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TensorSharp runs GGUF language models locally in .NET, with no per-token fees to worry about.
Grounded in available product and source data
A native .NET local LLM inference engine is exactly what TensorSharp is, built specifically for developers who want GGUF models running on their own hardware rather than paying per-token through a cloud API — the site states plainly that prompts, documents, and images never leave the machine, since all processing happens locally.
Deployment isn't limited to one shape either: a command-line tool handles one-shot generation, a browser-based chat server offers a Web UI for interactive use, Ollama- and OpenAI-compatible APIs let existing tooling point at it with minimal changes, and an embeddable C# library drops inference directly into a .NET application. GPU acceleration covers the major hardware ecosystems — NVIDIA CUDA, AMD/Intel Vulkan, and Apple Metal/MLX — with native CPU execution as a fallback when no GPU is available.
The feature set goes beyond basic text generation: multimodal input spans text, image, video, and audio, alongside model thinking and tool-calling support, tensor parallelism across multiple GPUs for larger models, and cross-machine distribution over a TCP mesh for teams that want to pool hardware rather than running everything on one box.
Performance claims are specific rather than vague: 1.28× faster prefill on CUDA compared to llama.cpp, and 1.21× faster decode on Vulkan, both measured specifically for Gemma 4 12B — worth treating as a benchmark for that model and hardware combination rather than a universal speedup across every model TensorSharp supports. A recommended starting point is Gemma 4 E4B at roughly 7.48 GiB, alongside support for DeepSeek V4 Flash, Qwen, GPT-OSS, and Mistral. Running it requires the .NET 10 SDK across supported operating systems, and there's no pricing tier to evaluate since the entire pitch is avoiding per-token cloud fees in favor of predictable, hardware-based cost instead.
It works either way — GPU acceleration is available across NVIDIA CUDA, AMD/Intel Vulkan, and Apple Metal/MLX, but native CPU execution serves as a fallback when no compatible GPU is present.
Yes — an embeddable C# library is one of the four deployment options, alongside a CLI tool, a browser-based chat server, and Ollama/OpenAI-compatible APIs for other integration styles.
The published numbers — 1.28× faster CUDA prefill and 1.21× faster Vulkan decode versus llama.cpp — are specifically measured for Gemma 4 12B, so results for a different model or hardware setup aren't directly represented by that figure.
The local-processing model is the specific reason it might be — nothing about the inference architecture requires reaching an external server, which removes one common data-exposure concern by design.
Video is one of the explicitly supported input types, alongside text, image, and audio, so it's not limited to a text-only chat use case.
