TensorSharp: Open-Source Local LLM Inference Engine
TensorSharp is an open-source C# inference engine designed for running large language models locally. It supports various model architectures and provides multiple interfaces for programmatic access. The engine features optimized backends for CPU and GPU, enabling efficient multimodal inference.
- ▪TensorSharp allows users to run large language models locally using GGUF model files.
- ▪It offers a console application, a web-based chatbot interface, and APIs compatible with Ollama and OpenAI.
- ▪The engine supports multiple model architectures and provides optimized backends for both CPU and GPU.
Hacker News (AI / LLM) files mainly under ai. We currently carry 3,301 of its stories.
Story provenance
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Story provenance
Attribution is not the same as permission. This drawer separates discovery metadata, excerpts, WeSearch-generated summaries, reuse status, and whether the publisher receives the visit. Nothing here claims a legal grant the publisher has not made.
Record
| Original publisher | GitHub |
| Canonical URL | https://github.com/zhongkaifu/TensorSharp |
| Publication time | Thu, 04 Jun 2026 00:29:10 +0000 |
| Retrieval time | 2026-06-04T01:25:03.252Z |
| Last seen | 2026-06-04T01:25:03.252Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | 5x3QtYjaTq0f |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
Rights status (four layers)
WeSearch handling by dimension
| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
TensorSharp English | 中文 A C# inference engine for running large language models (LLMs) locally using GGUF model files. TensorSharp provides a console application, a web-based chatbot interface, and Ollama/OpenAI-compatible HTTP APIs for programmatic access. Documentation Map Start here Use this when you want to... Quick build and usage Build the solution, compile the native GGML bridge, and run the CLI or server Supported model architectures Check which GGUF architecture keys, modalities, thinking mode, and tool calling paths are implemented Compute backends Choose between pure C# CPU, direct CUDA/cuBLAS, MLX Metal, GGML CPU, GGML Metal, and GGML CUDA HTTP APIs Use the Ollama-compatible, OpenAI-compatible, or Web UI SSE endpoints Per-model architecture cards Read end-to-end documentation…
Excerpt limited to ~120 words for fair-use compliance. The full article is at GitHub.