prompt configuration to balance performance and correctness. Because it depends on external model weights, LLamaSharp functions more as a runtime glue layer than as an end‑to‑end model‑serving platform, but it excels at giving .NET shops a clean abstraction over llama.cpp so they can integrate local LLMs into their existing codebases.
Overall, LLamaSharp trades some initial setup complexity for flexibility, local execution, and deep integration with .NET tooling. It is well‑suited for individual developers, small teams, or enterprise projects that want to prototype or deploy private‑deployment LLM applications using familiar C# patterns, while still benefiting from the performance and model variety of the llama.cpp ecosystem.
Llamasharp pricing
Pricing model: Freemium
LLamaSharp itself is an open‑source library with no paid tiers or subscription pricing; it is free to use, modify, and distribute under its chosen license. There is no separate billing for inference, tokens, or concurrent users when running models locally. However, users must obtain model weights from external providers such as Hugging Face, where some models may be free and others may be subject to the model owner’s licensing or usage terms. Any associated costs for running the tool arise from local hardware (electricity, GPU/SSD usage) and time spent on setup and maintenance rather than from LLamaSharp’s own pricing structure.
Llamasharp pros
- Runs LLaMA/LLaVA and other LLMs directly on local hardware
- Built on llama.cpp for efficient CPU and GPU inference
- Cross‑platform support for Windows, Linux, and macOS
- Native C#/.NET API for .NET developers
- Supports multiple backends such as CPU, CUDA‑11, and CUDA‑12
- Provides model inference, quantization, and embeddings generation
- Includes interactive, instruct, and stateless executor modes
- Offers chat‑session APIs to manage conversation history
- Allows saving and loading model state for persistent sessions
- Integrates easily with semantic‑kernel, BotSharp, Unity, WPF, and Web
- Supports a wide range of GGUF‑format models from Hugging Face
- Enables RAG‑style patterns and higher‑level abstractions
- Fine‑tuning is planned for future versions
- Open‑source project under the SciSharp ecosystem
- No mandatory cloud dependency or per‑token API pricing
- Good for building privacy‑aware, offline‑first AI apps
Llamasharp cons
- Requires manual download and management of model weights from external sources
- Model must be in GGUF format, which may require conversion from other formats
- GPU setup can be complex and version‑sensitive between backend and llama.cpp
- Slow inference with large models (e.g., over 30B parameters) unless hardware is strong
- Some backends may not be compatible with the latest LLamaSharp releases
- Limited or spotty documentation on certain advanced features
- Web‑based demo projects may be unstable or unmaintained
- Debugging model‑ and backend‑related issues can require low‑level knowledge
Frequently asked questions about Llamasharp
Why does my GPU not get used even though I have installed CUDA?
LLamaSharp may still fall back to CPU if the CUDA backend package does not match your CUDA version or if multiple backends are installed in older releases. Make sure you install the correct CUDA‑11 or CUDA‑12 backend package for your device and for your LLamaSharp version. You can also enable logging via NativeLibraryConfig.Instance.WithLogs(LLamaLogLevel.Info) to see which native library is loaded and confirm that the CUDA library is being picked. If the CUDA library is loaded but performance is still low, check that GpuLayerCount is set to a non‑zero value when loading the model so that layers are actually offloaded to the GPU.
Why is inference with my model so slow?
Large LLMs inherently require more time to generate tokens, especially models above 30B parameters, so some slowness is expected even with LLamaSharp. To determine if this is a framework problem, try running the same model and configuration with the official llama.cpp examples and compare their speed. If llama.cpp is significantly faster, it may indicate a bug in LLamaSharp. You can also optimize by setting GpuLayerCount to the maximum supported value on your GPU, using a quantized model (such as Q4_K_S or Q5_K_M), and reducing context size or batch sizes if you are processing many concurrent requests.
Why does my program crash before any output appears?
Crashes before output are often caused by mismatches between the native backend library and the LLamaSharp version, or by an incompatible model file. If you compiled the native library yourself, verify that you checked out the llama.cpp commit corresponding to your LLamaSharp release, which is listed in the README. If you are using a GGUF file from Hugging Face, check its publishing date to ensure it was converted for a compatible llama.cpp/LLamaSharp version. Updating to matching versions of both the library and the backend, or using a pre‑converted GGUF that explicitly lists compatibility, usually resolves this issue.
How can I run LLMs for non‑English languages with LLamaSharp?
For non‑English outputs the key is to choose a model that was trained or fine‑tuned on that language. For example, Chinese‑LLaMA‑Alpaca will perform far better on Chinese text than the base LLaMA model, which used little Chinese data during pretraining. LLamaSharp itself does not change the language capability of the model; it only runs whatever weights you provide. You should therefore select a GGUF model specifically tuned for your target language and verify that its training data covers that language adequately to get good quality generations in that language.
How long should my prompt be when using LLamaSharp?
The total length of your prompt (including expected response tokens) must fit within the model’s context size limit, otherwise inference may fail or behave unexpectedly. LLamaSharp passes context bounds to the underlying llama.cpp engine, so you must ensure that the combined length of your prompt and the anticipated response does not exceed the model’s maximum context. If you need to handle very long documents, consider splitting the text, using summarization, or choosing a model variant with a larger context window and adjusting the prompt length accordingly.
Which models are compatible with LLamaSharp?
LLamaSharp supports models that are compatible with llama.cpp and converted to GGUF format. Commonly used families include LLaMA, Alpaca, GPT4All, Chinese‑LLaMA‑Alpaca, Vigogne, Vicuna, Koala, OpenBuddy (multilingual), Pygmalion/Metharme, WizardLM, and others, as long as the weight file is in GGUF and is produced for a compatible llama.cpp version. You should download GGUF models from Hugging Face or convert existing PyTorch/Hugging Face weights according to llama.cpp instructions, paying attention to the conversion date and version constraints mentioned in the LLamaSharp documentation.
How do I choose the right model weights for my task?
Different model fine‑tunes exhibit significantly different behaviors even on the same architecture. For example, a model specifically tuned for Chinese outputs will handle Chinese‑language tasks far better than a generic LLaMA base model. When building a chatbot in a non‑English language, choose a model that was fine‑tuned on that language; for coding or technical tasks, pick a model with a strong coding‑focused tuning. LLamaSharp allows you to switch weights easily, so you can test multiple GGUF variants on your specific use case and select the one that balances accuracy, speed, and memory usage for your target hardware.
Can I run LLamaSharp in a web application or on a server?
Yes, LLamaSharp can be embedded into web applications or server‑side .NET services, but you must manage model loading, memory, and concurrency carefully. Because each model can consume several gigabytes of RAM or VRAM, running multiple concurrent sessions or large models on a single server may require load balancing or careful resource allocation. You can also integrate LLamaSharp with higher‑level frameworks such as semantic‑kernel or custom web backends to expose local‑model inference via HTTP APIs, provided the server hardware can sustain the required memory and compute for your expected throughput.
Is there a web UI or demo for LLamaSharp that I can try out?
LLamaSharp provides example and demo projects that aim to show a web‑based chat interface, but the web component can be fragile or out of date in some releases. Community reports indicate that the current state of the web demo may prevent it from working reliably in all environments, and some users have to manually adjust or rebuild the web project. For stable experimentation, it is usually recommended to start with the console or library examples and then adapt the code into your own web or desktop UI rather than relying on the bundled web demo as a production‑ready product.