Lokale LLMs
llama.cpp
LLM inference in C/C++
★ 130,2 Tsd. SterneMIT
Anleitung ansehen →Web LLM ist ein Open-Source-Projekt aus der Kategorie Lokale LLMs von mlc-ai (mlc-ai/web-llm auf GitHub). Es hat 19.219 Sterne auf GitHub und 1.388 Forks, wird unter der Lizenz Apache-2.0 veröffentlicht und wurde zuletzt am 3. Oktober 2026 auf GitHub aktualisiert.
High-performance In-browser LLM Inference Engine
One-command installers exist for every major OS; servers and engines use Docker or simple binaries.
Pull from a built-in library or point at any GGUF/compatible weights you already have.
Use the bundled chat UI, or point your apps and coding agents at the local OpenAI-style endpoint.
Pick model sizes that fit your RAM/VRAM; quantization options trade a little quality for a lot of speed.
Prompts never leave your machine — no accounts, no logging, no training on your data.
Once weights are downloaded, everything runs with the network cable unplugged.
Local endpoints your existing tools and code can call with minimal changes.
Llama, Qwen, Gemma, DeepSeek, Mistral and more, in sizes from phone-friendly to workstation-class.
Coding assistants and automation tools plug straight into the local server.
Run bigger models on smaller hardware via GGUF and friends.
Download the source and move into the project folder.
git clone https://github.com/mlc-ai/web-llm.git
cd web-llmUse the package manager the project specifies (check for pnpm or yarn lockfiles).
npm installLaunch the dev server or build, per the README.
npm run devRoughly the model size in GB plus headroom — 8B models fit comfortably on 16GB machines with quantization.
Local wins on privacy and cost; cloud wins on raw frontier quality. Many people use both.
It is an open-source project at mlc-ai/web-llm with 1,388 forks. Check the repo's contributors and recent commits to see how active maintenance is.
A recent mid-size open model in the library's recommendations — start small and scale up.
Yes — the local API plus client libraries make it a popular backend for indie apps.
Yes — several of these are optimized for it, and unified memory helps a lot.
LLM inference in C/C++
A high-throughput and memory-efficient inference and serving engine for LLMs
Open-source desktop app for local LLMs. Text, vision, tool-calling, OpenAI/Anthropic-compatible API. 100% private
TokenSpeed — a speed-of-light LLM inference engine designed for agentic workloads, with TensorRT-LLM-level performance and vLLM-level usability
Run a 70B LLM on a single 4GB GPU — layer-by-layer inference with almost no memory tricks skipped
Compile and run LLMs anywhere - phones, laptops, browsers and GPUs - with one engine
Die anderen Projekte der Kategorie Lokale LLMs mit den meisten Sternen im LibreAI-Katalog — die realistische Auswahl, falls Web LLM nicht das Richtige ist.
The simplest way to run open language models on your own machine.
User-friendly AI Interface (Supports Ollama, OpenAI API, ...)
LLM inference in C/C++
A high-throughput and memory-efficient inference and serving engine for LLMs
🤯 LobeHub is your Chief Agent Operator, organizing your agents into 7×24 operations by hiring, scheduling, and reporting on your entire AI team.
Verwandte Projekte der Kategorie Lokale LLMs im Katalog, die Web LLM bei den Sternen am nächsten stehen — zum Weiterstöbern auf beiden Seiten.
A fast LLM inference server for Apple Silicon, managed from the macOS menu bar
Qwen3-Coder - Alibaba's open code model series for agentic coding, up to 1M tokens of context
🔥 MaxKB is an open-source platform for building enterprise-grade agents. 强大易用的开源企业级智能体平台。
Alibaba's lightweight, blazing-fast inference engine for on-device LLMs and edge AI.
Compile and run LLMs anywhere - phones, laptops, browsers and GPUs - with one engine
A lightweight vLLM implementation built from scratch in about 1,200 readable lines of Python.
Kostenlos und offen — hol dir den Quellcode, gib dem Repository einen Stern und leg los.