TensorSharp

TensorSharp is a native .NET inference engine for running large language models locally, on hardware you already own. It loads models in the GGUF format and gives you three ways to work with them, a command-line tool, a browser-based chat server, and a set of HTTP APIs. Everything runs on your own machine, so prompts, documents, and images never leave it, and there are no per-token fees to keep an eye on.

The problem it goes after is the cost and privacy tax that comes with cloud inference. Most teams that want a language model reach for a hosted API, then pay for every token and hand their data to someone else's servers. Running models locally answers both of those concerns, but the tooling has historically lived in C++ or Python. TensorSharp is written natively in C# on .NET 10, which means a .NET shop can run and embed local inference without stepping outside the stack it already knows.

In day-to-day use you pick whichever interface suits the task. The CLI handles one-shot generation along with image prompts, audio prompts, batch jobs, and benchmarks, so it's a natural fit for scripting, automation, and quick tests from the shell. The server stands up a local chatbot in the browser for people who'd rather just open a page and talk to a model. The HTTP layer exposes REST endpoints you can call from curl, Python, or any compatible client, which is the path most applications will take. And because the engine ships as a C# library, you can embed it straight inside a .NET application instead of shelling out to a separate process, which keeps everything in one runtime.

One of the more practical touches is that the APIs are Ollama- and OpenAI-compatible. Tools and SDKs written for either of those services work against TensorSharp without changes, so you can point an existing integration at a local backend and keep your code as it is. That matters because it turns a migration into a config change rather than a rewrite. An app that already talks to a paid cloud endpoint can be redirected at a local instance and behave the same way, which makes trying local inference a low-risk experiment instead of a rebuild.

The engine also reaches well past plain text. It handles vision, audio, reasoning, and tool use in the same place, so one setup can describe an image, work with audio, run step-by-step reasoning, or call out to tools. Running all of that locally has a knock-on benefit that's easy to underrate. Because prompts, documents, and images never leave the machine, sensitive material stays put, which is the kind of thing that matters for regulated data, private work, or simply keeping proprietary content off third-party servers. It also means inference keeps working offline, since there's no external service in the loop.

Performance is where the project plants its flag. On identical GGUF files it reports a 26B-A4B mixture-of-experts model prefilling about 1.32 times faster than llama.cpp, with first tokens arriving roughly 1.30 times sooner, a 12B model that wins or ties every decode scenario it was tested in, and JSON-mode decoding that streams several times faster on one of its models. Those figures are the maker's own, so they're worth checking against your own workload, but they show speed is a real focus. Hardware coverage is broad, with GPU acceleration on NVIDIA through CUDA, on AMD, Intel, or NVIDIA through Vulkan, and on Apple Silicon through Metal and MLX, plus a pure CPU fallback when there's no GPU to lean on.

The audience is deliberately wide, spanning beginners who want a private chatbot on their own laptop through developers, engineers, and researchers who need scriptable local inference they can wire into other software. What separates it from the usual options is that it's native .NET rather than a wrapper around a C++ library, so it feels at home for anyone already building on the Microsoft stack. If you've wanted local models but didn't want to adopt a Python or C++ toolchain to get them, that's the gap it fills, and it does so with the compatibility layer that lets it slot into tooling built for the bigger names.

TensorSharp is open source, with a public GitHub repository, and it's free to run because the compute is yours. There's no subscription and nothing metered, so the only real cost is the hardware you point it at and the time to set it up. For a team weighing a hosted API against keeping inference in-house, it's a way to do the latter while staying inside a familiar .NET world, with the CLI, server, and API all available out of the same project.